import{S as vf,i as kf,s as Tf,e as n,k as c,w as y,t as r,M as yf,c as s,d as t,m as p,a,x as b,h as i,b as d,N as _f,G as e,g as f,y as M,q as w,o as x,B as $,v as bf,L as Yt}from"../../chunks/vendor-hf-doc-builder.js";import{T as Wo}from"../../chunks/Tip-hf-doc-builder.js";import{D as Z}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Fe}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ne}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Jt}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Mf(z){let h,k,_,g,v;return g=new Fe({props:{code:`from transformers import MarianModel, MarianConfig

# Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration
configuration = MarianConfig()

# Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration
model = MarianModel(configuration)

# Accessing the model configuration
configuration = model.config`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> MarianModel, MarianConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = MarianConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MarianModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){h=n("p"),k=r("Examples:"),_=c(),y(g.$$.fragment)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Examples:"),u.forEach(t),_=p(l),b(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,k),f(l,_,u),M(g,l,u),v=!0},p:Yt,i(l){v||(w(g.$$.fragment,l),v=!0)},o(l){x(g.$$.fragment,l),v=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function wf(z){let h,k,_,g,v;return g=new Fe({props:{code:`from transformers import MarianTokenizer

tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
src_texts = ["I am a small frog.", "Tom asked his teacher for advice."]
tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."]  # optional
inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True)

outputs = model(**inputs)  # should work`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> MarianTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = MarianTokenizer.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-de&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>src_texts = [<span class="hljs-string">&quot;I am a small frog.&quot;</span>, <span class="hljs-string">&quot;Tom asked his teacher for advice.&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>tgt_texts = [<span class="hljs-string">&quot;Ich bin ein kleiner Frosch.&quot;</span>, <span class="hljs-string">&quot;Tom bat seinen Lehrer um Rat.&quot;</span>]  <span class="hljs-comment"># optional</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># keys  [input_ids, attention_mask, labels].</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)  <span class="hljs-comment"># should work</span>`}}),{c(){h=n("p"),k=r("Examples:"),_=c(),y(g.$$.fragment)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Examples:"),u.forEach(t),_=p(l),b(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,k),f(l,_,u),M(g,l,u),v=!0},p:Yt,i(l){v||(w(g.$$.fragment,l),v=!0)},o(l){x(g.$$.fragment,l),v=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function xf(z){let h,k,_,g,v;return{c(){h=n("p"),k=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(u,"CODE",{});var E=a(_);g=i(E,"Module"),E.forEach(t),v=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,k),e(h,_),e(_,g),e(h,v)},d(l){l&&t(h)}}}function $f(z){let h,k,_,g,v;return g=new Fe({props:{code:`from transformers import AutoTokenizer, MarianModel

tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
model = MarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")

inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
decoder_inputs = tokenizer(
    "<pad> Studien haben gezeigt dass es hilfreich ist einen Hund zu besitzen",
    return_tensors="pt",
    add_special_tokens=False,
)
outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)

last_hidden_states = outputs.last_hidden_state
list(last_hidden_states.shape)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, MarianModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-de&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MarianModel.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-de&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Studies have been shown that owning a dog is good for you&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_inputs = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&lt;pad&gt; Studien haben gezeigt dass es hilfreich ist einen Hund zu besitzen&quot;</span>,
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;pt&quot;</span>,
<span class="hljs-meta">... </span>    add_special_tokens=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">26</span>, <span class="hljs-number">512</span>]`}}),{c(){h=n("p"),k=r("Example:"),_=c(),y(g.$$.fragment)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Example:"),u.forEach(t),_=p(l),b(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,k),f(l,_,u),M(g,l,u),v=!0},p:Yt,i(l){v||(w(g.$$.fragment,l),v=!0)},o(l){x(g.$$.fragment,l),v=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function zf(z){let h,k,_,g,v;return{c(){h=n("p"),k=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(u,"CODE",{});var E=a(_);g=i(E,"Module"),E.forEach(t),v=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,k),e(h,_),e(_,g),e(h,v)},d(l){l&&t(h)}}}function jf(z){let h,k,_,g,v;return g=new Fe({props:{code:`from transformers import AutoTokenizer, MarianMTModel

src = "fr"  # source language
trg = "en"  # target language

model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
model = MarianMTModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

sample_text = "o\xF9 est l'arr\xEAt de bus ?"
batch = tokenizer([sample_text], return_tensors="pt")

generated_ids = model.generate(**batch)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, MarianMTModel

<span class="hljs-meta">&gt;&gt;&gt; </span>src = <span class="hljs-string">&quot;fr&quot;</span>  <span class="hljs-comment"># source language</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>trg = <span class="hljs-string">&quot;en&quot;</span>  <span class="hljs-comment"># target language</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = <span class="hljs-string">f&quot;Helsinki-NLP/opus-mt-<span class="hljs-subst">{src}</span>-<span class="hljs-subst">{trg}</span>&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MarianMTModel.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(model_name)

<span class="hljs-meta">&gt;&gt;&gt; </span>sample_text = <span class="hljs-string">&quot;o\xF9 est l&#x27;arr\xEAt de bus ?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>batch = tokenizer([sample_text], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**batch)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]
<span class="hljs-string">&quot;Where&#x27;s the bus stop?&quot;</span>`}}),{c(){h=n("p"),k=r("Examples:"),_=c(),y(g.$$.fragment)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Examples:"),u.forEach(t),_=p(l),b(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,k),f(l,_,u),M(g,l,u),v=!0},p:Yt,i(l){v||(w(g.$$.fragment,l),v=!0)},o(l){x(g.$$.fragment,l),v=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function Ef(z){let h,k,_,g,v;return g=new Fe({props:{code:`from transformers import AutoTokenizer, MarianForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en", add_cross_attention=False)
assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

