



"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__a      =				logging.get_logger(__name__)

__a      =				OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

__a      =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def    A_  (			_lowercase	):







						'''simple docstring'''


						for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
												if class_name in extractors:
																		snake_case_ :Optional[int]						=			model_type_to_module_name(_lowercase	)

																		snake_case_ :Tuple						=			importlib.import_module(f""".{module_name}""",					"""transformers.models"""	)
																		try:
																								return getattr(_lowercase,					_lowercase	)
																		except AttributeError:
																								continue

						for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
												if getattr(_lowercase,					"""__name__""",					_lowercase	) == class_name:
																		return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
						snake_case_ :Optional[int]						=			importlib.import_module("""transformers"""	)
						if hasattr(_lowercase,					_lowercase	):
												return getattr(_lowercase,					_lowercase	)

						return None
def    A_  (			_lowercase,					_lowercase = None,					_lowercase = False,					_lowercase = False,					_lowercase = None,					_lowercase = None,					_lowercase = None,					_lowercase = False,					**_lowercase,					):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			get_file_from_repo(
						    _lowercase,					_lowercase,					cache_dir=_lowercase,					force_download=_lowercase,					resume_download=_lowercase,					proxies=_lowercase,					use_auth_token=_lowercase,					revision=_lowercase,					local_files_only=_lowercase,					)
						if resolved_config_file is None:
												logger.info(
												    """Could not locate the feature extractor configuration file, will try to use the model config instead."""	)
												return {}

						with open(_lowercase,					encoding="""utf-8"""	) as reader:
												return json.load(_lowercase	)

class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							List[str]						)   ->	Tuple:
											raise EnvironmentError(
											    """AutoFeatureExtractor is designed to be instantiated """
											    """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""						)






					@classmethod
					@replace_list_option_in_docstrings(snake_case						)
					def    lowerCAmelCase_				(       cls:							List[Any]						,     snake_case:							List[Any]						,     **snake_case:							Any						)   ->	List[Any]:
											snake_case_ :Optional[int]						=			kwargs.pop("""config"""						,     snake_case						)
											snake_case_ :Optional[int]						=			kwargs.pop("""trust_remote_code"""						,     snake_case						)
											snake_case_ :str						=			True

											snake_case_,       snake_case_ :int						=			FeatureExtractionMixin.get_feature_extractor_dict(snake_case						,     **snake_case						)
											snake_case_ :List[str]						=			config_dict.get("""feature_extractor_type"""						,     snake_case						)
											snake_case_ :List[Any]						=			None
											if "AutoFeatureExtractor" in config_dict.get("""auto_map"""						,     {}						):
																	snake_case_ :str						=			config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

											# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
											if feature_extractor_class is None and feature_extractor_auto_map is None:
																	if not isinstance(snake_case						,     snake_case						):
																							snake_case_ :Union[str, Any]						=			AutoConfig.from_pretrained(snake_case						,     **snake_case						)
																	# It could be in `config.feature_extractor_type``
																	snake_case_ :Dict						=			getattr(snake_case						,     """feature_extractor_type"""						,     snake_case						)
																	if hasattr(snake_case						,     """auto_map"""						) and "AutoFeatureExtractor" in config.auto_map:
																							snake_case_ :str						=			config.auto_map["""AutoFeatureExtractor"""]

											if feature_extractor_class is not None:
																	snake_case_ :str						=			feature_extractor_class_from_name(snake_case						)

											snake_case_ :Any						=			feature_extractor_auto_map is not None
											snake_case_ :int						=			feature_extractor_class is not None or type(snake_case						) in FEATURE_EXTRACTOR_MAPPING
											snake_case_ :Optional[int]						=			resolve_trust_remote_code(
											    snake_case						,     snake_case						,     snake_case						,     snake_case						)

											if has_remote_code and trust_remote_code:
																	snake_case_ :str						=			get_class_from_dynamic_module(
																	    snake_case						,     snake_case						,     **snake_case						)
																	snake_case_ :Union[str, Any]						=			kwargs.pop("""code_revision"""						,     snake_case						)
																	if os.path.isdir(snake_case						):
																							feature_extractor_class.register_for_auto_class()
																	return feature_extractor_class.from_dict(snake_case						,     **snake_case						)
											elif feature_extractor_class is not None:
																	return feature_extractor_class.from_dict(snake_case						,     **snake_case						)
											# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
											elif type(snake_case						) in FEATURE_EXTRACTOR_MAPPING:
																	snake_case_ :Optional[int]						=			FEATURE_EXTRACTOR_MAPPING[type(snake_case						)]
																	return feature_extractor_class.from_dict(snake_case						,     **snake_case						)

											raise ValueError(
											    f"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
											    f"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
											    f"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()						)}"""						)






					@staticmethod
					def    lowerCAmelCase_				(       snake_case:							List[Any]						,     snake_case:							int						)   ->	Optional[Any]:
											FEATURE_EXTRACTOR_MAPPING.register(snake_case						,     snake_case						)



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()
_UpperCAmelCase		:   List[Any]										=       logging.get_logger()







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: List[nn.Module]       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: list       			=			field(default_factory=lowercase_			)




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    len(list(m.modules()       )       ) == 1 or isinstance(snake_case      ,   nn.Convad       ) or isinstance(snake_case      ,   nn.BatchNormad       )
											if has_not_submodules:
																		self.traced.append(snake_case       )




				def __call__(					self      ,   snake_case       ):
											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook       )       )
											self.module(snake_case       )
											[x.remove() for x in self.handles]
											return self




				@property
				def     a							(					self       ):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case       : len(list(x.state_dict().keys()       )       ) > 0      ,   self.traced       )       )







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




				def __call__(					self      ,   snake_case       ):
											snake_case_							=    Tracker(self.dest       )(snake_case       ).parametrized
											snake_case_							=    Tracker(self.src       )(snake_case       ).parametrized

											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.src_skip      ,   snake_case       )       )
											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.dest_skip      ,   snake_case       )       )

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

											for dest_m, src_m in zip(snake_case      ,   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	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							print(F'''Converting {name}...'''      )
							with torch.no_grad():
														snake_case_							=    timm.create_model(UpperCamelCase__   ,							pretrained=UpperCamelCase__      ).eval()
														snake_case_							=    ResNetForImageClassification(UpperCamelCase__      ).eval()
														snake_case_							=    ModuleTransfer(src=UpperCamelCase__   ,							dest=UpperCamelCase__      )
														snake_case_							=    torch.randn((1, 3, 224, 224)      )
														module_transfer(UpperCamelCase__      )

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

							snake_case_							=    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
														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=UpperCamelCase__   ,							)

														print(F'''Pushed {checkpoint_name}'''      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = None   ,							UpperCamelCase__ = True      ):







							'''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(UpperCamelCase__   ,							UpperCamelCase__   ,							repo_type='dataset'      )   ,							'r'      )      )
							snake_case_							=    {int(UpperCamelCase__      ): v for k, v in idalabel.items()}

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

							snake_case_							=    partial(UpperCamelCase__   ,							num_labels=UpperCamelCase__   ,							idalabel=UpperCamelCase__   ,							labelaid=UpperCamelCase__      )

							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(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__":
					_UpperCAmelCase		:   List[str]										=       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.""",
					)

					_UpperCAmelCase		:   Optional[Any]										=       parser.parse_args()
					_UpperCAmelCase		:   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)




