


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				:       int      =  logging.get_logger()






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					List[nn.Module]    					=						field(default_factory=_a )
					snake_case_		:					list    					=						field(default_factory=_a )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tensor  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		len(list(m.modules()    )    ) == 1 or isinstance(SCREAMING_SNAKE_CASE  ,     nn.Convad    ) or isinstance(SCREAMING_SNAKE_CASE  ,     nn.BatchNormad    )
											if has_not_submodules:
																	self.traced.append(SCREAMING_SNAKE_CASE    )


					def __call__(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						List[str]:




											'''simple docstring'''

											for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook    )    )
											self.module(SCREAMING_SNAKE_CASE    )
											[x.remove() for x in self.handles]
											return self






					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

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






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					nn.Module
					snake_case_		:					int    					=						0
					snake_case_		:					List    					=						field(default_factory=_a )
					snake_case_		:					List    					=						field(default_factory=_a )


					def __call__(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		Tracker(self.dest    )(SCREAMING_SNAKE_CASE    ).parametrized
											__snake_case				=		Tracker(self.src    )(SCREAMING_SNAKE_CASE    ).parametrized

											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.src_skip  ,     SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.dest_skip  ,     SCREAMING_SNAKE_CASE    )    )

											if len(SCREAMING_SNAKE_CASE    ) != len(SCREAMING_SNAKE_CASE    ):
																	raise Exception(
																	    f'''Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE    )} operations while'''
																	    f''' destination module has {len(SCREAMING_SNAKE_CASE    )}.'''    )

											for dest_m, src_m in zip(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	dest_m.load_state_dict(src_m.state_dict()    )
																	if self.verbose == 1:
																							print(f'''Transfered from={src_m} to={dest_m}'''    )





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						print(F'''Converting {name}...'''			)
						with torch.no_grad():
												__snake_case				=		timm.create_model(_lowerCAmelCase   ,						pretrained=_lowerCAmelCase			).eval()
												__snake_case				=		ResNetForImageClassification(_lowerCAmelCase			).eval()
												__snake_case				=		ModuleTransfer(src=_lowerCAmelCase   ,						dest=_lowerCAmelCase			)
												__snake_case				=		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."

						__snake_case				=		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
												__snake_case				=		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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase = None   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		"imagenet-1k-id2label.json"
						__snake_case				=		1000
						__snake_case				=		(1, num_labels)

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

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

						__snake_case				=		partial(_lowerCAmelCase   ,						num_labels=_lowerCAmelCase   ,						idalabel=_lowerCAmelCase   ,						labelaid=_lowerCAmelCase			)

						__snake_case				=		{
						    "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__":
	A				:       Optional[Any]      =  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				:       Union[str, Any]      =  parser.parse_args()
	A				:       Path      =  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 datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching





def 						_lowerCAmelCase							(     )    ->						Tuple:


						'''simple docstring'''






						import os as original_os
						from os import path as original_path
						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join

						__snake_case				=		"__test_patch_submodule_mock__"
						with patch_submodule(_test_patching   ,						"os.path.join"   ,						_lowerCAmelCase			):
												# Every way to access os.path.join must be patched, and the rest must stay untouched

												# check os.path.join
												assert isinstance(_test_patching.os   ,						_PatchedModuleObj			)
												assert isinstance(_test_patching.os.path   ,						_PatchedModuleObj			)
												assert _test_patching.os.path.join is mock

												# check path.join
												assert isinstance(_test_patching.path   ,						_PatchedModuleObj			)
												assert _test_patching.path.join is mock

												# check join
												assert _test_patching.join is mock

												# check that the other attributes are untouched
												assert _test_patching.os.rename is original_rename
												assert _test_patching.path.dirname is original_dirname
												assert _test_patching.os.path.dirname is original_dirname

												# Even renamed modules or objects must be patched

												# check renamed_os.path.join
												assert isinstance(_test_patching.renamed_os   ,						_PatchedModuleObj			)
												assert isinstance(_test_patching.renamed_os.path   ,						_PatchedModuleObj			)
												assert _test_patching.renamed_os.path.join is mock

												# check renamed_path.join
												assert isinstance(_test_patching.renamed_path   ,						_PatchedModuleObj			)
												assert _test_patching.renamed_path.join is mock

												# check renamed_join
												assert _test_patching.renamed_join is mock

												# check that the other attributes are untouched
												assert _test_patching.renamed_os.rename is original_rename
												assert _test_patching.renamed_path.dirname is original_dirname
												assert _test_patching.renamed_os.path.dirname is original_dirname

						# check that everthing is back to normal when the patch is over

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join





def 						_lowerCAmelCase							(     )    ->						int:


						'''simple docstring'''






						assert _test_patching.open is open

						__snake_case				=		"__test_patch_submodule_builtin_mock__"
						# _test_patching has "open" in its globals
						assert _test_patching.open is open
						with patch_submodule(_test_patching   ,						"open"   ,						_lowerCAmelCase			):
												assert _test_patching.open is mock

						# check that everthing is back to normal when the patch is over

						assert _test_patching.open is open





def 						_lowerCAmelCase							(     )    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_missing_mock__"
						with patch_submodule(_test_patching   ,						"pandas.read_csv"   ,						_lowerCAmelCase			):
												pass





def 						_lowerCAmelCase							(     )    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_missing_builtin_mock__"
						# _test_patching doesn't have "len" in its globals
						assert getattr(_test_patching   ,						"len"   ,						_lowerCAmelCase			) is None
						with patch_submodule(_test_patching   ,						"len"   ,						_lowerCAmelCase			):
												assert _test_patching.len is mock
						assert _test_patching.len is len





def 						_lowerCAmelCase							(     )    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_start_and_stop_mock__"
						__snake_case				=		patch_submodule(_test_patching   ,						"open"   ,						_lowerCAmelCase			)
						assert _test_patching.open is open
						patch.start()
						assert _test_patching.open is mock
						patch.stop()
						assert _test_patching.open is open





def 						_lowerCAmelCase							(     )    ->						Any:


						'''simple docstring'''






						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						__snake_case				=		"__test_patch_submodule_successive_join__"
						__snake_case				=		"__test_patch_submodule_successive_dirname__"
						__snake_case				=		"__test_patch_submodule_successive_rename__"
						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename

						with patch_submodule(_test_patching   ,						"os.path.join"   ,						_lowerCAmelCase			):
												with patch_submodule(_test_patching   ,						"os.rename"   ,						_lowerCAmelCase			):
																		with patch_submodule(_test_patching   ,						"os.path.dirname"   ,						_lowerCAmelCase			):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

    # try another order
						with patch_submodule(_test_patching   ,						"os.rename"   ,						_lowerCAmelCase			):
												with patch_submodule(_test_patching   ,						"os.path.join"   ,						_lowerCAmelCase			):
																		with patch_submodule(_test_patching   ,						"os.path.dirname"   ,						_lowerCAmelCase			):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename





def 						_lowerCAmelCase							(     )    ->						str:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_doesnt_exist_mock__"
						with patch_submodule(_test_patching   ,						"__module_that_doesn_exist__.__attribute_that_doesn_exist__"   ,						_lowerCAmelCase			):
												pass
						with patch_submodule(_test_patching   ,						"os.__attribute_that_doesn_exist__"   ,						_lowerCAmelCase			):
												pass





