




"""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    snake_case     :
				SCREAMING_SNAKE_CASE_       :				nn.Module
				SCREAMING_SNAKE_CASE_       :				List[nn.Module]       				=  field(default_factory=SCREAMING_SNAKE_CASE__  )
				SCREAMING_SNAKE_CASE_       :				list       				=  field(default_factory=SCREAMING_SNAKE_CASE__  )

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    Tensor					,  UpperCamelCase__							:    Tensor)->       Dict:



						'''simple docstring'''


						__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							:    Union[str, Any]					,  UpperCamelCase__							:    Tensor)->       Dict:



						'''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 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


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





@dataclass
class    snake_case     :
				SCREAMING_SNAKE_CASE_       :				nn.Module
				SCREAMING_SNAKE_CASE_       :				nn.Module
				SCREAMING_SNAKE_CASE_       :				int       				=  0
				SCREAMING_SNAKE_CASE_       :				List       				=  field(default_factory=SCREAMING_SNAKE_CASE__  )
				SCREAMING_SNAKE_CASE_       :				List       				=  field(default_factory=SCREAMING_SNAKE_CASE__  )

				def __call__(     self							:    str					,  UpperCamelCase__							:    Tensor)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      Tracker(self.dest)(a_).parametrized
						__lowerCAmelCase:	Union[str, Any]   				=      Tracker(self.src)(a_).parametrized

						__lowerCAmelCase:	Union[str, Any]   				=      list(filter(lambda UpperCamelCase__: type(a_) not in self.src_skip					,  a_))
						__lowerCAmelCase:	Optional[int]   				=      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    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = True			)     ->     Dict:
		print(F"Converting {name}..."			)
		with torch.no_grad():
				__lowerCAmelCase:	str   				=      timm.create_model(_UpperCamelCase ,			pretrained=_UpperCamelCase			).eval()
				__lowerCAmelCase:	Any   				=      ResNetForImageClassification(_UpperCamelCase			).eval()
				__lowerCAmelCase:	Optional[int]   				=      ModuleTransfer(src=_UpperCamelCase ,			dest=_UpperCamelCase			)
				__lowerCAmelCase:	List[str]   				=      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."

		__lowerCAmelCase:	Optional[int]   				=      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:	List[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=_UpperCamelCase ,			)

				print(F"Pushed {checkpoint_name}"			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = None ,			__SCREAMING_SNAKE_CASE = True			)     ->     int:
		__lowerCAmelCase:	Union[str, Any]   				=      "imagenet-1k-id2label.json"
		__lowerCAmelCase:	Union[str, Any]   				=      1_0_0_0
		__lowerCAmelCase:	List[Any]   				=      (1, num_labels)

		__lowerCAmelCase:	Union[str, Any]   				=      "huggingface/label-files"
		__lowerCAmelCase:	Tuple   				=      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:	Union[str, Any]   				=      idalabel
		__lowerCAmelCase:	Dict   				=      {v: k for k, v in idalabel.items()}

		__lowerCAmelCase:	Any   				=      partial(_UpperCamelCase ,			num_labels=_UpperCamelCase ,			idalabel=_UpperCamelCase ,			labelaid=_UpperCamelCase			)

		__lowerCAmelCase:	Tuple   				=      {
		    "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)





"""simple docstring"""


import numpy as np






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     np.array:
		return (2 / (1 + np.exp(-2 * vector			))) - 1


if __name__ == "__main__":
			import doctest

			doctest.testmod()


