


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						,				__A ):






     """simple docstring"""





     lowerCamelCase :					int    =				[self.constructed_objects[key_node] for key_node, _ in node.value]
     lowerCamelCase :					Optional[int]    =				[tuple(__A ) if isinstance(__A						,				__A ) else key for key in keys]
     lowerCamelCase :					Optional[int]    =				Counter(__A )
     lowerCamelCase :					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 _snake_case    (		self						,				__A						,				__A=False ):






     """simple docstring"""





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






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				list(readme_content.splitlines()	)
  if full_content and full_content[0] == "---" and "---" in full_content[1:]:
    lowerCamelCase :					str    =				full_content[1:].index("---"	) + 1
    lowerCamelCase :					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_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		{"train_eval_index"}  # train-eval-index in the YAML metadata






   @classmethod
   def _snake_case    (		cls						,				__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A = None ):






     """simple docstring"""





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






   @classmethod
   def _snake_case    (		cls						,				__A ):






     """simple docstring"""





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

     # Convert the YAML keys to DatasetMetadata fields
     lowerCamelCase :					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 _snake_case    (		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" )



_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             =     {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      str     		=		"decision_transformer"
   __A :      Union[str, Any]     		=		["past_key_values"]
   __A :      Optional[int]     		=		{
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(		self						,				__A=17						,				__A=4						,				__A=128						,				__A=4096						,				__A=True						,				__A=1						,				__A=1024						,				__A=3						,				__A=1						,				__A=None						,				__A="relu"						,				__A=0.1						,				__A=0.1						,				__A=0.1						,				__A=1e-5						,				__A=0.02						,				__A=True						,				__A=True						,				__A=5_0256						,				__A=5_0256						,				__A=False						,				__A=False						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				state_dim
     lowerCamelCase :					Tuple    =				act_dim
     lowerCamelCase :					List[str]    =				hidden_size
     lowerCamelCase :					Optional[Any]    =				max_ep_len
     lowerCamelCase :					Union[str, Any]    =				action_tanh
     lowerCamelCase :					int    =				vocab_size
     lowerCamelCase :					List[Any]    =				n_positions
     lowerCamelCase :					Dict    =				n_layer
     lowerCamelCase :					int    =				n_head
     lowerCamelCase :					List[Any]    =				n_inner
     lowerCamelCase :					Any    =				activation_function
     lowerCamelCase :					Optional[int]    =				resid_pdrop
     lowerCamelCase :					str    =				embd_pdrop
     lowerCamelCase :					Tuple    =				attn_pdrop
     lowerCamelCase :					List[Any]    =				layer_norm_epsilon
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					Optional[int]    =				scale_attn_weights
     lowerCamelCase :					List[Any]    =				use_cache
     lowerCamelCase :					Tuple    =				scale_attn_by_inverse_layer_idx
     lowerCamelCase :					Optional[int]    =				reorder_and_upcast_attn

     lowerCamelCase :					Dict    =				bos_token_id
     lowerCamelCase :					Any    =				eos_token_id

     super().__init__(bos_token_id=__A						,				eos_token_id=__A						,				**__A )



