






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

import yaml


class  SCREAMING_SNAKE_CASE			(			yaml.SafeLoader					):
     '''simple docstring'''


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [self.constructed_objects[key_node] for key_node, _ in node.value]
       SCREAMING_SNAKE_CASE       :      Tuple							=    [tuple(UpperCAmelCase_      ) if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) else key for key in keys]
       SCREAMING_SNAKE_CASE       :      Dict							=    Counter(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Dict							=    [key for key in counter if counter[key] > 1]
       if duplicate_keys:
         raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''      )






     def 				_A       (				self       :	int					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	int=False      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    super().construct_mapping(UpperCAmelCase_					, deep=UpperCAmelCase_      )
       self._check_no_duplicates_on_constructed_node(UpperCAmelCase_      )
       return mapping





def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    list(readme_content.splitlines()				)
  if full_content and full_content[0] == "---" and "---" in full_content[1:]:
    SCREAMING_SNAKE_CASE       :      str							=    full_content[1:].index("---"				) + 1
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    "\n".join(full_content[1:sep_idx]				)
    return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

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


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[str]			=	{'''train_eval_index'''}  # train-eval-index in the YAML metadata


     @classmethod
     def 				_A       (				cls       :	int					, UpperCAmelCase_       :	Path      ):
       with open(UpperCAmelCase_					, encoding="utf-8"      ) as readme_file:
         SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    _split_yaml_from_readme(readme_file.read()      )
       if yaml_string is not None:
         return cls.from_yaml_string(UpperCAmelCase_      )
       else:
         return cls()


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	Path      ):
       if path.exists():
         with open(UpperCAmelCase_					, encoding="utf-8"      ) as readme_file:
           SCREAMING_SNAKE_CASE       :      List[Any]							=    readme_file.read()
       else:
         SCREAMING_SNAKE_CASE       :      Tuple							=    None
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self._to_readme(UpperCAmelCase_      )
       with open(UpperCAmelCase_					, "w"					, encoding="utf-8"      ) as readme_file:
         readme_file.write(UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[str] = None      ):
       if readme_content is not None:
         SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[int]							=    _split_yaml_from_readme(UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    "---\n" + self.to_yaml_string() + "---\n" + content
       else:
         SCREAMING_SNAKE_CASE       :      Tuple							=    "---\n" + self.to_yaml_string() + "---\n"
       return full_content


     @classmethod
     def 				_A       (				cls       :	str					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      Any							=    yaml.load(UpperCAmelCase_					, Loader=_NoDuplicateSafeLoader      ) or {}

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






     def 				_A       (				self       :	int      ):
       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"      )





snake_case    				=		{
    """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

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

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














from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	'''markuplm'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[str]=3_0522					, UpperCAmelCase_       :	Optional[Any]=768					, UpperCAmelCase_       :	Any=12					, UpperCAmelCase_       :	Tuple=12					, UpperCAmelCase_       :	Union[str, Any]=3072					, UpperCAmelCase_       :	Dict="gelu"					, UpperCAmelCase_       :	Optional[int]=0.1					, UpperCAmelCase_       :	Tuple=0.1					, UpperCAmelCase_       :	Dict=512					, UpperCAmelCase_       :	Optional[int]=2					, UpperCAmelCase_       :	Any=0.02					, UpperCAmelCase_       :	Dict=1E-12					, UpperCAmelCase_       :	int=0					, UpperCAmelCase_       :	Optional[Any]=0					, UpperCAmelCase_       :	Optional[int]=2					, UpperCAmelCase_       :	str=256					, UpperCAmelCase_       :	str=1024					, UpperCAmelCase_       :	List[str]=216					, UpperCAmelCase_       :	List[Any]=1001					, UpperCAmelCase_       :	Union[str, Any]=32					, UpperCAmelCase_       :	Dict=50					, UpperCAmelCase_       :	Optional[int]="absolute"					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Any=None					, **UpperCAmelCase_       :	Any					, ):
       super().__init__(
           pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_					, )
       SCREAMING_SNAKE_CASE       :      Any							=    vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Tuple							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      List[str]							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      Tuple							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      Any							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      int							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[str]							=    use_cache
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    classifier_dropout
       # additional properties
       SCREAMING_SNAKE_CASE       :      Any							=    max_depth
       SCREAMING_SNAKE_CASE       :      int							=    max_xpath_tag_unit_embeddings
       SCREAMING_SNAKE_CASE       :      List[str]							=    max_xpath_subs_unit_embeddings
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    tag_pad_id
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    subs_pad_id
       SCREAMING_SNAKE_CASE       :      List[Any]							=    xpath_unit_hidden_size






