

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()
SCREAMING_SNAKE_CASE_            = logging.get_logger()

@dataclass
class 			a					:
						_lowercase        =     4_2
						_lowercase        =     field(default_factory=UpperCAmelCase			)
						_lowercase        =     field(default_factory=UpperCAmelCase			)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      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  ,   A_		):







										'''simple docstring'''


										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		):







										'''simple docstring'''


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







@dataclass
class 			a					:
						_lowercase        =     4_2
						_lowercase        =     4_2
						_lowercase        =     0
						_lowercase        =     field(default_factory=UpperCAmelCase			)
						_lowercase        =     field(default_factory=UpperCAmelCase			)


						def __call__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      Tracker(self.dest		)(A_		).parametrized
										_UpperCAmelCase    :						List[Any]							=      Tracker(self.src		)(A_		).parametrized

										_UpperCAmelCase    :						Union[str, Any]							=      list(filter(lambda A_		: type(A_		) not in self.src_skip  ,   A_		)		)
										_UpperCAmelCase    :						Optional[Any]							=      list(filter(lambda A_		: 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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					ResNetConfig					, lowerCAmelCase:					Path					, lowerCAmelCase:					bool = True	)				->    int:
				print(F'Converting {name}...'	)
				with torch.no_grad():
								_UpperCAmelCase    :						Union[str, Any]							=      timm.create_model(lowerCAmelCase					, pretrained=lowerCAmelCase	).eval()
								_UpperCAmelCase    :						Optional[Any]							=      ResNetForImageClassification(lowerCAmelCase	).eval()
								_UpperCAmelCase    :						Optional[int]							=      ModuleTransfer(src=lowerCAmelCase					, dest=lowerCAmelCase	)
								_UpperCAmelCase    :						Tuple							=      torch.randn((1, 3, 224, 224)	)
								module_transfer(lowerCAmelCase	)

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

				_UpperCAmelCase    :						Dict							=      F'resnet{"-".join(name.split("resnet"	)	)}'
				print(lowerCAmelCase	)

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

								# we can use the convnext one
								_UpperCAmelCase    :						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=lowerCAmelCase					, )

								print(F'Pushed {checkpoint_name}'	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Path					, lowerCAmelCase:					str = None					, lowerCAmelCase:					bool = True	)				->    Optional[Any]:
				_UpperCAmelCase    :						List[Any]							=      "imagenet-1k-id2label.json"
				_UpperCAmelCase    :						Tuple							=      1000
				_UpperCAmelCase    :						str							=      (1, num_labels)

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

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

				_UpperCAmelCase    :						List[str]							=      partial(lowerCAmelCase					, num_labels=lowerCAmelCase					, idalabel=lowerCAmelCase					, labelaid=lowerCAmelCase	)

				_UpperCAmelCase    :						Union[str, Any]							=      {
				    "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(lowerCAmelCase					, names_to_config[model_name]					, lowerCAmelCase					, lowerCAmelCase	)
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)
				return config, expected_shape


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_            = 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.',
			)

			SCREAMING_SNAKE_CASE_            = parser.parse_args()
			SCREAMING_SNAKE_CASE_            = 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)





from __future__ import annotations

from random import choice

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[int]:
				return choice(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      random_pivot(lowerCAmelCase	)

				# partition based on pivot
				# linear time
				_UpperCAmelCase    :						List[str]							=      [e for e in lst if e < pivot]
				_UpperCAmelCase    :						Any							=      [e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(lowerCAmelCase	) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(lowerCAmelCase	) < k - 1:
								return kth_number(lowerCAmelCase					, k - len(lowerCAmelCase	) - 1	)
				# pivot is in elements smaller than k
				else:
								return kth_number(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


