





"""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						: Tuple     	=			logging.get_logger()


@dataclass
class        __UpperCamelCase				:
							lowerCamelCase   :					nn.Module
							lowerCamelCase   :					List[nn.Module]      					=field(default_factory=a__						)
							lowerCamelCase   :					list      					=field(default_factory=a__						)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> List[str]:
								a      :		Dict	=				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      ,					lowerCAmelCase__      )	-> Optional[Any]:
								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     __a   (   self      )	-> str:
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda lowerCAmelCase__      : len(list(x.state_dict().keys()      )      ) > 0      ,					self.traced      )      )


@dataclass
class        __UpperCamelCase				:
							lowerCamelCase   :					nn.Module
							lowerCamelCase   :					nn.Module
							lowerCamelCase   :					int      					=0
							lowerCamelCase   :					List      					=field(default_factory=a__						)
							lowerCamelCase   :					List      					=field(default_factory=a__						)






							def __call__(   self      ,					lowerCAmelCase__      )	-> List[Any]:
								a      :		List[Any]	=				Tracker(self.dest      )(lowerCAmelCase__      ).parametrized
								a      :		Optional[Any]	=				Tracker(self.src      )(lowerCAmelCase__      ).parametrized

								a      :		Tuple	=				list(filter(lambda lowerCAmelCase__      : type(lowerCAmelCase__      ) not in self.src_skip      ,					lowerCAmelCase__      )      )
								a      :		int	=				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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    ResNetConfig    ,       _lowercase						:    Path    ,       _lowercase						:    bool = True					) ->Union[str, Any]:






	'''simple docstring'''






	print(F"""Converting {name}..."""					)
	with torch.no_grad():
		a      :		Dict	=				timm.create_model(_lowercase    ,       pretrained=_lowercase					).eval()
		a      :		Union[str, Any]	=				ResNetForImageClassification(_lowercase					).eval()
		a      :		str	=				ModuleTransfer(src=_lowercase    ,       dest=_lowercase					)
		a      :		List[Any]	=				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."

	a      :		List[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
		a      :		Union[str, Any]	=				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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Path    ,       _lowercase						:    str = None    ,       _lowercase						:    bool = True					) ->Dict:






	'''simple docstring'''






	a      :		List[str]	=				"imagenet-1k-id2label.json"
	a      :		Any	=				1000
	a      :		List[str]	=				(1, num_labels)

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

	a      :		Any	=				idalabel
	a      :		Tuple	=				{v: k for k, v in idalabel.items()}

	a      :		Optional[Any]	=				partial(_lowercase    ,       num_labels=_lowercase    ,       idalabel=_lowercase    ,       labelaid=_lowercase					)

	a      :		Dict	=				{
	    "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						: List[str]     	=			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						: str     	=			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"""






def   a_						(					lowerCamelCase       ):
				return str(lowerCamelCase       ) == str(lowerCamelCase       )[::-1]



def   a_						(					lowerCamelCase       ):
				return int(lowerCamelCase       ) + int(str(lowerCamelCase       )[::-1]       )



def   a_						(					lowerCamelCase = 1_0_0_0_0       ):
				UpperCAmelCase__             =							[]
				for num in range(1					,			lowerCamelCase       ):
								UpperCAmelCase__             =							0
								UpperCAmelCase__             =							num
								while iterations < 5_0:
												UpperCAmelCase__             =							sum_reverse(lowerCamelCase       )
												iterations += 1
												if is_palindrome(lowerCamelCase       ):
																break
								else:
												lychrel_nums.append(lowerCamelCase       )
				return len(lowerCamelCase       )


if __name__ == "__main__":
						print(F"""{solution() = }""")



