



'''simple docstring'''



import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase	 =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
lowerCamelCase	 =       """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""





class _UpperCamelCase		(     unittest.TestCase		):





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											__lowercase       	=tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir  ,       'models/bert/'))
											__lowercase       	=self.transformer_dir
											shutil.copy(
											    os.path.join(_lowerCamelCase  ,       'src/transformers/models/bert/modeling_bert.py')  ,       os.path.join(self.transformer_dir  ,       'models/bert/modeling_bert.py')  ,       )

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											__lowercase       	='src/transformers'
											shutil.rmtree(self.transformer_dir)

				def 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  Optional[int]  ,       _lowerCAmelCase  :  Optional[int]  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  str=None):







											'''simple docstring'''

											__lowercase       	=comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
											if overwrite_result is not None:
																		__lowercase       	=comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
											__lowercase       	=black.Mode(target_versions={black.TargetVersion.PYaa}  ,       line_length=1_1_9)
											__lowercase       	=black.format_str(_lowerCamelCase  ,       mode=_lowerCamelCase)
											__lowercase       	=os.path.join(self.transformer_dir  ,       'new_code.py')
											with open(_lowerCamelCase  ,       'w'  ,       newline='\n') as f:
																		f.write(_lowerCamelCase)
											if overwrite_result is None:
																		self.assertTrue(len(check_copies.is_copy_consistent(_lowerCamelCase)) == 0)
											else:
																		check_copies.is_copy_consistent(f.name  ,       overwrite=_lowerCamelCase)
																		with open(_lowerCamelCase  ,       'r') as f:
																									self.assertTrue(f.read()  ,       _lowerCamelCase)

				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											__lowercase       	=check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead')
											self.assertEqual(_lowerCamelCase  ,       _lowerCamelCase)

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'  ,       'BertLMPredictionHead'  ,       REFERENCE_CODE + '\n'  ,       )

											# With no empty line at the end
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'  ,       'BertLMPredictionHead'  ,       _lowerCamelCase  ,       )

											# Copy consistency with rename
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'  ,       'TestModelLMPredictionHead'  ,       re.sub('Bert'  ,       'TestModel'  ,       _lowerCamelCase)  ,       )

											# Copy consistency with a really long name
											__lowercase       	='TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
											self.check_copy_consistency(
											    f"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""  ,       f"""{long_class_name}LMPredictionHead"""  ,       re.sub('Bert'  ,       _lowerCamelCase  ,       _lowerCamelCase)  ,       )

											# Copy consistency with overwrite
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'  ,       'TestModelLMPredictionHead'  ,       _lowerCamelCase  ,       overwrite_result=re.sub('Bert'  ,       'TestModel'  ,       _lowerCamelCase)  ,       )

				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											__lowercase       	=check_copies.LOCALIZED_READMES['README_zh-hans.md']

											__lowercase       	=(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
											    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
											    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
											    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
											    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
											    ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
											    ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
											    ' method has been applied to compress GPT2 into'
											    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
											    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
											    ' Multilingual BERT into'
											    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
											    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
											    ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
											    ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
											    ' Luong, Quoc V. Le, Christopher D. Manning.'
											)
											__lowercase       	=(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
											    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
											)
											__lowercase       	=(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
											    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
											    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
											    ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
											    ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
											    ' method has been applied to compress GPT2 into'
											    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
											    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
											    ' Multilingual BERT into'
											    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
											    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
											    ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
											    ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
											    ' Christopher D. Manning 发布。\n'
											)

											__lowercase	,				__lowercase       	=check_copies.convert_to_localized_md(
											    _lowerCamelCase  ,       _lowerCamelCase  ,       localized_readme['format_model_list'])

											self.assertFalse(_lowerCamelCase)
											self.assertEqual(_lowerCamelCase  ,       _lowerCamelCase)

											__lowercase	,				__lowercase       	=check_copies.convert_to_localized_md(
											    _lowerCamelCase  ,       _lowerCamelCase  ,       localized_readme['format_model_list'])

											# Check whether the number of models is equal to README.md after conversion.
											self.assertTrue(_lowerCamelCase)

											__lowercase       	=(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
											    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
											    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
											    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
											)
											__lowercase       	=(
											    '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
											    ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
											)
											__lowercase       	=(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
											    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
											)

											__lowercase	,				__lowercase       	=check_copies.convert_to_localized_md(
											    _lowerCamelCase  ,       _lowerCamelCase  ,       localized_readme['format_model_list'])

											# Check if the model link is synchronized.
											self.assertEqual(_lowerCamelCase  ,       _lowerCamelCase)








'''simple docstring'''



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	 =       logging.get_logger()