logits = outputs.logits
expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
list(logits.shape) == expected_shape`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, MarianForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-fr-en&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MarianForCausalLM.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-fr-en&quot;</span>, add_cross_attention=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">assert</span> model.config.is_decoder, <span class="hljs-string">f&quot;<span class="hljs-subst">{model.__class__}</span> has to be configured as a decoder.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span>expected_shape = [<span class="hljs-number">1</span>, inputs.input_ids.shape[-<span class="hljs-number">1</span>], model.config.vocab_size]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(logits.shape) == expected_shape
<span class="hljs-literal">True</span>`}}),{c(){h=n("p"),k=r("Example:"),_=c(),y(g.$$.fragment)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Example:"),u.forEach(t),_=p(l),b(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,k),f(l,_,u),M(g,l,u),v=!0},p:Yt,i(l){v||(w(g.$$.fragment,l),v=!0)},o(l){x(g.$$.fragment,l),v=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function qf(z){let h,k,_,g,v,l,u,E,Ke,Ne,O,he,X,j,Ge,D,ue,ct,Q,U,_e,se,Je,ae,re,Ye,Ae,H,Ze,Ce,ee,N,L,me,B,Xe,Le,I,te,ve,A,Qe,R,et,Oe,P,ke,Te,W,tt,V,ie,ye,C,ot,S,nt,be;return{c(){h=n("p"),k=r("TensorFlow models and layers in "),_=n("code"),g=r("transformers"),v=r(" accept two formats as input:"),l=c(),u=n("ul"),E=n("li"),Ke=r("having all inputs as keyword arguments (like PyTorch models), or"),Ne=c(),O=n("li"),he=r("having all inputs as a list, tuple or dict in the first positional argument."),X=c(),j=n("p"),Ge=r(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),D=n("code"),ue=r("model.fit()"),ct=r(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),Q=n("code"),U=r("model.fit()"),_e=r(` supports! If, however, you want to use the second
format outside of Keras methods like `),se=n("code"),Je=r("fit()"),ae=r(" and "),re=n("code"),Ye=r("predict()"),Ae=r(`, such as when creating your own layers or models with
the Keras `),H=n("code"),Ze=r("Functional"),Ce=r(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),ee=c(),N=n("ul"),L=n("li"),me=r("a single Tensor with "),B=n("code"),Xe=r("input_ids"),Le=r(" only and nothing else: "),I=n("code"),te=r("model(input_ids)"),ve=c(),A=n("li"),Qe=r(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),R=n("code"),et=r("model([input_ids, attention_mask])"),Oe=r(" or "),P=n("code"),ke=r("model([input_ids, attention_mask, token_type_ids])"),Te=c(),W=n("li"),tt=r(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
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`),S=n("a"),nt=r("subclassing"),be=r(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(T){h=s(T,"P",{});var q=a(h);k=i(q,"TensorFlow models and layers in "),_=s(q,"CODE",{});var Me=a(_);g=i(Me,"transformers"),Me.forEach(t),v=i(q," accept two formats as input:"),q.forEach(t),l=p(T),u=s(T,"UL",{});var we=a(u);E=s(we,"LI",{});var _t=a(E);Ke=i(_t,"having all inputs as keyword arguments (like PyTorch models), or"),_t.forEach(t),Ne=p(we),O=s(we,"LI",{});var xe=a(O);he=i(xe,"having all inputs as a list, tuple or dict in the first positional argument."),xe.forEach(t),we.forEach(t),X=p(T),j=s(T,"P",{});var F=a(j);Ge=i(F,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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pass your inputs and labels in any format that `),Q=s(F,"CODE",{});var kt=a(Q);U=i(kt,"model.fit()"),kt.forEach(t),_e=i(F,` supports! If, however, you want to use the second
format outside of Keras methods like `),se=s(F,"CODE",{});var st=a(se);Je=i(st,"fit()"),st.forEach(t),ae=i(F," and "),re=s(F,"CODE",{});var at=a(re);Ye=i(at,"predict()"),at.forEach(t),Ae=i(F,`, such as when creating your own layers or models with
the Keras `),H=s(F,"CODE",{});var Tt=a(H);Ze=i(Tt,"Functional"),Tt.forEach(t),Ce=i(F,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),F.forEach(t),ee=p(T),N=s(T,"UL",{});var K=a(N);L=s(K,"LI",{});var G=a(L);me=i(G,"a single Tensor with "),B=s(G,"CODE",{});var rt=a(B);Xe=i(rt,"input_ids"),rt.forEach(t),Le=i(G," only and nothing else: "),I=s(G,"CODE",{});var yt=a(I);te=i(yt,"model(input_ids)"),yt.forEach(t),G.forEach(t),ve=p(K),A=s(K,"LI",{});var le=a(A);Qe=i(le,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),S=s(je,"A",{href:!0,rel:!0});var bt=a(S);nt=i(bt,"subclassing"),bt.forEach(t),be=i(je,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),je.forEach(t),this.h()},h(){d(S,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),d(S,"rel","nofollow")},m(T,q){f(T,h,q),e(h,k),e(h,_),e(_,g),e(h,v),f(T,l,q),f(T,u,q),e(u,E),e(E,Ke),e(u,Ne),e(u,O),e(O,he),f(T,X,q),f(T,j,q),e(j,Ge),e(j,D),e(D,ue),e(j,ct),e(j,Q),e(Q,U),e(j,_e),e(j,se),e(se,Je),e(j,ae),e(j,re),e(re,Ye),e(j,Ae),e(j,H),e(H,Ze),e(j,Ce),f(T,ee,q),f(T,N,q),e(N,L),e(L,me),e(L,B),e(B,Xe),e(L,Le),e(L,I),e(I,te),e(N,ve),e(N,A),e(A,Qe),e(A,R),e(R,et),e(A,Oe),e(A,P),e(P,ke),e(N,Te),e(N,W),e(W,tt),e(W,V),e(V,ie),f(T,ye,q),f(T,C,q),e(C,ot),e(C,S),e(S,nt),e(C,be)},d(T){T&&t(h),T&&t(l),T&&t(u),T&&t(X),T&&t(j),T&&t(ee),T&&t(N),T&&t(ye),T&&t(C)}}}function Pf(z){let h,k,_,g,v;return{c(){h=n("p"),k=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(u,"CODE",{});var E=a(_);g=i(E,"Module"),E.forEach(t),v=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,k),e(h,_),e(_,g),e(h,v)},d(l){l&&t(h)}}}function Ff(z){let h,k,_,g,v;return g=new Fe({props:{code:`from transformers import AutoTokenizer, TFMarianModel
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
model = TFMarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")

inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
outputs = model(inputs)

last_hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFMarianModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-de&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMarianModel.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-de&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){h=n("p"),k=r("Example:"),_=c(),y(g.$$.fragment)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Example:"),u.forEach(t),_=p(l),b(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,k),f(l,_,u),M(g,l,u),v=!0},p:Yt,i(l){v||(w(g.$$.fragment,l),v=!0)},o(l){x(g.$$.fragment,l),v=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function Nf(z){let h,k,_,g,v,l,u,E,Ke,Ne,O,he,X,j,Ge,D,ue,ct,Q,U,_e,se,Je,ae,re,Ye,Ae,H,Ze,Ce,ee,N,L,me,B,Xe,Le,I,te,ve,A,Qe,R,et,Oe,P,ke,Te,W,tt,V,ie,ye,C,ot,S,nt,be;return{c(){h=n("p"),k=r("TensorFlow models and layers in "),_=n("code"),g=r("transformers"),v=r(" accept two formats as input:"),l=c(),u=n("ul"),E=n("li"),Ke=r("having all inputs as keyword arguments (like PyTorch models), or"),Ne=c(),O=n("li"),he=r("having all inputs as a list, tuple or dict in the first positional argument."),X=c(),j=n("p"),Ge=r(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(u,"CODE",{});var E=a(_);g=i(E,"Module"),E.forEach(t),v=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,k),e(h,_),e(_,g),e(h,v)},d(l){l&&t(h)}}}function Cf(z){let h,k,_,g,v;return g=new Fe({props:{code:`from transformers import AutoTokenizer, TFMarianMTModel
from typing import List

src = "fr"  # source language
trg = "en"  # target language
sample_text = "o\xF9 est l'arr\xEAt de bus ?"
model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"

model = TFMarianMTModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
batch = tokenizer([sample_text], return_tensors="tf")
gen = model.generate(**batch)
tokenizer.batch_decode(gen, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFMarianMTModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">List</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>src = <span class="hljs-string">&quot;fr&quot;</span>  <span class="hljs-comment"># source language</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>trg = <span class="hljs-string">&quot;en&quot;</span>  <span class="hljs-comment"># target language</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>sample_text = <span class="hljs-string">&quot;o\xF9 est l&#x27;arr\xEAt de bus ?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = <span class="hljs-string">f&quot;Helsinki-NLP/opus-mt-<span class="hljs-subst">{src}</span>-<span class="hljs-subst">{trg}</span>&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMarianMTModel.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span>batch = tokenizer([sample_text], return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>gen = model.generate(**batch)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(gen, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">&quot;Where is the bus stop ?&quot;</span>`}}),{c(){h=n("p"),k=r("Examples:"),_=c(),y(g.$$.fragment)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Examples:"),u.forEach(t),_=p(l),b(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,k),f(l,_,u),M(g,l,u),v=!0},p:Yt,i(l){v||(w(g.$$.fragment,l),v=!0)},o(l){x(g.$$.fragment,l),v=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function Lf(z){let h,k,_,g,v;return{c(){h=n("p"),k=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(u,"CODE",{});var E=a(_);g=i(E,"Module"),E.forEach(t),v=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,k),e(h,_),e(_,g),e(h,v)},d(l){l&&t(h)}}}function Of(z){let h,k,_,g,v;return g=new Fe({props:{code:`from transformers import AutoTokenizer, FlaxMarianModel

tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
model = FlaxMarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")

inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxMarianModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-de&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxMarianModel.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-de&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){h=n("p"),k=r("Example:"),_=c(),y(g.$$.fragment)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Example:"),u.forEach(t),_=p(l),b(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,k),f(l,_,u),M(g,l,u),v=!0},p:Yt,i(l){v||(w(g.$$.fragment,l),v=!0)},o(l){x(g.$$.fragment,l),v=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function If(z){let h,k,_,g,v;return{c(){h=n("p"),k=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(u,"CODE",{});var E=a(_);g=i(E,"Module"),E.forEach(t),v=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,k),e(h,_),e(_,g),e(h,v)},d(l){l&&t(h)}}}function Sf(z){let h,k,_,g,v;return g=new Fe({props:{code:`from transformers import AutoTokenizer, FlaxMarianMTModel

model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")

text = "My friends are cool but they eat too many carbs."
input_ids = tokenizer(text, max_length=64, return_tensors="jax").input_ids

sequences = model.generate(input_ids, max_length=64, num_beams=2).sequences

outputs = tokenizer.batch_decode(sequences, skip_special_tokens=True)
# should give *Meine Freunde sind cool, aber sie essen zu viele Kohlenhydrate.*`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxMarianMTModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxMarianMTModel.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-de&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-de&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;My friends are cool but they eat too many carbs.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(text, max_length=<span class="hljs-number">64</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span>sequences = model.generate(input_ids, max_length=<span class="hljs-number">64</span>, num_beams=<span class="hljs-number">2</span>).sequences

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = tokenizer.batch_decode(sequences, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># should give *Meine Freunde sind cool, aber sie essen zu viele Kohlenhydrate.*</span>`}}),{c(){h=n("p"),k=r("Example:"),_=c(),y(g.$$.fragment)},l(l){h=s(l,"P",{});var u=a(h);k=i(u,"Example:"),u.forEach(t),_=p(l),b(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,k),f(l,_,u),M(g,l,u),v=!0},p:Yt,i(l){v||(w(g.$$.fragment,l),v=!0)},o(l){x(g.$$.fragment,l),v=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function Df(z){let h,k,_,g,v,l,u,E,Ke,Ne,O,he,X,j,Ge,D,ue,ct,Q,U,_e,se,Je,ae,re,Ye,Ae,H,Ze,Ce,ee,N,L,me,B,Xe,Le,I,te,ve,A,Qe,R,et,Oe,P,ke,Te,W,tt,V,ie,ye,C,ot,S,nt,be,T,q,Me,we,_t,xe,F,vt,kt,st,at,Tt,K,G,rt,yt,le,$e,ze,it,Ie,je,bt,al,Mt,Vo,rl,aa,il,ll,dl,Ko,cl,ra,pl,hl,ul,wt,ml,ia,fl,gl,la,_l,vl,kl,da,Go,Tl,ca,yl,bl,Ml,pa,Jo,wl,Yo,xl,$l,qr,xt,Zt,ha,Zo,zl,ua,jl,Pr,Se,xs,El,ma,ql,Pl,Xo,Fl,Qo,Nl,Al,Cl,$t,Ll,fa,Ol,Il,ga,Sl,Dl,Hl,_a,Ul,Fr,zt,Xt,va,en,Bl,ka,Rl,Nr,Qt,Ta,Wl,Vl,ya,tn,Kl,Ar,jt,eo,ba,on,Gl,Ma,Jl,Cr,De,nn,Yl,wa,Zl,Xl,Ql,sn,ed,xa,td,od,nd,an,sd,rn,ad,rd,id,ln,ld,$a,dd,cd,Lr,to,pd,dn,hd,ud,Or,cn,Ir,$s,md,Sr,pn,Dr,Et,oo,za,hn,fd,ja,gd,Hr,zs,_d,Ur,un,Br,js,vd,Rr,mn,Wr,qt,no,Ea,fn,kd,qa,Td,Vr,pt,Pa,Es,yd,bd,Fa,qs,Md,wd,Na,Ps,xd,Kr,Pt,so,Aa,gn,$d,Ca,zd,Gr,Ee,_n,jd,Ft,Ed,Fs,qd,Pd,vn,Fd,Nd,Ad,Nt,Cd,Ns,Ld,Od,As,Id,Sd,Dd,ao,Jr,At,ro,La,kn,Hd,Oa,Ud,Yr,de,Tn,Bd,yn,Rd,bn,Wd,Vd,Kd,Mn,Gd,Cs,Jd,Yd,Zd,io,Xd,lo,wn,Qd,Ia,ec,Zr,Ct,co,Sa,xn,tc,Da,oc,Xr,qe,$n,nc,zn,sc,Ls,ac,rc,ic,jn,lc,En,dc,cc,pc,He,qn,hc,Lt,uc,Os,mc,fc,Ha,gc,_c,vc,po,kc,ho,Qr,Ot,uo,Ua,Pn,Tc,Ba,yc,ei,Pe,Fn,bc,Nn,Mc,Is,wc,xc,$c,An,zc,Cn,jc,Ec,qc,fe,Ln,Pc,It,Fc,Ss,Nc,Ac,Ra,Cc,Lc,Oc,mo,Ic,On,Sc,In,Dc,Hc,Uc,fo,ti,St,go,Wa,Sn,Bc,Va,Rc,oi,Dt,Dn,Wc,_o,Hn,Vc,vo,ni,Ht,ko,Ka,Un,Kc,Ga,Gc,si,ce,Bn,Jc,Rn,Yc,Ds,Zc,Xc,Qc,Wn,ep,Vn,tp,op,np,To,sp,Ue,Kn,ap,Ut,rp,Hs,ip,lp,Ja,dp,cp,pp,yo,hp,bo,ai,Bt,Mo,Ya,Gn,up,Za,mp,ri,pe,Jn,fp,Yn,gp,Us,_p,vp,kp,Zn,Tp,Xn,yp,bp,Mp,wo,wp,ge,Qn,xp,Rt,$p,Bs,zp,jp,Xa,Ep,qp,Pp,xo,Fp,es,Np,ts,Ap,Cp,Lp,$o,ii,Wt,zo,Qa,os,Op,er,Ip,li,J,ns,Sp,ss,Dp,Rs,Hp,Up,Bp,as,Rp,rs,Wp,Vp,Kp,tr,Gp,Jp,lt,or,is,Yp,Zp,nr,ls,Xp,Qp,sr,ds,eh,th,ar,cs,oh,nh,Be,ps,sh,Vt,ah,rr,rh,ih,ir,lh,dh,ch,jo,ph,Eo,di,Kt,qo,lr,hs,hh,dr,uh,ci,Y,us,mh,ms,fh,Ws,gh,_h,vh,fs,kh,gs,Th,yh,bh,cr,Mh,wh,dt,pr,_s,xh,$h,hr,vs,zh,jh,ur,ks,Eh,qh,mr,Ts,Ph,Fh,Re,ys,Nh,Gt,Ah,fr,Ch,Lh,gr,Oh,Ih,Sh,Po,Dh,Fo,pi;return l=new ne({}),A=new ne({}),Zo=new ne({}),en=new ne({}),on=new ne({}),cn=new Fe({props:{code:`from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>fra<< this is a sentence in english that we want to translate to french",
    ">>por<< This should go to portuguese",
    ">>esp<< And this to Spanish",
]

