




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_						=logging.get_logger()


@dataclass
class 							__UpperCamelCase			:





				'''simple docstring'''



				__a							: nn.Module
				__a							: List[nn.Module]										=field(default_factory=__UpperCAmelCase  )
				__a							: list										=field(default_factory=__UpperCAmelCase  )



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



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






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




@dataclass
class 							__UpperCamelCase			:





				'''simple docstring'''



				__a							: nn.Module
				__a							: nn.Module
				__a							: int										=0
				__a							: List										=field(default_factory=__UpperCAmelCase  )
				__a							: List										=field(default_factory=__UpperCAmelCase  )



				def __call__( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		Tracker(self.dest						)(UpperCAmelCase_						).parametrized
											lowerCAmelCase   =		Tracker(self.src						)(UpperCAmelCase_						).parametrized

											lowerCAmelCase   =		list(filter(lambda UpperCAmelCase_						: type(UpperCAmelCase_						) not in self.src_skip       , UpperCAmelCase_						)						)
											lowerCAmelCase   =		list(filter(lambda UpperCAmelCase_						: type(UpperCAmelCase_						) not in self.dest_skip       , UpperCAmelCase_						)						)

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

											for dest_m, src_m in zip(UpperCAmelCase_       , UpperCAmelCase_						):
																		dest_m.load_state_dict(src_m.state_dict()						)
																		if self.verbose == 1:
																									print(F"""Transfered from={src_m} to={dest_m}"""						)




def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case = True							):
							print(F"""Converting {name}..."""							)
							with torch.no_grad():
														lowerCAmelCase   =		timm.create_model(_snake_case						, pretrained=_snake_case							).eval()
														lowerCAmelCase   =		ResNetForImageClassification(_snake_case							).eval()
														lowerCAmelCase   =		ModuleTransfer(src=_snake_case						, dest=_snake_case							)
														lowerCAmelCase   =		torch.randn((1, 3, 224, 224)							)
														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   =		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   =		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       UpperCAmelCase       (     _snake_case						, _snake_case = None						, _snake_case = True							):
							lowerCAmelCase   =		'''imagenet-1k-id2label.json'''
							lowerCAmelCase   =		1000
							lowerCAmelCase   =		(1, num_labels)

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

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

							lowerCAmelCase   =		partial(_snake_case						, num_labels=_snake_case						, idalabel=_snake_case						, labelaid=_snake_case							)

							lowerCAmelCase   =		{
							    '''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(_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__":
		UpperCAmelCase_						=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_						=parser.parse_args()
		UpperCAmelCase_						=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 collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class 							__UpperCamelCase			( yaml.SafeLoader  ):





				'''simple docstring'''



				def       __snake_case		( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		[self.constructed_objects[key_node] for key_node, _ in node.value]
											lowerCAmelCase   =		[tuple(UpperCAmelCase_						) if isinstance(UpperCAmelCase_       , UpperCAmelCase_						) else key for key in keys]
											lowerCAmelCase   =		Counter(UpperCAmelCase_						)
											lowerCAmelCase   =		[key for key in counter if counter[key] > 1]
											if duplicate_keys:
																		raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""						)






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=False						):
											lowerCAmelCase   =		super().construct_mapping(UpperCAmelCase_       , deep=UpperCAmelCase_						)
											self._check_no_duplicates_on_constructed_node(UpperCAmelCase_						)
											return mapping




def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		list(readme_content.splitlines()							)
							if full_content and full_content[0] == "---" and "---" in full_content[1:]:
														lowerCAmelCase   =		full_content[1:].index('''---'''							) + 1
														lowerCAmelCase   =		'''\n'''.join(full_content[1:sep_idx]							)
														return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

							return None, "\n".join(_snake_case							)


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										={"""train_eval_index"""}  # train-eval-index in the YAML metadata



				@classmethod
				def       __snake_case		( cls       , UpperCAmelCase_						):
											with open(UpperCAmelCase_       , encoding='''utf-8'''						) as readme_file:
																		lowerCAmelCase   ,     lowerCAmelCase   =		_split_yaml_from_readme(readme_file.read()						)
											if yaml_string is not None:
																		return cls.from_yaml_string(UpperCAmelCase_						)
											else:
																		return cls()



				def       __snake_case		( self       , UpperCAmelCase_						):
											if path.exists():
																		with open(UpperCAmelCase_       , encoding='''utf-8'''						) as readme_file:
																									lowerCAmelCase   =		readme_file.read()
											else:
																		lowerCAmelCase   =		None
											lowerCAmelCase   =		self._to_readme(UpperCAmelCase_						)
											with open(UpperCAmelCase_       , '''w'''       , encoding='''utf-8'''						) as readme_file:
																		readme_file.write(UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_ = None						):
											if readme_content is not None:
																		lowerCAmelCase   ,     lowerCAmelCase   =		_split_yaml_from_readme(UpperCAmelCase_						)
																		lowerCAmelCase   =		'''---\n''' + self.to_yaml_string() + '''---\n''' + content
											else:
																		lowerCAmelCase   =		'''---\n''' + self.to_yaml_string() + '''---\n'''
											return full_content



				@classmethod
				def       __snake_case		( cls       , UpperCAmelCase_						):
											lowerCAmelCase   =		yaml.load(UpperCAmelCase_       , Loader=_NoDuplicateSafeLoader						) or {}

											# Convert the YAML keys to DatasetMetadata fields
											lowerCAmelCase   =		{
											    (key.replace('''-'''       , '''_'''						) if key.replace('''-'''       , '''_'''						) in cls._FIELDS_WITH_DASHES else key): value
											    for key, value in metadata_dict.items()
											}
											return cls(**UpperCAmelCase_						)






				def       __snake_case		( self						):
											return yaml.safe_dump(
											    {
											        (key.replace('''_'''       , '''-'''						) if key in self._FIELDS_WITH_DASHES else key): value
											        for key, value in self.items()
											    }       , sort_keys=UpperCAmelCase_       , allow_unicode=UpperCAmelCase_       , encoding='''utf-8'''       , ).decode('''utf-8'''						)




UpperCAmelCase_						={
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
		from argparse import ArgumentParser

		UpperCAmelCase_						=ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
		ap.add_argument("""readme_filepath""")
		UpperCAmelCase_						=ap.parse_args()

		UpperCAmelCase_						=Path(args.readme_filepath)
		UpperCAmelCase_						=DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)



