





"""simple docstring"""





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

__A											=		logging.get_logger(__name__)


__A											=		{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__A											=		{
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

__A											=		{"""facebook/blenderbot-3B""": 128}






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       __magic_name__     :Dict		  =					VOCAB_FILES_NAMES
       __magic_name__     :List[str]		  =					PRETRAINED_VOCAB_FILES_MAP
       __magic_name__     :Any		  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __magic_name__     :Tuple		  =					["""input_ids""", """attention_mask"""]
       __magic_name__     :int		  =					BlenderbotTokenizer






       def __init__(    self    , __UpperCAmelCase=None    , __UpperCAmelCase=None    , __UpperCAmelCase=None    , __UpperCAmelCase="replace"    , __UpperCAmelCase="<s>"    , __UpperCAmelCase="</s>"    , __UpperCAmelCase="</s>"    , __UpperCAmelCase="<s>"    , __UpperCAmelCase="<unk>"    , __UpperCAmelCase="<pad>"    , __UpperCAmelCase="<mask>"    , __UpperCAmelCase=False    , __UpperCAmelCase=True    , **__UpperCAmelCase    , ):



         '''simple docstring'''

         super().__init__(
             __UpperCAmelCase    , __UpperCAmelCase    , tokenizer_file=__UpperCAmelCase    , errors=__UpperCAmelCase    , bos_token=__UpperCAmelCase    , eos_token=__UpperCAmelCase    , sep_token=__UpperCAmelCase    , cls_token=__UpperCAmelCase    , unk_token=__UpperCAmelCase    , pad_token=__UpperCAmelCase    , mask_token=__UpperCAmelCase    , add_prefix_space=__UpperCAmelCase    , trim_offsets=__UpperCAmelCase    , **__UpperCAmelCase    , )

         lowerCAmelCase__			:Dict											=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
         if pre_tok_state.get('add_prefix_space'    , __UpperCAmelCase      ) != add_prefix_space:
           lowerCAmelCase__			:Optional[int]											=							getattr(__UpperCAmelCase    , pre_tok_state.pop('type'      )      )
           lowerCAmelCase__			:Union[str, Any]											=							add_prefix_space
           lowerCAmelCase__			:Optional[int]											=							pre_tok_class(**__UpperCAmelCase      )

         lowerCAmelCase__			:Tuple											=							add_prefix_space

         lowerCAmelCase__			:Optional[Any]											=							'post_processor'
         lowerCAmelCase__			:Any											=							getattr(self.backend_tokenizer    , __UpperCAmelCase    , __UpperCAmelCase      )
         if tokenizer_component_instance:
           lowerCAmelCase__			:List[str]											=							json.loads(tokenizer_component_instance.__getstate__()      )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
             lowerCAmelCase__			:Tuple											=							tuple(state['sep']      )
           if "cls" in state:
             lowerCAmelCase__			:List[Any]											=							tuple(state['cls']      )

           lowerCAmelCase__			:List[Any]											=							False

           if state.get('add_prefix_space'    , __UpperCAmelCase      ) != add_prefix_space:
             lowerCAmelCase__			:Union[str, Any]											=							add_prefix_space
             lowerCAmelCase__			:int											=							True

           if state.get('trim_offsets'    , __UpperCAmelCase      ) != trim_offsets:
             lowerCAmelCase__			:List[Any]											=							trim_offsets
             lowerCAmelCase__			:Any											=							True

           if changes_to_apply:
             lowerCAmelCase__			:Optional[Any]											=							getattr(__UpperCAmelCase    , state.pop('type'      )      )
             lowerCAmelCase__			:Union[str, Any]											=							component_class(**__UpperCAmelCase      )
             setattr(self.backend_tokenizer    , __UpperCAmelCase    , __UpperCAmelCase      )






       @property
       # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
       def      snake_case						(    self      ):



         '''simple docstring'''

         if self._mask_token is None:
           if self.verbose:
             logger.error('Using mask_token, but it is not set yet.'      )
           return None
         return str(self._mask_token      )






