import torch
from torch import nn
import torch.nn.functional as F


class PitchShift(nn.Module):

    def __init__(self, shift):
        super(PitchShift, self).__init__()
        self.shift = shift

    def forward(self, x):
        if len(x.shape) == 2:
            x = x.unsqueeze(0)
        x = x.squeeze()
        mel_size = x.shape[1]
        shift_scale = (mel_size + self.shift) / mel_size
        x = F.interpolate(x.unsqueeze(1), scale_factor=(shift_scale, 1.0),
            align_corners=False, recompute_scale_factor=True, mode='bilinear'
            ).squeeze(1)
        x = x[:, :mel_size]
        if x.size(1) < mel_size:
            pad_size = mel_size - x.size(1)
            x = torch.cat([x, torch.zeros(x.size(0), pad_size, x.size(2))],
                dim=1)
        x = x.squeeze()
        return x.unsqueeze(1)


def get_inputs():
    return [torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'shift': 4}]

import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0(
    in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 8
    x0 = xindex % 4
    x2 = xindex // 32
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 3, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tmp13 = x0
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp14 + tmp2
    tmp16 = 1.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp17 - tmp2
    tmp19 = triton_helpers.maximum(tmp18, tmp6)
    tmp20 = tmp19.to(tl.int32)
    tmp21 = tmp20 + tmp9
    tmp22 = triton_helpers.minimum(tmp21, tmp11)
    tmp23 = tl.load(in_ptr0 + (tmp22 + 4 * tmp12 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (tmp20 + 4 * tmp12 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp25 = tmp23 - tmp24
    tmp26 = tmp20.to(tl.float32)
    tmp27 = tmp19 - tmp26
    tmp28 = triton_helpers.maximum(tmp27, tmp6)
    tmp29 = triton_helpers.minimum(tmp28, tmp16)
    tmp30 = tmp25 * tmp29
    tmp31 = tmp24 + tmp30
    tmp32 = tl.load(in_ptr0 + (tmp20 + 4 * tmp8 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (tmp22 + 4 * tmp8 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp34 = tmp33 - tmp32
    tmp35 = tmp34 * tmp29
    tmp36 = tmp32 + tmp35
    tmp37 = tmp31 - tmp36
    tmp38 = tmp8.to(tl.float32)
    tmp39 = tmp7 - tmp38
    tmp40 = triton_helpers.maximum(tmp39, tmp6)
    tmp41 = triton_helpers.minimum(tmp40, tmp16)
    tmp42 = tmp37 * tmp41
    tmp43 = tmp36 + tmp42
    tl.store(in_out_ptr0 + x4, tmp43, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 8, 4), (32, 128, 4, 1), torch.float32)
        buf1 = buf0
        del buf0
        buf2 = reinterpret_tensor(buf1, (4, 1, 8, 4), (32, 32, 4, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
            (128)](buf2, arg0_1, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf2, (4, 1, 4, 4), (32, 16, 4, 1), 0),


class PitchShiftNew(nn.Module):

    def __init__(self, shift):
        super(PitchShiftNew, self).__init__()
        self.shift = shift

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
