


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        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  nn.Module
					__SCREAMING_SNAKE_CASE       :  List[nn.Module]						=   field(default_factory=__magic_name__   )
					__SCREAMING_SNAKE_CASE       :  list						=   field(default_factory=__magic_name__   )

					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   Tensor							,					UpperCamelCase__       :   Tensor       ):






										'''simple docstring'''






										lowercase_  							=			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       :   List[str]							,					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       :   List[str]       ):






										'''simple docstring'''






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







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  nn.Module
					__SCREAMING_SNAKE_CASE       :  nn.Module
					__SCREAMING_SNAKE_CASE       :  int						=   0
					__SCREAMING_SNAKE_CASE       :  List						=   field(default_factory=__magic_name__   )
					__SCREAMING_SNAKE_CASE       :  List						=   field(default_factory=__magic_name__   )

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






										'''simple docstring'''






										lowercase_  							=			Tracker(self.dest       )(UpperCamelCase__       ).parametrized
										lowercase_  							=			Tracker(self.src       )(UpperCamelCase__       ).parametrized

										lowercase_  							=			list(filter(lambda UpperCamelCase__       : type(UpperCamelCase__       ) not in self.src_skip							,					UpperCamelCase__       )       )
										lowercase_  							=			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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__ = True  ):
					print(F'''Converting {name}...'''  )
					with torch.no_grad():
										lowercase_  							=			timm.create_model(UpperCAmelCase__						,							pretrained=UpperCAmelCase__  ).eval()
										lowercase_  							=			ResNetForImageClassification(UpperCAmelCase__  ).eval()
										lowercase_  							=			ModuleTransfer(src=UpperCAmelCase__						,							dest=UpperCAmelCase__  )
										lowercase_  							=			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."

					lowercase_  							=			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
										lowercase_  							=			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_				(      UpperCAmelCase__						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = True  ):
					lowercase_  							=			"""imagenet-1k-id2label.json"""
					lowercase_  							=			1_0_0_0
					lowercase_  							=			(1, num_labels)

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

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

					lowercase_  							=			partial(UpperCAmelCase__						,							num_labels=UpperCAmelCase__						,							idalabel=UpperCAmelCase__						,							labelaid=UpperCAmelCase__  )

					lowercase_  							=			{
					    """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__":
							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)






def 			UpperCAmelCase_				(      UpperCAmelCase__=2_8_1_2_3  ):
					lowercase_  							=			[1] * (limit + 1)

					for i in range(2						,							int(limit**0.5  ) + 1  ):
										sum_divs[i * i] += i
										for k in range(i + 1						,							limit // i + 1  ):
															sum_divs[k * i] += k + i

					lowercase_  							=			set()
					lowercase_  							=			0

					for n in range(1						,							limit + 1  ):
										if sum_divs[n] > n:
															abundants.add(UpperCAmelCase__  )

										if not any((n - a in abundants) for a in abundants  ):
															res += n

					return res


if __name__ == "__main__":
							print(solution())


