


'''simple docstring'''



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class 							__UpperCamelCase				(		yaml.SafeLoader					):




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       int					    =  [self.constructed_objects[key_node] for key_node, _ in node.value]
						__a			:       Optional[int]					    =  [tuple(__A							) if isinstance(__A						,				__A							) else key for key in keys]
						__a			:       Optional[int]					    =  Counter(__A							)
						__a			:       Union[str, Any]					    =  [key for key in counter if counter[key] > 1]
						if duplicate_keys:
											raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a=False							):

						'''simple docstring'''





						__a			:       Any					    =  super().construct_mapping(__A						,				deep=__A							)
						self._check_no_duplicates_on_constructed_node(__A							)
						return mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any   ):
					__a			:       List[str]					    =  list(readme_content.splitlines()   )
					if full_content and full_content[0] == "---" and "---" in full_content[1:]:
										__a			:       str					    =  full_content[1:].index('---'   ) + 1
										__a			:       Optional[int]					    =  "\n".join(full_content[1:sep_idx]   )
										return yamlblock, "\n".join(full_content[sep_idx + 1 :]   )

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





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    {"train_eval_index"}  # train-eval-index in the YAML metadata




	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a							):

						'''simple docstring'''





						with open(__A						,				encoding='utf-8'							) as readme_file:
											__a			:       Dict					    =  _split_yaml_from_readme(readme_file.read()							)
						if yaml_string is not None:
											return cls.from_yaml_string(__A							)
						else:
											return cls()




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						if path.exists():
											with open(__A						,				encoding='utf-8'							) as readme_file:
																__a			:       int					    =  readme_file.read()
						else:
											__a			:       Tuple					    =  None
						__a			:       Any					    =  self._to_readme(__A							)
						with open(__A						,				'w'						,				encoding='utf-8'							) as readme_file:
											readme_file.write(__A							)




	def       __UpperCAmelCase							(   self						,				__a = None							):

						'''simple docstring'''





						if readme_content is not None:
											__a			:       Dict					    =  _split_yaml_from_readme(__A							)
											__a			:       List[Any]					    =  "---\n" + self.to_yaml_string() + "---\n" + content
						else:
											__a			:       List[Any]					    =  "---\n" + self.to_yaml_string() + "---\n"
						return full_content




	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  yaml.load(__A						,				Loader=_NoDuplicateSafeLoader							) or {}

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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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



__lowercase	:						Dict       =	{
    '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

	__lowercase	:						str       =	ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
	ap.add_argument('readme_filepath')
	__lowercase	:						int       =	ap.parse_args()

	__lowercase	:						int       =	Path(args.readme_filepath)
	__lowercase	:						Dict       =	DatasetMetadata.from_readme(readme_filepath)
	print(dataset_metadata)
	dataset_metadata.to_readme(readme_filepath)







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