model_name = "Helsinki-NLP/opus-mt-en-roa"
tokenizer = MarianTokenizer.from_pretrained(model_name)
print(tokenizer.supported_language_codes)

model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
[tokenizer.decode(t, skip_special_tokens=True) for t in translated]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> MarianMTModel, MarianTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>src_text = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&gt;&gt;fra&lt;&lt; this is a sentence in english that we want to translate to french&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&gt;&gt;por&lt;&lt; This should go to portuguese&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&gt;&gt;esp&lt;&lt; And this to Spanish&quot;</span>,
<span class="hljs-meta">... </span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = <span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-roa&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = MarianTokenizer.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.supported_language_codes)
[<span class="hljs-string">&#x27;&gt;&gt;zlm_Latn&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;mfe&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;hat&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;pap&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;ast&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;cat&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;ind&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;glg&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;wln&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;spa&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;fra&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;ron&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;por&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;ita&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;oci&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;arg&lt;&lt;&#x27;</span>, <span class="hljs-string">&#x27;&gt;&gt;min&lt;&lt;&#x27;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>model = MarianMTModel.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span>translated = model.generate(**tokenizer(src_text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>[tokenizer.decode(t, skip_special_tokens=<span class="hljs-literal">True</span>) <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> translated]
[<span class="hljs-string">&quot;c&#x27;est une phrase en anglais que nous voulons traduire en fran\xE7ais&quot;</span>,
 <span class="hljs-string">&#x27;Isto deve ir para o portugu\xEAs.&#x27;</span>,
 <span class="hljs-string">&#x27;Y esto al espa\xF1ol&#x27;</span>]`}}),pn=new Fe({props:{code:`from huggingface_hub import list_models

model_list = list_models()
org = "Helsinki-NLP"
model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
suffix = [x.split("/")[1] for x in model_ids]
old_style_multi_models = [f"{org}/{s}" for s in suffix if s != s.lower()]`,highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> list_models

