"""simple docstring"""





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase   :						Optional[int]      =    logging.get_logger(__name__)

_UpperCamelCase   :						Dict      =    "▁"

_UpperCamelCase   :						Optional[Any]      =    {"vocab_file": "sentencepiece.bpe.model"}

_UpperCamelCase   :						Dict      =    {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

_UpperCamelCase   :						Tuple      =    {
    "facebook/xglm-564M": 20_48,
}


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Optional[int]   =  VOCAB_FILES_NAMES
      lowerCamelCase__		:    List[Any]   =  PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__		:    int   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__		:    List[str]   =  ["input_ids", "attention_mask"]


      def __init__(   self		,			a		,			a="<s>"		,			a="</s>"		,			a="</s>"		,			a="<s>"		,			a="<unk>"		,			a="<pad>"		,			a = None		,			**a		,			)       ->					None:
             lowercase__			:		Optional[Any]       =	{} if sp_model_kwargs is None else sp_model_kwargs

             # Compatibility with the original tokenizer
             lowercase__			:		int       =	7
             lowercase__			:		int       =	[f"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

             lowercase__			:		Optional[int]       =	kwargs.get('additional_special_tokens'		,			[]							)
             kwargs["additional_special_tokens"] += [
                 word for word in madeup_words if word not in kwargs["additional_special_tokens"]
             ]

             super().__init__(
                 bos_token=a		,			eos_token=a		,			unk_token=a		,			sep_token=a		,			cls_token=a		,			pad_token=a		,			sp_model_kwargs=self.sp_model_kwargs		,			**a		,			)

             lowercase__			:		Optional[int]       =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(str(a							)							)
             lowercase__			:		Union[str, Any]       =	vocab_file

             # Original fairseq vocab and spm vocab must be "aligned":
             # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
             # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
             # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
             # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

             # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
             lowercase__			:		List[str]       =	1

             # Mimic fairseq token-to-id alignment for the first 4 token
             lowercase__			:		Union[str, Any]       =	{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

             lowercase__			:		Tuple       =	len(self.sp_model							)
             lowercase__			:		List[Any]       =	{f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
             self.fairseq_tokens_to_ids.update(a							)

             lowercase__			:		str       =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}


      def __getstate__(   self							)       ->					List[str]:
             lowercase__			:		List[Any]       =	self.__dict__.copy()
             lowercase__			:		Tuple       =	None
             lowercase__			:		Dict       =	self.sp_model.serialized_model_proto()
             return state


      def __setstate__(   self		,			a							)       ->					int:
             lowercase__			:		Optional[Any]       =	d

             # for backward compatibility
             if not hasattr(self		,			'sp_model_kwargs'							):
                    lowercase__			:		Tuple       =	{}

             lowercase__			:		Dict       =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


      def 		_UpperCAmelCase       (   self		,			a		,			a = None							)       ->					List[int]:

             if token_ids_a is None:
                    return [self.sep_token_id] + token_ids_a
             lowercase__			:		Optional[Any]       =	[self.sep_token_id]
             return sep + token_ids_a + sep + sep + token_ids_a


      def 		_UpperCAmelCase       (   self		,			a		,			a = None		,			a = False							)       ->					List[int]:

             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=a		,			token_ids_a=a		,			already_has_special_tokens=a							)

             if token_ids_a is None:
                    return [1] + ([0] * len(a							))
             return [1] + ([0] * len(a							)) + [1, 1] + ([0] * len(a							))


      def 		_UpperCAmelCase       (   self		,			a		,			a = None							)       ->					List[int]:
             lowercase__			:		Union[str, Any]       =	[self.sep_token_id]

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


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		Optional[Any]       =	{self.convert_ids_to_tokens(a							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab


      def 		_UpperCAmelCase       (   self		,			a							)       ->					List[str]:
             return self.sp_model.encode(a		,			out_type=a							)


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Any:
             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             lowercase__			:		Any       =	self.sp_model.PieceToId(a							)

             # Need to return unknown token if the SP model returned 0
             return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


      def 		_UpperCAmelCase       (   self		,			a							)       ->					List[Any]:
             if index in self.fairseq_ids_to_tokens:
                    return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset							)


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Dict:
             lowercase__			:		List[Any]       =	''.join(a							).replace(a		,			' '							).strip()
             return out_string


      def 		_UpperCAmelCase       (   self		,			a		,			a = None							)       ->					Tuple[str]:
             if not os.path.isdir(a							):
                    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
                    return
             lowercase__			:		Any       =	os.path.join(
                 a		,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

             if os.path.abspath(self.vocab_file							) != os.path.abspath(a							) and os.path.isfile(self.vocab_file							):
                    copyfile(self.vocab_file		,			a							)
             elif not os.path.isfile(self.vocab_file							):
                    with open(a		,			'wb'							) as fi:
                           lowercase__			:		Any       =	self.sp_model.serialized_model_proto()
                           fi.write(a							)

             return (out_vocab_file,)


"""simple docstring"""







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

import yaml







class        UpperCamelCase		(			yaml.SafeLoader  ):
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				str													=	[self.constructed_objects[key_node] for key_node, _ in node.value]
        lowercase_	:				str													=	[tuple(__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else key for key in keys]
        lowercase_	:				List[Any]													=	Counter(__UpperCamelCase			)
        lowercase_	:				str													=	[key for key in counter if counter[key] > 1]
        if duplicate_keys:
             raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=False			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	super().construct_mapping(__UpperCamelCase     ,deep=__UpperCamelCase			)
        self._check_no_duplicates_on_constructed_node(__UpperCamelCase			)
        return mapping






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Tuple													=	list(readme_content.splitlines()      )
     if full_content and full_content[0] == "---" and "---" in full_content[1:]:
          lowercase_	:				Dict													=	full_content[1:].index('---'      ) + 1
          lowercase_	:				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		(			lowercase_  ):
   # class attributes
   lowercase							=  {'train_eval_index'}  # train-eval-index in the YAML metadata
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
             lowercase_							,  lowercase_	:				Optional[int]													=	_split_yaml_from_readme(readme_file.read()			)
        if yaml_string is not None:
             return cls.from_yaml_string(__UpperCamelCase			)
        else:
             return cls()
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        if path.exists():
             with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
                  lowercase_	:				Dict													=	readme_file.read()
        else:
             lowercase_	:				int													=	None
        lowercase_	:				Any													=	self._to_readme(__UpperCamelCase			)
        with open(__UpperCamelCase     ,'w'     ,encoding='utf-8'			) as readme_file:
             readme_file.write(__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = None			)	->      str:


        '''simple docstring'''


        if readme_content is not None:
             lowercase_							,  lowercase_	:				Optional[Any]													=	_split_yaml_from_readme(__UpperCamelCase			)
             lowercase_	:				Optional[Any]													=	'---\n' + self.to_yaml_string() + '---\n' + content
        else:
             lowercase_	:				Tuple													=	'---\n' + self.to_yaml_string() + '---\n'
        return full_content
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        lowercase_	:				List[str]													=	yaml.load(__UpperCamelCase     ,Loader=_NoDuplicateSafeLoader			) or {}

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







   def 			_UpperCAmelCase   (			self			)	->      str:


        '''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'			)






__SCREAMING_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

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

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

