'''simple docstring'''







class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       int							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       List[Any]=None							,							UpperCamelCase_ :       Any=None      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Tuple						    = data
													SCREAMING_SNAKE_CASE__     :List[str]						    = previous
													SCREAMING_SNAKE_CASE__     :List[str]						    = next_node




						def __str__(    self :       str      )     ->				str:
													return f'''{self.data}'''




						def      __lowerCamelCase       (    self :       List[str]      )     ->				int:
													return self.data




						def      __lowerCamelCase       (    self :       int      )     ->				Any:
													return self.next




						def      __lowerCamelCase       (    self :       Any      )     ->				Any:
													return self.previous




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       Any							,							UpperCamelCase_ :       List[Any]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = head




						def __iter__(    self :       int      )     ->				Optional[Any]:
													return self




						def      __lowerCamelCase       (    self :       str      )     ->				Any:
													if not self.current:
																				raise StopIteration
													else:
																				SCREAMING_SNAKE_CASE__     :Dict						    = self.current.get_data()
																				SCREAMING_SNAKE_CASE__     :int						    = self.current.get_next()
																				return value




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       Dict      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :str						    = None  # First node in list
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = None  # Last node in list




						def __str__(    self :       Optional[int]      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :str						    = self.head
													SCREAMING_SNAKE_CASE__     :int						    = []
													while current is not None:
																				nodes.append(current.get_data()      )
																				SCREAMING_SNAKE_CASE__     :Any						    = current.get_next()
													return " ".join(str(UpperCamelCase_      ) for node in nodes      )




						def __contains__(    self :       Tuple							,							UpperCamelCase_ :       int      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.head
													while current:
																				if current.get_data() == value:
																											return True
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = current.get_next()
													return False




						def __iter__(    self :       Optional[Any]      )     ->				int:
													return LinkedListIterator(self.head      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Optional[int]:
													if self.head:
																				return self.head.get_data()
													return None




						def      __lowerCamelCase       (    self :       Dict      )     ->				Optional[Any]:
													if self.tail:
																				return self.tail.get_data()
													return None




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       Node      )     ->				None:
													if self.head is None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = node
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = node
													else:
																				self.insert_before_node(self.head							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Node      )     ->				None:
													if self.head is None:
																				self.set_head(UpperCamelCase_      )
													else:
																				self.insert_after_node(self.tail							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       int      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Tuple						    = Node(UpperCamelCase_      )
													if self.head is None:
																				self.set_head(UpperCamelCase_      )
													else:
																				self.set_tail(UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Node							,							UpperCamelCase_ :       Node      )     ->				None:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = node
													SCREAMING_SNAKE_CASE__     :Any						    = node.previous

													if node.get_previous() is None:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = node_to_insert
													else:
																				SCREAMING_SNAKE_CASE__     :Dict						    = node_to_insert

													SCREAMING_SNAKE_CASE__     :List[Any]						    = node_to_insert




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Node							,							UpperCamelCase_ :       Node      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Dict						    = node
													SCREAMING_SNAKE_CASE__     :List[str]						    = node.next

													if node.get_next() is None:
																				SCREAMING_SNAKE_CASE__     :Any						    = node_to_insert
													else:
																				SCREAMING_SNAKE_CASE__     :Dict						    = node_to_insert

													SCREAMING_SNAKE_CASE__     :Tuple						    = node_to_insert




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       int      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Tuple						    = 1
													SCREAMING_SNAKE_CASE__     :Tuple						    = Node(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = self.head
													while node:
																				if current_position == position:
																											self.insert_before_node(UpperCamelCase_							,							UpperCamelCase_      )
																											return
																				current_position += 1
																				SCREAMING_SNAKE_CASE__     :List[str]						    = node.next
													self.insert_after_node(self.tail							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       int      )     ->				Node:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.head
													while node:
																				if node.get_data() == item:
																											return node
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = node.get_next()
													raise Exception('Node not found'      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       List[str]      )     ->				Any:
													if (node := self.get_node(UpperCamelCase_      )) is not None:
																				if node == self.head:
																											SCREAMING_SNAKE_CASE__     :str						    = self.head.get_next()

																				if node == self.tail:
																											SCREAMING_SNAKE_CASE__     :int						    = self.tail.get_previous()

																				self.remove_node_pointers(UpperCamelCase_      )




						@staticmethod
						def      __lowerCamelCase       (    UpperCamelCase_ :       Node      )     ->				None:
													if node.get_next():
																				SCREAMING_SNAKE_CASE__     :Any						    = node.previous

													if node.get_previous():
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = node.next

													SCREAMING_SNAKE_CASE__     :Tuple						    = None
													SCREAMING_SNAKE_CASE__     :List[str]						    = None




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[str]:
													return self.head is None


def    lowerCamelCase       (			)			->	None:


							'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()







'''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()
UpperCamelCase_      	=	logging.get_logger()




@dataclass
class _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							List[nn.Module]     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							list     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Tensor							,							UpperCamelCase_ :       Tensor      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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 :       Tuple							,							UpperCamelCase_ :       Tensor      )     ->				int:
													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      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													# check the len of the state_dict keys to see if we have learnable params
													return list(filter(lambda UpperCamelCase_      : len(list(x.state_dict().keys()      )      ) > 0							,							self.traced      )      )




@dataclass
class _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							nn.Module
						A_			:							int     		= 0
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def __call__(    self :       List[Any]							,							UpperCamelCase_ :       Tensor      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[str]						    = Tracker(self.dest      )(UpperCamelCase_      ).parametrized
													SCREAMING_SNAKE_CASE__     :Any						    = Tracker(self.src      )(UpperCamelCase_      ).parametrized

													SCREAMING_SNAKE_CASE__     :Dict						    = list(filter(lambda UpperCamelCase_      : type(UpperCamelCase_      ) not in self.src_skip							,							UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :str						    = 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       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  ResNetConfig				,						UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  bool = True		)			->	Union[str, Any]:


							'''simple docstring'''






							print(F'''Converting {name}...'''		)
							with torch.no_grad():
														SCREAMING_SNAKE_CASE__     :str						    = timm.create_model(UpperCAmelCase__				,						pretrained=UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :List[str]						    = ResNetForImageClassification(UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :Dict						    = ModuleTransfer(src=UpperCAmelCase__				,						dest=UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.randn((1, 3, 2_2_4, 2_2_4)		)
														module_transfer(UpperCAmelCase__		)

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

							SCREAMING_SNAKE_CASE__     :List[str]						    = F'''resnet{"-".join(name.split("resnet"		)		)}'''
							print(UpperCAmelCase__		)

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

														# we can use the convnext one
														SCREAMING_SNAKE_CASE__     :int						    = 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=UpperCAmelCase__				,						)

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


def    lowerCamelCase       (			UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  str = None				,						UpperCAmelCase__				:  bool = True		)			->	int:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = 'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE__     :Any						    = 1_0_0_0
							SCREAMING_SNAKE_CASE__     :List[Any]						    = (1, num_labels)

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

							SCREAMING_SNAKE_CASE__     :str						    = idalabel
							SCREAMING_SNAKE_CASE__     :Any						    = {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = partial(UpperCAmelCase__				,						num_labels=UpperCAmelCase__				,						idalabel=UpperCAmelCase__				,						labelaid=UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :List[str]						    = {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 2_3, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 3_6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							}

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