model_list = list_models()
org = <span class="hljs-string">&quot;Helsinki-NLP&quot;</span>
model_ids = [x.modelId <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> model_list <span class="hljs-keyword">if</span> x.modelId.startswith(org)]
suffix = [x.split(<span class="hljs-string">&quot;/&quot;</span>)[<span class="hljs-number">1</span>] <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> model_ids]
old_style_multi_models = [<span class="hljs-string">f&quot;<span class="hljs-subst">{org}</span>/<span class="hljs-subst">{s}</span>&quot;</span> <span class="hljs-keyword">for</span> s <span class="hljs-keyword">in</span> suffix <span class="hljs-keyword">if</span> s != s.lower()]`}}),hn=new ne({}),un=new Fe({props:{code:`['Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU',
 'Helsinki-NLP/opus-mt-ROMANCE-en',
 'Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA',
 'Helsinki-NLP/opus-mt-de-ZH',
 'Helsinki-NLP/opus-mt-en-CELTIC',
 'Helsinki-NLP/opus-mt-en-ROMANCE',
 'Helsinki-NLP/opus-mt-es-NORWAY',
 'Helsinki-NLP/opus-mt-fi-NORWAY',
 'Helsinki-NLP/opus-mt-fi-ZH',
 'Helsinki-NLP/opus-mt-fi_nb_no_nn_ru_sv_en-SAMI',
 'Helsinki-NLP/opus-mt-sv-NORWAY',
 'Helsinki-NLP/opus-mt-sv-ZH']
GROUP_MEMBERS = {
 'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
 'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
 'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
 'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
 'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
 'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
 'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
}`,highlighted:`[<span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-ROMANCE-en&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-de-ZH&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-en-CELTIC&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-en-ROMANCE&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-es-NORWAY&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-fi-NORWAY&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-fi-ZH&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-fi_nb_no_nn_ru_sv_en-SAMI&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-sv-NORWAY&#x27;</span>,
 <span class="hljs-string">&#x27;Helsinki-NLP/opus-mt-sv-ZH&#x27;</span>]
GROUP_MEMBERS = {
 <span class="hljs-string">&#x27;ZH&#x27;</span>: [<span class="hljs-string">&#x27;cmn&#x27;</span>, <span class="hljs-string">&#x27;cn&#x27;</span>, <span class="hljs-string">&#x27;yue&#x27;</span>, <span class="hljs-string">&#x27;ze_zh&#x27;</span>, <span class="hljs-string">&#x27;zh_cn&#x27;</span>, <span class="hljs-string">&#x27;zh_CN&#x27;</span>, <span class="hljs-string">&#x27;zh_HK&#x27;</span>, <span class="hljs-string">&#x27;zh_tw&#x27;</span>, <span class="hljs-string">&#x27;zh_TW&#x27;</span>, <span class="hljs-string">&#x27;zh_yue&#x27;</span>, <span class="hljs-string">&#x27;zhs&#x27;</span>, <span class="hljs-string">&#x27;zht&#x27;</span>, <span class="hljs-string">&#x27;zh&#x27;</span>],
 <span class="hljs-string">&#x27;ROMANCE&#x27;</span>: [<span class="hljs-string">&#x27;fr&#x27;</span>, <span class="hljs-string">&#x27;fr_BE&#x27;</span>, <span class="hljs-string">&#x27;fr_CA&#x27;</span>, <span class="hljs-string">&#x27;fr_FR&#x27;</span>, <span class="hljs-string">&#x27;wa&#x27;</span>, <span class="hljs-string">&#x27;frp&#x27;</span>, <span class="hljs-string">&#x27;oc&#x27;</span>, <span class="hljs-string">&#x27;ca&#x27;</span>, <span class="hljs-string">&#x27;rm&#x27;</span>, <span class="hljs-string">&#x27;lld&#x27;</span>, <span class="hljs-string">&#x27;fur&#x27;</span>, <span class="hljs-string">&#x27;lij&#x27;</span>, <span class="hljs-string">&#x27;lmo&#x27;</span>, <span class="hljs-string">&#x27;es&#x27;</span>, <span class="hljs-string">&#x27;es_AR&#x27;</span>, <span class="hljs-string">&#x27;es_CL&#x27;</span>, <span class="hljs-string">&#x27;es_CO&#x27;</span>, <span class="hljs-string">&#x27;es_CR&#x27;</span>, <span class="hljs-string">&#x27;es_DO&#x27;</span>, <span class="hljs-string">&#x27;es_EC&#x27;</span>, <span class="hljs-string">&#x27;es_ES&#x27;</span>, <span class="hljs-string">&#x27;es_GT&#x27;</span>, <span class="hljs-string">&#x27;es_HN&#x27;</span>, <span class="hljs-string">&#x27;es_MX&#x27;</span>, <span class="hljs-string">&#x27;es_NI&#x27;</span>, <span class="hljs-string">&#x27;es_PA&#x27;</span>, <span class="hljs-string">&#x27;es_PE&#x27;</span>, <span class="hljs-string">&#x27;es_PR&#x27;</span>, <span class="hljs-string">&#x27;es_SV&#x27;</span>, <span class="hljs-string">&#x27;es_UY&#x27;</span>, <span class="hljs-string">&#x27;es_VE&#x27;</span>, <span class="hljs-string">&#x27;pt&#x27;</span>, <span class="hljs-string">&#x27;pt_br&#x27;</span>, <span class="hljs-string">&#x27;pt_BR&#x27;</span>, <span class="hljs-string">&#x27;pt_PT&#x27;</span>, <span class="hljs-string">&#x27;gl&#x27;</span>, <span class="hljs-string">&#x27;lad&#x27;</span>, <span class="hljs-string">&#x27;an&#x27;</span>, <span class="hljs-string">&#x27;mwl&#x27;</span>, <span class="hljs-string">&#x27;it&#x27;</span>, <span class="hljs-string">&#x27;it_IT&#x27;</span>, <span class="hljs-string">&#x27;co&#x27;</span>, <span class="hljs-string">&#x27;nap&#x27;</span>, <span class="hljs-string">&#x27;scn&#x27;</span>, <span class="hljs-string">&#x27;vec&#x27;</span>, <span class="hljs-string">&#x27;sc&#x27;</span>, <span class="hljs-string">&#x27;ro&#x27;</span>, <span class="hljs-string">&#x27;la&#x27;</span>],
 <span class="hljs-string">&#x27;NORTH_EU&#x27;</span>: [<span class="hljs-string">&#x27;de&#x27;</span>, <span class="hljs-string">&#x27;nl&#x27;</span>, <span class="hljs-string">&#x27;fy&#x27;</span>, <span class="hljs-string">&#x27;af&#x27;</span>, <span class="hljs-string">&#x27;da&#x27;</span>, <span class="hljs-string">&#x27;fo&#x27;</span>, <span class="hljs-string">&#x27;is&#x27;</span>, <span class="hljs-string">&#x27;no&#x27;</span>, <span class="hljs-string">&#x27;nb&#x27;</span>, <span class="hljs-string">&#x27;nn&#x27;</span>, <span class="hljs-string">&#x27;sv&#x27;</span>],
 <span class="hljs-string">&#x27;SCANDINAVIA&#x27;</span>: [<span class="hljs-string">&#x27;da&#x27;</span>, <span class="hljs-string">&#x27;fo&#x27;</span>, <span class="hljs-string">&#x27;is&#x27;</span>, <span class="hljs-string">&#x27;no&#x27;</span>, <span class="hljs-string">&#x27;nb&#x27;</span>, <span class="hljs-string">&#x27;nn&#x27;</span>, <span class="hljs-string">&#x27;sv&#x27;</span>],
 <span class="hljs-string">&#x27;SAMI&#x27;</span>: [<span class="hljs-string">&#x27;se&#x27;</span>, <span class="hljs-string">&#x27;sma&#x27;</span>, <span class="hljs-string">&#x27;smj&#x27;</span>, <span class="hljs-string">&#x27;smn&#x27;</span>, <span class="hljs-string">&#x27;sms&#x27;</span>],
 <span class="hljs-string">&#x27;NORWAY&#x27;</span>: [<span class="hljs-string">&#x27;nb_NO&#x27;</span>, <span class="hljs-string">&#x27;nb&#x27;</span>, <span class="hljs-string">&#x27;nn_NO&#x27;</span>, <span class="hljs-string">&#x27;nn&#x27;</span>, <span class="hljs-string">&#x27;nog&#x27;</span>, <span class="hljs-string">&#x27;no_nb&#x27;</span>, <span class="hljs-string">&#x27;no&#x27;</span>],
 <span class="hljs-string">&#x27;CELTIC&#x27;</span>: [<span class="hljs-string">&#x27;ga&#x27;</span>, <span class="hljs-string">&#x27;cy&#x27;</span>, <span class="hljs-string">&#x27;br&#x27;</span>, <span class="hljs-string">&#x27;gd&#x27;</span>, <span class="hljs-string">&#x27;kw&#x27;</span>, <span class="hljs-string">&#x27;gv&#x27;</span>]
}`}}),mn=new Fe({props:{code:`from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>fr<< this is a sentence in english that we want to translate to french",
    ">>pt<< This should go to portuguese",
    ">>es<< And this to Spanish",
]

model_name = "Helsinki-NLP/opus-mt-en-ROMANCE"
tokenizer = MarianTokenizer.from_pretrained(model_name)

model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> MarianMTModel, MarianTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>src_text = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&gt;&gt;fr&lt;&lt; this is a sentence in english that we want to translate to french&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&gt;&gt;pt&lt;&lt; This should go to portuguese&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&gt;&gt;es&lt;&lt; And this to Spanish&quot;</span>,
<span class="hljs-meta">... </span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = <span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-ROMANCE&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = MarianTokenizer.from_pretrained(model_name)

