






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()
UpperCamelCase			    =     logging.get_logger()



@dataclass
class 				__UpperCAmelCase		:
			__snake_case    :				nn.Module
			__snake_case    :				List[nn.Module]									=							field(default_factory=_UpperCAmelCase   )
			__snake_case    :				list									=							field(default_factory=_UpperCAmelCase   )







			def 							UpperCamelCase				(     self:  Tuple				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Tensor				,     UpperCAmelCase_:  Tensor					):







									'''simple docstring'''




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







			def __call__(     self:  Union[str, Any]				,     UpperCAmelCase_:  Tensor					):







									'''simple docstring'''




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







			@property
			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




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



@dataclass
class 				__UpperCAmelCase		:
			__snake_case    :				nn.Module
			__snake_case    :				nn.Module
			__snake_case    :				int									=							0
			__snake_case    :				List									=							field(default_factory=_UpperCAmelCase   )
			__snake_case    :				List									=							field(default_factory=_UpperCAmelCase   )







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







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  Tracker(self.dest					)(UpperCAmelCase_					).parametrized
									_SCREAMING_SNAKE_CASE     				=  Tracker(self.src					)(UpperCAmelCase_					).parametrized

									_SCREAMING_SNAKE_CASE     				=  list(filter(lambda UpperCAmelCase_					: type(UpperCAmelCase_					) not in self.src_skip				,     UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  list(filter(lambda UpperCAmelCase_					: type(UpperCAmelCase_					) not in self.dest_skip				,     UpperCAmelCase_					)					)

									if len(UpperCAmelCase_					) != len(UpperCAmelCase_					):
															raise Exception(
															    F'Numbers of operations are different. Source module has {len(UpperCAmelCase_					)} operations while'
															    F' destination module has {len(UpperCAmelCase_					)}.'					)

									for dest_m, src_m in zip(UpperCAmelCase_				,     UpperCAmelCase_					):
															dest_m.load_state_dict(src_m.state_dict()					)
															if self.verbose == 1:
																					print(F'Transfered from={src_m} to={dest_m}'					)

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__ = True		)							->  Tuple:






						"""simple docstring"""





						print(F'Converting {name}...'		)
						with torch.no_grad():
												_SCREAMING_SNAKE_CASE     				=  timm.create_model(snake_case__	,pretrained=snake_case__		).eval()
												_SCREAMING_SNAKE_CASE     				=  ResNetForImageClassification(snake_case__		).eval()
												_SCREAMING_SNAKE_CASE     				=  ModuleTransfer(src=snake_case__	,dest=snake_case__		)
												_SCREAMING_SNAKE_CASE     				=  torch.randn((1, 3, 2_24, 2_24)		)
												module_transfer(snake_case__		)

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

						_SCREAMING_SNAKE_CASE     				=  F'resnet{"-".join(name.split("resnet"		)		)}'
						print(snake_case__		)

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

												# we can use the convnext one
												_SCREAMING_SNAKE_CASE     				=  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=snake_case__	,)

												print(F'Pushed {checkpoint_name}'		)

def 	__lowerCamelCase      (		snake_case__	,snake_case__ = None	,snake_case__ = True		)							->  List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  """imagenet-1k-id2label.json"""
						_SCREAMING_SNAKE_CASE     				=  10_00
						_SCREAMING_SNAKE_CASE     				=  (1, num_labels)

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

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

						_SCREAMING_SNAKE_CASE     				=  partial(snake_case__	,num_labels=snake_case__	,idalabel=snake_case__	,labelaid=snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  {
						    """resnet18""": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]	,hidden_sizes=[64, 1_28, 2_56, 5_12]	,layer_type="""basic"""		),
						    """resnet26""": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]	,hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,layer_type="""bottleneck"""		),
						    """resnet34""": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]	,hidden_sizes=[64, 1_28, 2_56, 5_12]	,layer_type="""basic"""		),
						    """resnet50""": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]	,hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,layer_type="""bottleneck"""		),
						    """resnet101""": ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3]	,hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,layer_type="""bottleneck"""		),
						    """resnet152""": ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3]	,hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,layer_type="""bottleneck"""		),
						}

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


if __name__ == "__main__":
		UpperCamelCase			    =     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.''',
		)

		UpperCamelCase			    =     parser.parse_args()
		UpperCamelCase			    =     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)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase			    =     {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''LayoutLMv3TokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     [
		    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv3ForQuestionAnswering''',
		    '''LayoutLMv3ForSequenceClassification''',
		    '''LayoutLMv3ForTokenClassification''',
		    '''LayoutLMv3Model''',
		    '''LayoutLMv3PreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     [
		    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFLayoutLMv3ForQuestionAnswering''',
		    '''TFLayoutLMv3ForSequenceClassification''',
		    '''TFLayoutLMv3ForTokenClassification''',
		    '''TFLayoutLMv3Model''',
		    '''TFLayoutLMv3PreTrainedModel''',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''LayoutLMv3FeatureExtractor''']
		UpperCamelCase			    =     ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		UpperCamelCase			    =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