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    field(default_factory=A		)
				lowerCAmelCase__					    =    field(default_factory=A		)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  Tensor  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											__lowercase       	=len(list(m.modules())) == 1 or isinstance(_lowerCAmelCase  ,       nn.Convad) or isinstance(_lowerCAmelCase  ,       nn.BatchNormad)
											if has_not_submodules:
																		self.traced.append(_lowerCAmelCase)

				def __call__(							self  :  Dict  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook))
											self.module(_lowerCAmelCase)
											[x.remove() for x in self.handles]
											return self

				@property
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											return list(filter(lambda _lowerCAmelCase: len(list(x.state_dict().keys())) > 0  ,       self.traced))





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    0
				lowerCAmelCase__					    =    field(default_factory=A		)
				lowerCAmelCase__					    =    field(default_factory=A		)

				def __call__(							self  :  Any  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											__lowercase       	=Tracker(self.dest)(_lowerCAmelCase).parametrized
											__lowercase       	=Tracker(self.src)(_lowerCAmelCase).parametrized

											__lowercase       	=list(filter(lambda _lowerCAmelCase: type(_lowerCAmelCase) not in self.src_skip  ,       _lowerCAmelCase))
											__lowercase       	=list(filter(lambda _lowerCAmelCase: type(_lowerCAmelCase) not in self.dest_skip  ,       _lowerCAmelCase))

											if len(_lowerCAmelCase) != len(_lowerCAmelCase):
																		raise Exception(
																		    f"""Numbers of operations are different. Source module has {len(_lowerCAmelCase)} operations while"""
																		    f""" destination module has {len(_lowerCAmelCase)}.""")

											for dest_m, src_m in zip(_lowerCAmelCase  ,       _lowerCAmelCase):
																		dest_m.load_state_dict(src_m.state_dict())
																		if self.verbose == 1:
																									print(f"""Transfered from={src_m} to={dest_m}""")



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase = True					):



							"""simple docstring"""



							print(f"""Converting {name}..."""					)
							with torch.no_grad():
														__lowercase       	=timm.create_model(_lowerCAmelCase  ,   pretrained=_lowerCAmelCase					).eval()
														__lowercase       	=ResNetForImageClassification(_lowerCAmelCase					).eval()
														__lowercase       	=ModuleTransfer(src=_lowerCAmelCase  ,   dest=_lowerCAmelCase					)
														__lowercase       	=torch.randn((1, 3, 224, 224)					)
														module_transfer(_lowerCAmelCase					)

							assert torch.allclose(from_model(_lowerCAmelCase					)  ,   our_model(_lowerCAmelCase					).logits					), "The model logits don't match the original one."

							__lowercase       	=f"""resnet{'-'.join(name.split('resnet'					)					)}"""
							print(_lowerCAmelCase					)

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name  ,   commit_message='Add model'  ,   use_temp_dir=_lowerCAmelCase  ,   )

														# we can use the convnext one
														__lowercase       	=AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name  ,   commit_message='Add image processor'  ,   use_temp_dir=_lowerCAmelCase  ,   )

														print(f"""Pushed {checkpoint_name}"""					)



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = True					):



							"""simple docstring"""



							__lowercase       	='imagenet-1k-id2label.json'
							__lowercase       	=1_000
							__lowercase       	=(1, num_labels)

							__lowercase       	='huggingface/label-files'
							__lowercase       	=num_labels
							__lowercase       	=json.load(open(hf_hub_download(_lowerCAmelCase  ,   _lowerCAmelCase  ,   repo_type='dataset'					)  ,   'r'					)					)
							__lowercase       	={int(_lowerCAmelCase					): v for k, v in idalabel.items()}

							__lowercase       	=idalabel
							__lowercase       	={v: k for k, v in idalabel.items()}

							__lowercase       	=partial(_lowerCAmelCase  ,   num_labels=_lowerCAmelCase  ,   idalabel=_lowerCAmelCase  ,   labelaid=_lowerCAmelCase					)

							__lowercase       	={
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]  ,   hidden_sizes=[64, 128, 256, 512]  ,   layer_type='basic'					),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]  ,   hidden_sizes=[64, 128, 256, 512]  ,   layer_type='basic'					),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							}

							if model_name:
														convert_weight_and_push(_lowerCAmelCase  ,   names_to_config[model_name]  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
							return config, expected_shape


if __name__ == "__main__":
		lowerCamelCase	 =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--model_name""",
		    default=None,
		    type=str,
		    help=(
		        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
		        """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default=None,
		    type=Path,
		    required=True,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument(
		    """--push_to_hub""",
		    default=True,
		    type=bool,
		    required=False,
		    help="""If True, push model and image processor to the hub.""",
		)

		lowerCamelCase	 =       parser.parse_args()
		lowerCamelCase	 =       args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