<span class="hljs-meta">&gt;&gt;&gt; </span>model = MarianMTModel.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span>translated = model.generate(**tokenizer(src_text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>tgt_text = [tokenizer.decode(t, skip_special_tokens=<span class="hljs-literal">True</span>) <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> translated]
[<span class="hljs-string">&quot;c&#x27;est une phrase en anglais que nous voulons traduire en fran\xE7ais&quot;</span>, 
 <span class="hljs-string">&#x27;Isto deve ir para o portugu\xEAs.&#x27;</span>,
 <span class="hljs-string">&#x27;Y esto al espa\xF1ol&#x27;</span>]`}}),fn=new ne({}),gn=new ne({}),_n=new Z({props:{name:"class transformers.MarianConfig",anchor:"transformers.MarianConfig",parameters:[{name:"vocab_size",val:" = 58101"},{name:"decoder_vocab_size",val:" = None"},{name:"max_position_embeddings",val:" = 1024"},{name:"encoder_layers",val:" = 12"},{name:"encoder_ffn_dim",val:" = 4096"},{name:"encoder_attention_heads",val:" = 16"},{name:"decoder_layers",val:" = 12"},{name:"decoder_ffn_dim",val:" = 4096"},{name:"decoder_attention_heads",val:" = 16"},{name:"encoder_layerdrop",val:" = 0.0"},{name:"decoder_layerdrop",val:" = 0.0"},{name:"use_cache",val:" = True"},{name:"is_encoder_decoder",val:" = True"},{name:"activation_function",val:" = 'gelu'"},{name:"d_model",val:" = 1024"},{name:"dropout",val:" = 0.1"},{name:"attention_dropout",val:" = 0.0"},{name:"activation_dropout",val:" = 0.0"},{name:"init_std",val:" = 0.02"},{name:"decoder_start_token_id",val:" = 58100"},{name:"scale_embedding",val:" = False"},{name:"pad_token_id",val:" = 58100"},{name:"eos_token_id",val:" = 0"},{name:"forced_eos_token_id",val:" = 0"},{name:"share_encoder_decoder_embeddings",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MarianConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 58101) &#x2014;
Vocabulary size of the Marian model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianModel">MarianModel</a> or <a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a>.`,name:"vocab_size"},{anchor:"transformers.MarianConfig.d_model",description:`<strong>d_model</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Dimensionality of the layers and the pooler layer.`,name:"d_model"},{anchor:"transformers.MarianConfig.encoder_layers",description:`<strong>encoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of encoder layers.`,name:"encoder_layers"},{anchor:"transformers.MarianConfig.decoder_layers",description:`<strong>decoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of decoder layers.`,name:"decoder_layers"},{anchor:"transformers.MarianConfig.encoder_attention_heads",description:`<strong>encoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"encoder_attention_heads"},{anchor:"transformers.MarianConfig.decoder_attention_heads",description:`<strong>decoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer decoder.`,name:"decoder_attention_heads"},{anchor:"transformers.MarianConfig.decoder_ffn_dim",description:`<strong>decoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"decoder_ffn_dim"},{anchor:"transformers.MarianConfig.encoder_ffn_dim",description:`<strong>encoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"encoder_ffn_dim"},{anchor:"transformers.MarianConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"activation_function"},{anchor:"transformers.MarianConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.MarianConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.MarianConfig.activation_dropout",description:`<strong>activation_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for activations inside the fully connected layer.`,name:"activation_dropout"},{anchor:"transformers.MarianConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.MarianConfig.init_std",description:`<strong>init_std</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"init_std"},{anchor:"transformers.MarianConfig.encoder_layerdrop",description:`<strong>encoder_layerdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The LayerDrop probability for the encoder. See the [LayerDrop paper](see <a href="https://arxiv.org/abs/1909.11556" rel="nofollow">https://arxiv.org/abs/1909.11556</a>)
for more details.`,name:"encoder_layerdrop"},{anchor:"transformers.MarianConfig.decoder_layerdrop",description:`<strong>decoder_layerdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The LayerDrop probability for the decoder. See the [LayerDrop paper](see <a href="https://arxiv.org/abs/1909.11556" rel="nofollow">https://arxiv.org/abs/1909.11556</a>)
for more details.`,name:"decoder_layerdrop"},{anchor:"transformers.MarianConfig.scale_embedding",description:`<strong>scale_embedding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Scale embeddings by diving by sqrt(d_model).`,name:"scale_embedding"},{anchor:"transformers.MarianConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models)`,name:"use_cache"},{anchor:"transformers.MarianConfig.forced_eos_token_id",description:`<strong>forced_eos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The id of the token to force as the last generated token when <code>max_length</code> is reached. Usually set to
<code>eos_token_id</code>.`,name:"forced_eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/configuration_marian.py#L34"}}),ao=new Jt({props:{anchor:"transformers.MarianConfig.example",$$slots:{default:[Mf]},$$scope:{ctx:z}}}),kn=new ne({}),Tn=new Z({props:{name:"class transformers.MarianTokenizer",anchor:"transformers.MarianTokenizer",parameters:[{name:"source_spm",val:""},{name:"target_spm",val:""},{name:"vocab",val:""},{name:"target_vocab_file",val:" = None"},{name:"source_lang",val:" = None"},{name:"target_lang",val:" = None"},{name:"unk_token",val:" = '<unk>'"},{name:"eos_token",val:" = '</s>'"},{name:"pad_token",val:" = '<pad>'"},{name:"model_max_length",val:" = 512"},{name:"sp_model_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"separate_vocabs",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MarianTokenizer.source_spm",description:`<strong>source_spm</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a .spm extension) that
contains the vocabulary for the source language.`,name:"source_spm"},{anchor:"transformers.MarianTokenizer.target_spm",description:`<strong>target_spm</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a .spm extension) that
contains the vocabulary for the target language.`,name:"target_spm"},{anchor:"transformers.MarianTokenizer.source_lang",description:`<strong>source_lang</strong> (<code>str</code>, <em>optional</em>) &#x2014;
A string representing the source language.`,name:"source_lang"},{anchor:"transformers.MarianTokenizer.target_lang",description:`<strong>target_lang</strong> (<code>str</code>, <em>optional</em>) &#x2014;
A string representing the target language.`,name:"target_lang"},{anchor:"transformers.MarianTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.MarianTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.MarianTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.MarianTokenizer.model_max_length",description:`<strong>model_max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sentence length the model accepts.`,name:"model_max_length"},{anchor:"transformers.MarianTokenizer.additional_special_tokens",description:`<strong>additional_special_tokens</strong> (<code>List[str]</code>, <em>optional</em>, defaults to <code>[&quot;&lt;eop&gt;&quot;, &quot;&lt;eod&gt;&quot;]</code>) &#x2014;
Additional special tokens used by the tokenizer.`,name:"additional_special_tokens"},{anchor:"transformers.MarianTokenizer.sp_model_kwargs",description:`<strong>sp_model_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Will be passed to the <code>SentencePieceProcessor.__init__()</code> method. The <a href="https://github.com/google/sentencepiece/tree/master/python" rel="nofollow">Python wrapper for
SentencePiece</a> can be used, among other things,
to set:</p>
<ul>
<li>
<p><code>enable_sampling</code>: Enable subword regularization.</p>
</li>
<li>
<p><code>nbest_size</code>: Sampling parameters for unigram. Invalid for BPE-Dropout.</p>
<ul>
<li><code>nbest_size = {0,1}</code>: No sampling is performed.</li>
<li><code>nbest_size &gt; 1</code>: samples from the nbest_size results.</li>
<li><code>nbest_size &lt; 0</code>: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.</li>
</ul>
</li>
<li>
<p><code>alpha</code>: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.</p>
</li>
</ul>`,name:"sp_model_kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/tokenization_marian.py#L61"}}),io=new Jt({props:{anchor:"transformers.MarianTokenizer.example",$$slots:{default:[wf]},$$scope:{ctx:z}}}),wn=new Z({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.MarianTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:""},{name:"token_ids_1",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/tokenization_marian.py#L281"}}),xn=new ne({}),$n=new Z({props:{name:"class transformers.MarianModel",anchor:"transformers.MarianModel",parameters:[{name:"config",val:": MarianConfig"}],parametersDescription:[{anchor:"transformers.MarianModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_marian.py#L1095"}}),qn=new Z({props:{name:"forward",anchor:"transformers.MarianModel.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_outputs",val:": typing.Union[typing.Tuple[torch.Tensor], transformers.modeling_outputs.BaseModelOutput, NoneType] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MarianModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.MarianModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.MarianModel.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>Marian uses the <code>pad_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.MarianModel.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"transformers.MarianModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.MarianModel.forward.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.MarianModel.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.MarianModel.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.MarianModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.MarianModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MarianModel.forward.decoder_inputs_embeds",description:`<strong>decoder_inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, target_sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>decoder_input_ids</code> you can choose to directly pass an embedded
