
"""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						=			logging.get_logger()






@dataclass
class 	_lowercase       :
	lowercase_				=     42
	lowercase_				=     field(default_factory=__UpperCAmelCase		)
	lowercase_				=     field(default_factory=__UpperCAmelCase		)




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			lowerCamelCase					:      str          =   len(list(m.modules() ) ) == 1 or isinstance(_A		,			nn.Convad ) or isinstance(_A		,			nn.BatchNormad )
			if has_not_submodules:
					self.traced.append(_A )




	def __call__(			self		,			UpperCAmelCase_ )		->							Optional[Any]:
			for m in self.module.modules():
					self.handles.append(m.register_forward_hook(self._forward_hook ) )
			self.module(_A )
			[x.remove() for x in self.handles]
			return self




	@property
	def       _UpperCamelCase   (			self )		->							List[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 	_lowercase       :
	lowercase_				=     42
	lowercase_				=     42
	lowercase_				=     0
	lowercase_				=     field(default_factory=__UpperCAmelCase		)
	lowercase_				=     field(default_factory=__UpperCAmelCase		)




	def __call__(			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Any          =   Tracker(self.dest )(_A ).parametrized
			lowerCamelCase					:      int          =   Tracker(self.src )(_A ).parametrized

			lowerCamelCase					:      List[Any]          =   list(filter(lambda UpperCAmelCase_ : type(_A ) not in self.src_skip		,			_A ) )
			lowerCamelCase					:      Any          =   list(filter(lambda UpperCAmelCase_ : type(_A ) not in self.dest_skip		,			_A ) )

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

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


def 		UpperCAmelCase (		a_,						a_,						a_,						a_ = True      ):





		'''simple docstring'''


		print(F"""Converting {name}..."""      )
		with torch.no_grad():
				lowerCamelCase					:      Dict          =   timm.create_model(UpperCAmelCase_,						pretrained=UpperCAmelCase_      ).eval()
				lowerCamelCase					:      List[Any]          =   ResNetForImageClassification(UpperCAmelCase_      ).eval()
				lowerCamelCase					:      int          =   ModuleTransfer(src=UpperCAmelCase_,						dest=UpperCAmelCase_      )
				lowerCamelCase					:      Optional[Any]          =   torch.randn((1, 3, 224, 224)      )
				module_transfer(UpperCAmelCase_      )

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

		lowerCamelCase					:      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
				lowerCamelCase					:      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 		UpperCAmelCase (		a_,						a_ = None,						a_ = True      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   'imagenet-1k-id2label.json'
		lowerCamelCase					:      Optional[Any]          =   1000
		lowerCamelCase					:      Any          =   (1, num_labels)

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

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

		lowerCamelCase					:      Optional[int]          =   partial(UpperCAmelCase_,						num_labels=UpperCAmelCase_,						idalabel=UpperCAmelCase_,						labelaid=UpperCAmelCase_      )

		lowerCamelCase					:      str          =   {
		    '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(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__":
					_A						=			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						=			parser.parse_args()
					_A						=			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 		UpperCAmelCase (		a_ = 10      ):





		'''simple docstring'''


		if not isinstance(a_,						a_      ) or n < 0:
				raise ValueError('Invalid input'      )
		lowerCamelCase					:      Union[str, Any]          =   10**n
		lowerCamelCase					:      int          =   2_8433 * (pow(2,						783_0457,						a_      )) + 1
		return str(number % modulus      )


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					print(F"""{solution(1_0) = }""")






