





'''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()
A   : int             =			logging.get_logger()







@dataclass
class 			lowerCamelCase					:
					_SCREAMING_SNAKE_CASE							   =    42
					_SCREAMING_SNAKE_CASE							   =    field(default_factory=__UpperCAmelCase  )
					_SCREAMING_SNAKE_CASE							   =    field(default_factory=__UpperCAmelCase  )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  Any       ,     __snake_case	:  Tensor       ,     __snake_case	:  Tensor       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		len(list(m.modules()       )       ) == 1 or isinstance(__snake_case       ,     nn.Convad       ) or isinstance(__snake_case       ,     nn.BatchNormad       )
											if has_not_submodules:
																	self.traced.append(__snake_case       )


					def __call__(					self	:  str       ,     __snake_case	:  Tensor       ):





											'''simple docstring'''





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




					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





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







@dataclass
class 			lowerCamelCase					:
					_SCREAMING_SNAKE_CASE							   =    42
					_SCREAMING_SNAKE_CASE							   =    42
					_SCREAMING_SNAKE_CASE							   =    0
					_SCREAMING_SNAKE_CASE							   =    field(default_factory=__UpperCAmelCase  )
					_SCREAMING_SNAKE_CASE							   =    field(default_factory=__UpperCAmelCase  )


					def __call__(					self	:  int       ,     __snake_case	:  Tensor       ):





											'''simple docstring'''





											_snake_case:   Any  					=		Tracker(self.dest       )(__snake_case       ).parametrized
											_snake_case:   Optional[Any]  					=		Tracker(self.src       )(__snake_case       ).parametrized

											_snake_case:   str  					=		list(filter(lambda __snake_case       : type(__snake_case       ) not in self.src_skip       ,     __snake_case       )       )
											_snake_case:   List[Any]  					=		list(filter(lambda __snake_case       : type(__snake_case       ) not in self.dest_skip       ,     __snake_case       )       )

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

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





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__ = True						)   ->List[Any]:
						print(F'''Converting {name}...'''						)
						with torch.no_grad():
												_snake_case:   Optional[Any]  					=		timm.create_model(lowercase__				,  pretrained=lowercase__						).eval()
												_snake_case:   Dict  					=		ResNetForImageClassification(lowercase__						).eval()
												_snake_case:   str  					=		ModuleTransfer(src=lowercase__				,  dest=lowercase__						)
												_snake_case:   str  					=		torch.randn((1, 3, 224, 224)						)
												module_transfer(lowercase__						)

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

						_snake_case:   Any  					=		F'''resnet{"-".join(name.split("resnet"						)						)}'''
						print(lowercase__						)

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

												# we can use the convnext one
												_snake_case:   List[str]  					=		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=lowercase__				,  )

												print(F'''Pushed {checkpoint_name}'''						)





def       lowercase_      (		lowercase__				,  lowercase__ = None				,  lowercase__ = True						)   ->Union[str, Any]:
						_snake_case:   str  					=		'imagenet-1k-id2label.json'
						_snake_case:   str  					=		1000
						_snake_case:   Optional[int]  					=		(1, num_labels)

						_snake_case:   List[Any]  					=		'huggingface/label-files'
						_snake_case:   Tuple  					=		num_labels
						_snake_case:   Any  					=		json.load(open(hf_hub_download(lowercase__				,  lowercase__				,  repo_type='dataset'						)				,  'r'						)						)
						_snake_case:   List[str]  					=		{int(lowercase__						): v for k, v in idalabel.items()}

						_snake_case:   Any  					=		idalabel
						_snake_case:   Optional[Any]  					=		{v: k for k, v in idalabel.items()}

						_snake_case:   Optional[Any]  					=		partial(lowercase__				,  num_labels=lowercase__				,  idalabel=lowercase__				,  labelaid=lowercase__						)

						_snake_case:   Tuple  					=		{
						    '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, 1024, 2048]				,  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, 1024, 2048]				,  layer_type='bottleneck'						),
						    'resnet101': ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3]				,  hidden_sizes=[256, 512, 1024, 2048]				,  layer_type='bottleneck'						),
						    'resnet152': ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3]				,  hidden_sizes=[256, 512, 1024, 2048]				,  layer_type='bottleneck'						),
						}

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


if __name__ == "__main__":
		A   : Optional[int]             =			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.',
		)

		A   : Optional[int]             =			parser.parse_args()
		A   : Path             =			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)












'''simple docstring'''






import torch





def       lowercase_      (		)   ->Dict:
						if torch.cuda.is_available():
												_snake_case:   Any  					=		torch.cuda.device_count()
						else:
												_snake_case:   Optional[int]  					=		0
						print(F'''Successfully ran on {num_gpus} GPUs'''						)


if __name__ == "__main__":
		main()