       @mask_token.setter
       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							AddedToken(__UpperCAmelCase    , lstrip=__UpperCAmelCase    , rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ) else value
         lowerCAmelCase__			:int											=							value






       def      snake_case						(    self    , *__UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							kwargs.get('is_split_into_words'    , __UpperCAmelCase      )
         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__UpperCAmelCase    , **__UpperCAmelCase      )






       def      snake_case						(    self    , *__UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							kwargs.get('is_split_into_words'    , __UpperCAmelCase      )

         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__UpperCAmelCase    , **__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = None      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							self._tokenizer.model.save(__UpperCAmelCase    , name=__UpperCAmelCase      )
         return tuple(__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = None      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							[self.sep_token_id]
         lowerCAmelCase__			:Optional[int]											=							[self.cls_token_id]

         if token_ids_a is None:
           return len(cls + token_ids_a + sep      ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = None      ):



         '''simple docstring'''

         return token_ids_a + [self.eos_token_id]






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							[]
         for is_user, text in conversation.iter_texts():
           if is_user:
             # We need to space prefix as it's being done within blenderbot
             inputs.append(' ' + text      )
           else:
             # Generated responses should contain them already.
             inputs.append(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							'  '.join(__UpperCAmelCase      )
         lowerCAmelCase__			:Optional[Any]											=							self.encode(__UpperCAmelCase      )
         if len(__UpperCAmelCase      ) > self.model_max_length:
           lowerCAmelCase__			:Any											=							input_ids[-self.model_max_length :]
           logger.warning(F"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."      )
         return input_ids







"""simple docstring"""





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

import yaml






class 					_lowerCAmelCase   (     yaml.SafeLoader       ):
       """simple docstring"""




       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							[self.constructed_objects[key_node] for key_node, _ in node.value]
         lowerCAmelCase__			:str											=							[tuple(__UpperCAmelCase      ) if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ) else key for key in keys]
         lowerCAmelCase__			:Optional[int]											=							Counter(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							[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      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							super().construct_mapping(__UpperCAmelCase    , deep=__UpperCAmelCase      )
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase      )
         return mapping






def  __A   (_SCREAMING_SNAKE_CASE			)					->Tuple[Optional[str], str]:





  """simple docstring"""


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

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






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




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






       @classmethod
       def      snake_case						(    cls    , __UpperCAmelCase      ):



         '''simple docstring'''

         with open(__UpperCAmelCase    , encoding='utf-8'      ) as readme_file:
           lowerCAmelCase__							,				lowerCAmelCase__			:Union[str, Any]											=							_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      ):



         '''simple docstring'''

         if path.exists():
           with open(__UpperCAmelCase    , encoding='utf-8'      ) as readme_file:
             lowerCAmelCase__			:Optional[Any]											=							readme_file.read()
         else:
           lowerCAmelCase__			:Union[str, Any]											=							None
         lowerCAmelCase__			:Union[str, Any]											=							self._to_readme(__UpperCAmelCase      )
         with open(__UpperCAmelCase    , 'w'    , encoding='utf-8'      ) as readme_file:
           readme_file.write(__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase = None      ):



         '''simple docstring'''

         if readme_content is not None:
           lowerCAmelCase__							,				lowerCAmelCase__			:Optional[int]											=							_split_yaml_from_readme(__UpperCAmelCase      )
           lowerCAmelCase__			:Optional[Any]											=							'---\n' + self.to_yaml_string() + '---\n' + content
         else:
           lowerCAmelCase__			:str											=							'---\n' + self.to_yaml_string() + '---\n'
         return full_content






       @classmethod
       def      snake_case						(    cls    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							yaml.load(__UpperCAmelCase    , Loader=_NoDuplicateSafeLoader      ) or {}

         # Convert the YAML keys to DatasetMetadata fields
         lowerCAmelCase__			:int											=							{
             (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      ):



         '''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=__UpperCAmelCase    , allow_unicode=__UpperCAmelCase    , encoding='utf-8'    , ).decode('utf-8'      )






__A											=		{
    """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

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

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