representation. If <code>past_key_values</code> is used, optionally only the last <code>decoder_inputs_embeds</code> have to be
input (see <code>past_key_values</code>). This is useful if you want more control over how to convert
<code>decoder_input_ids</code> indices into associated vectors than the model&#x2019;s internal embedding lookup matrix.</p>
<p>If <code>decoder_input_ids</code> and <code>decoder_inputs_embeds</code> are both unset, <code>decoder_inputs_embeds</code> takes the value
of <code>inputs_embeds</code>.`,name:"decoder_inputs_embeds"},{anchor:"transformers.MarianModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.MarianModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.MarianModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.MarianModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_marian.py#L1179",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput"
>transformers.modeling_outputs.Seq2SeqModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig"
>MarianConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput"
>transformers.modeling_outputs.Seq2SeqModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),po=new Wo({props:{$$slots:{default:[xf]},$$scope:{ctx:z}}}),ho=new Jt({props:{anchor:"transformers.MarianModel.forward.example",$$slots:{default:[$f]},$$scope:{ctx:z}}}),Pn=new ne({}),Fn=new Z({props:{name:"class transformers.MarianMTModel",anchor:"transformers.MarianMTModel",parameters:[{name:"config",val:": MarianConfig"}],parametersDescription:[{anchor:"transformers.MarianMTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_marian.py#L1281"}}),Ln=new Z({props:{name:"forward",anchor:"transformers.MarianMTModel.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_outputs",val:": typing.Union[typing.Tuple[torch.Tensor], transformers.modeling_outputs.BaseModelOutput, NoneType] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MarianMTModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.MarianMTModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.MarianMTModel.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>Marian uses the <code>pad_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.MarianMTModel.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"transformers.MarianMTModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.MarianMTModel.forward.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.MarianMTModel.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.MarianMTModel.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.MarianMTModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.MarianMTModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MarianMTModel.forward.decoder_inputs_embeds",description:`<strong>decoder_inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, target_sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>decoder_input_ids</code> you can choose to directly pass an embedded
representation. If <code>past_key_values</code> is used, optionally only the last <code>decoder_inputs_embeds</code> have to be
input (see <code>past_key_values</code>). This is useful if you want more control over how to convert
<code>decoder_input_ids</code> indices into associated vectors than the model&#x2019;s internal embedding lookup matrix.</p>
<p>If <code>decoder_input_ids</code> and <code>decoder_inputs_embeds</code> are both unset, <code>decoder_inputs_embeds</code> takes the value
of <code>inputs_embeds</code>.`,name:"decoder_inputs_embeds"},{anchor:"transformers.MarianMTModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.MarianMTModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.MarianMTModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.MarianMTModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.MarianMTModel.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should either be in <code>[0, ..., config.vocab_size]</code> or -100 (see <code>input_ids</code> docstring). Tokens with indices set to <code>-100</code> are ignored
(masked), the loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_marian.py#L1408",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput"
>transformers.modeling_outputs.Seq2SeqLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig"
>MarianConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput"
>transformers.modeling_outputs.Seq2SeqLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),mo=new Wo({props:{$$slots:{default:[zf]},$$scope:{ctx:z}}}),fo=new Jt({props:{anchor:"transformers.MarianMTModel.forward.example",$$slots:{default:[jf]},$$scope:{ctx:z}}}),Sn=new ne({}),Dn=new Z({props:{name:"class transformers.MarianForCausalLM",anchor:"transformers.MarianForCausalLM",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_marian.py#L1552"}}),Hn=new Z({props:{name:"forward",anchor:"transformers.MarianForCausalLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MarianForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.MarianForCausalLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.MarianForCausalLM.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.MarianForCausalLM.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:`,name:"encoder_attention_mask"},{anchor:"transformers.MarianForCausalLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.MarianForCausalLM.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.MarianForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of
shape <code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of
shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those
that don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of
all <code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.MarianForCausalLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should either be in <code>[0, ..., config.vocab_size]</code> or -100 (see <code>input_ids</code> docstring). Tokens with indices set to <code>-100</code> are ignored
(masked), the loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>.`,name:"labels"},{anchor:"transformers.MarianForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding
(see <code>past_key_values</code>).</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"use_cache"},{anchor:"transformers.MarianForCausalLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.MarianForCausalLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more detail.`,name:"output_hidden_states"},{anchor:"transformers.MarianForCausalLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_marian.py#L1585",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig"
>MarianConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),vo=new Jt({props:{anchor:"transformers.MarianForCausalLM.forward.example",$$slots:{default:[Ef]},$$scope:{ctx:z}}}),Un=new ne({}),Bn=new Z({props:{name:"class transformers.TFMarianModel",anchor:"transformers.TFMarianModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMarianModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_tf_marian.py#L1202"}}),To=new Wo({props:{$$slots:{default:[qf]},$$scope:{ctx:z}}}),Kn=new Z({props:{name:"call",anchor:"transformers.TFMarianModel.call",parameters:[{name:"input_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_position_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"head_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_head_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"encoder_outputs",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[tensorflow.python.framework.ops.Tensor]]] = None"},{name:"inputs_embeds",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"training",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMarianModel.call.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMarianModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMarianModel.call.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>Marian uses the <code>pad_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.TFMarianModel.call.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.`,name:"decoder_attention_mask"},{anchor:"transformers.TFMarianModel.call.decoder_position_ids",description:`<strong>decoder_position_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"decoder_position_ids"},{anchor:"transformers.TFMarianModel.call.head_mask",description:`<strong>head_mask</strong> (<code>tf.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMarianModel.call.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>tf.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.TFMarianModel.call.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>tf.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.TFMarianModel.call.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tf.FloatTensor</code>, <em>optional</em>) &#x2014;
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape <code>(batch_size, sequence_length, hidden_size)</code> is a sequence of`,name:"encoder_outputs"},{anchor:"transformers.TFMarianModel.call.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[tf.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.TFMarianModel.call.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>). Set to <code>False</code> during training, <code>True</code> during generation`,name:"use_cache"},{anchor:"transformers.TFMarianModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFMarianModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFMarianModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFMarianModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_tf_marian.py#L1214",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput"
>transformers.modeling_tf_outputs.TFSeq2SeqModelOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig"
>MarianConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput"
