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"""
2025.3.17
2025.3.19
4.50.0
0.15.2
__UNSLOTH_VERSIONING__
"""
# Unsloth Zoo - Utilities for Unsloth
# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import os
import importlib.util
if importlib.util.find_spec("unsloth_studio") is None:
UNSLOTH_STUDIO_ENABLED = False
else:
UNSLOTH_STUDIO_ENABLED = os.environ.get("UNSLOTH_STUDIO_DISABLED", "0") == "0"
pass
from typing import List, Dict, Tuple, Optional, Any, Callable
import math
torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False}
from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from transformers.models.gemma3.modeling_gemma3 import (List, Optional, Tuple, nn)
def forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
if self.padding_mode != "zeros":
raise ValueError(
"Only `zeros` padding mode is supported for ConvTranspose2d"
)
assert isinstance(self.padding, tuple)
# One cannot replace List by Tuple or Sequence in "_output_padding" because
# TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
num_spatial_dims = 2
output_padding = self._output_padding(
input,
output_size,
self.stride, # type: ignore[arg-type]
self.padding, # type: ignore[arg-type]
self.kernel_size, # type: ignore[arg-type]
num_spatial_dims,
self.dilation, # type: ignore[arg-type]
)
return F.conv_transpose2d(
input,
self.weight,
self.bias,
self.stride,
self.padding,
output_padding,
self.groups,
self.dilation,
).to(input.dtype).to(input.dtype)
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