

"""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 	A_		:
							_UpperCAmelCase   :  Dict										=				42
							_UpperCAmelCase   :  Optional[Any]										=				field(default_factory=SCREAMING_SNAKE_CASE    )
							_UpperCAmelCase   :  Any										=				field(default_factory=SCREAMING_SNAKE_CASE    )
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, 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		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      List[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    lowerCAmelCase					(		self		:      List[str]):
								return list(filter(lambda SCREAMING_SNAKE_CASE__: len(list(x.state_dict().keys())) > 0       ,self.traced))






@dataclass
class 	A_		:
							_UpperCAmelCase   :  str										=				42
							_UpperCAmelCase   :  Union[str, Any]										=				42
							_UpperCAmelCase   :  Any										=				0
							_UpperCAmelCase   :  List[str]										=				field(default_factory=SCREAMING_SNAKE_CASE    )
							_UpperCAmelCase   :  str										=				field(default_factory=SCREAMING_SNAKE_CASE    )
							def __call__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    Dict 	=			Tracker(self.dest)(__a).parametrized
								__lowerCamelCase    :    Dict 	=			Tracker(self.src)(__a).parametrized

								__lowerCamelCase    :    int 	=			list(filter(lambda SCREAMING_SNAKE_CASE__: type(__a) not in self.src_skip       ,__a))
								__lowerCamelCase    :    str 	=			list(filter(lambda SCREAMING_SNAKE_CASE__: 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__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = True      )  ->					Any:
	print(F"Converting {name}..."      )
	with torch.no_grad():
		__lowerCamelCase    :    Union[str, Any] 	=			timm.create_model(__snake_case     ,			pretrained=__snake_case      ).eval()
		__lowerCamelCase    :    Optional[int] 	=			ResNetForImageClassification(__snake_case      ).eval()
		__lowerCamelCase    :    List[str] 	=			ModuleTransfer(src=__snake_case     ,			dest=__snake_case      )
		__lowerCamelCase    :    List[str] 	=			torch.randn((1, 3, 2_2_4, 2_2_4)      )
		module_transfer(__snake_case      )

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

	__lowerCamelCase    :    int 	=			F"resnet{'-'.join(name.split('resnet'      )      )}"
	print(__snake_case      )

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

		# we can use the convnext one
		__lowerCamelCase    :    List[str] 	=			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=__snake_case     ,			)

		print(F"Pushed {checkpoint_name}"      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = None     ,			lowerCamelCase__ = True      )  ->					Union[str, Any]:
	__lowerCamelCase    :    Dict 	=			'imagenet-1k-id2label.json'
	__lowerCamelCase    :    Tuple 	=			1_0_0_0
	__lowerCamelCase    :    int 	=			(1, num_labels)

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

	__lowerCamelCase    :    Union[str, Any] 	=			idalabel
	__lowerCamelCase    :    Any 	=			{v: k for k, v in idalabel.items()}

	__lowerCamelCase    :    Union[str, Any] 	=			partial(__snake_case     ,			num_labels=__snake_case     ,			idalabel=__snake_case     ,			labelaid=__snake_case      )

	__lowerCamelCase    :    Any 	=			{
	    '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(__snake_case     ,			names_to_config[model_name]     ,			__snake_case     ,			__snake_case      )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(__snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case      )
	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)







# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


a		=open  # noqa: we just need to have a builtin inside this module to test it properly