>transformers.modeling_tf_outputs.TFSeq2SeqModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),yo=new Wo({props:{$$slots:{default:[Pf]},$$scope:{ctx:z}}}),bo=new Jt({props:{anchor:"transformers.TFMarianModel.call.example",$$slots:{default:[Ff]},$$scope:{ctx:z}}}),Gn=new ne({}),Jn=new Z({props:{name:"class transformers.TFMarianMTModel",anchor:"transformers.TFMarianMTModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMarianMTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_tf_marian.py#L1307"}}),wo=new Wo({props:{$$slots:{default:[Nf]},$$scope:{ctx:z}}}),Qn=new Z({props:{name:"call",anchor:"transformers.TFMarianMTModel.call",parameters:[{name:"input_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_position_ids",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"head_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_head_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"encoder_outputs",val:": typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[tensorflow.python.framework.ops.Tensor]]] = None"},{name:"inputs_embeds",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFMarianMTModel.call.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMarianMTModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>tf.Tensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMarianMTModel.call.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>Marian uses the <code>pad_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.TFMarianMTModel.call.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.`,name:"decoder_attention_mask"},{anchor:"transformers.TFMarianMTModel.call.decoder_position_ids",description:`<strong>decoder_position_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"decoder_position_ids"},{anchor:"transformers.TFMarianMTModel.call.head_mask",description:`<strong>head_mask</strong> (<code>tf.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMarianMTModel.call.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>tf.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.TFMarianMTModel.call.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>tf.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.TFMarianMTModel.call.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tf.FloatTensor</code>, <em>optional</em>) &#x2014;
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape <code>(batch_size, sequence_length, hidden_size)</code> is a sequence of`,name:"encoder_outputs"},{anchor:"transformers.TFMarianMTModel.call.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[tf.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.TFMarianMTModel.call.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>). Set to <code>False</code> during training, <code>True</code> during generation`,name:"use_cache"},{anchor:"transformers.TFMarianMTModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFMarianMTModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFMarianMTModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFMarianMTModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFMarianMTModel.call.labels",description:`<strong>labels</strong> (<code>tf.tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should either be in <code>[0, ..., config.vocab_size]</code> or -100 (see <code>input_ids</code> docstring). Tokens with indices set to <code>-100</code> are ignored
(masked), the loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_tf_marian.py#L1345",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput"
>transformers.modeling_tf_outputs.TFSeq2SeqLMOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig"
>MarianConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) \u2014 Language modeling loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput"
>transformers.modeling_tf_outputs.TFSeq2SeqLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),xo=new Wo({props:{$$slots:{default:[Af]},$$scope:{ctx:z}}}),$o=new Jt({props:{anchor:"transformers.TFMarianMTModel.call.example",$$slots:{default:[Cf]},$$scope:{ctx:z}}}),os=new ne({}),ns=new Z({props:{name:"class transformers.FlaxMarianModel",anchor:"transformers.FlaxMarianModel",parameters:[{name:"config",val:": MarianConfig"},{name:"input_shape",val:": typing.Tuple[int] = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxMarianModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
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The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_flax_marian.py#L1205"}}),ps=new Z({props:{name:"__call__",anchor:"transformers.FlaxMarianModel.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"attention_mask",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"decoder_input_ids",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"decoder_attention_mask",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"position_ids",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"decoder_position_ids",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"train",val:": bool = False"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"}],parametersDescription:[{anchor:"transformers.FlaxMarianModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxMarianModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxMarianModel.__call__.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>For translation and summarization training, <code>decoder_input_ids</code> should be provided. If no
<code>decoder_input_ids</code> is provided, the model will create this tensor by shifting the <code>input_ids</code> to the right
for denoising pre-training following the paper.`,name:"decoder_input_ids"},{anchor:"transformers.FlaxMarianModel.__call__.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.</p>
<p>If you want to change padding behavior, you should modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the
paper</a> for more information on the default strategy.`,name:"decoder_attention_mask"},{anchor:"transformers.FlaxMarianModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxMarianModel.__call__.decoder_position_ids",description:`<strong>decoder_position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"decoder_position_ids"},{anchor:"transformers.FlaxMarianModel.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxMarianModel.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxMarianModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_flax_marian.py#L1140",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput"
>transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig"
>MarianConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput"
>transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),jo=new Wo({props:{$$slots:{default:[Lf]},$$scope:{ctx:z}}}),Eo=new Jt({props:{anchor:"transformers.FlaxMarianModel.__call__.example",$$slots:{default:[Of]},$$scope:{ctx:z}}}),hs=new ne({}),us=new Z({props:{name:"class transformers.FlaxMarianMTModel",anchor:"transformers.FlaxMarianMTModel",parameters:[{name:"config",val:": MarianConfig"},{name:"input_shape",val:": typing.Tuple[int] = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxMarianMTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
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The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_flax_marian.py#L1289"}}),ys=new Z({props:{name:"__call__",anchor:"transformers.FlaxMarianMTModel.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"attention_mask",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"decoder_input_ids",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"decoder_attention_mask",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"position_ids",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"decoder_position_ids",val:": typing.Optional[jax._src.numpy.ndarray.ndarray] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"train",val:": bool = False"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"}],parametersDescription:[{anchor:"transformers.FlaxMarianMTModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxMarianMTModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxMarianMTModel.__call__.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>For translation and summarization training, <code>decoder_input_ids</code> should be provided. If no
<code>decoder_input_ids</code> is provided, the model will create this tensor by shifting the <code>input_ids</code> to the right
for denoising pre-training following the paper.`,name:"decoder_input_ids"},{anchor:"transformers.FlaxMarianMTModel.__call__.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.</p>
<p>If you want to change padding behavior, you should modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the
paper</a> for more information on the default strategy.`,name:"decoder_attention_mask"},{anchor:"transformers.FlaxMarianMTModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxMarianMTModel.__call__.decoder_position_ids",description:`<strong>decoder_position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"decoder_position_ids"},{anchor:"transformers.FlaxMarianMTModel.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxMarianMTModel.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxMarianMTModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/marian/modeling_flax_marian.py#L1140",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput"
>transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig"
>MarianConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput"
>transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Di.forEach(t),$c=p(So),An=s(So,"P",{});var Hi=a(An);zc=i(Hi,"This model is also a PyTorch "),Cn=s(Hi,"A",{href:!0,rel:!0});var gm=a(Cn);jc=i(gm,"torch.nn.Module"),gm.forEach(t),Ec=i(Hi,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Ki.forEach(t),Qc=p(mt),Wn=s(mt,"P",{});var Gi=a(Wn);ep=i(Gi,"This model is also a "),Vn=s(Gi,"A",{href:!0,rel:!0});var zm=a(Vn);tp=i(zm,"tf.keras.Model"),zm.forEach(t),op=i(Gi,` subclass. Use it
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This model inherits from `),Us=s(Yi,"A",{href:!0});var Nm=a(Us);_p=i(Nm,"TFPreTrainedModel"),Nm.forEach(t),vp=i(Yi,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Yi.forEach(t),kp=p(ft),Zn=s(ft,"P",{});var Zi=a(Zn);Tp=i(Zi,"This model is also a "),Xn=s(Zi,"A",{href:!0,rel:!0});var Am=a(Xn);yp=i(Am,"tf.keras.Model"),Am.forEach(t),bp=i(Zi,` subclass. Use it
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