




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase_									=	{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

UpperCAmelCase_									=	{
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

UpperCAmelCase_									=	'▁'





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Optional[Any]    		=   VOCAB_FILES_NAMES
      UpperCAmelCase__      :	Dict    		=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__      :	int    		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase__      :	Optional[Any]    		=   ['input_ids', 'attention_mask']
      def __init__(				self: List[Any]    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: Dict="<s>"    ,       UpperCamelCase_: str="</s>"    ,       UpperCamelCase_: Tuple="</s>"    ,       UpperCamelCase_: Dict="<s>"    ,       UpperCamelCase_: int="<unk>"    ,       UpperCamelCase_: List[Any]="<pad>"    ,       UpperCamelCase_: Union[str, Any]="<mask>"    ,       UpperCamelCase_: Optional[Dict[str, Any]] = None    ,       **UpperCamelCase_: List[Any]    ,       ):
             # Mask token behave like a normal word, i.e. include the space before it
             __lowerCamelCase   =						AddedToken(UpperCamelCase_    ,       lstrip=UpperCamelCase_    ,       rstrip=UpperCamelCase_   ) if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ) else mask_token

             __lowerCamelCase   =						{} if sp_model_kwargs is None else sp_model_kwargs

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

             __lowerCamelCase   =						vocab_file
             __lowerCamelCase   =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
             self.sp_model.Load(str(UpperCamelCase_   )   )

             __lowerCamelCase   =						{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

             __lowerCamelCase   =						len(self.sp_model   ) - 1
             __lowerCamelCase   =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: List[int]    ,       UpperCamelCase_: Optional[List[int]] = None   ):

             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             __lowerCamelCase   =						[self.cls_token_id]
             __lowerCamelCase   =						[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: List[int]    ,       UpperCamelCase_: Optional[List[int]] = None    ,       UpperCamelCase_: bool = False   ):
             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=UpperCamelCase_    ,       token_ids_a=UpperCamelCase_    ,       already_has_special_tokens=UpperCamelCase_   )

             if token_ids_a is None:
                    return [1] + ([0] * len(UpperCamelCase_   )) + [1]
             return [1] + ([0] * len(UpperCamelCase_   )) + [1, 1] + ([0] * len(UpperCamelCase_   )) + [1]
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: List[int]    ,       UpperCamelCase_: Optional[List[int]] = None   ):
             __lowerCamelCase   =						[self.sep_token_id]
             __lowerCamelCase   =						[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]
      @property
      def 			lowerCAmelCase__						(				self: str   ):
             return len(self.sp_model   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						{self.convert_ids_to_tokens(UpperCamelCase_   ): i for i in range(self.vocab_size   )}
             vocab.update(self.added_tokens_encoder   )
             return vocab
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: str   ):
             return self.sp_model.encode(UpperCamelCase_    ,       out_type=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Optional[int]   ):
             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             __lowerCamelCase   =						self.sp_model.PieceToId(UpperCamelCase_   )

             return spm_id if spm_id else self.unk_token_id
      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: int   ):
             if index in self.fairseq_ids_to_tokens:
                    return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: int   ):
             __lowerCamelCase   =						[]
             __lowerCamelCase   =						""""""
             __lowerCamelCase   =						False
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           if not prev_is_special:
                                  out_string += " "
                           out_string += self.sp_model.decode(UpperCamelCase_   ) + token
                           __lowerCamelCase   =						True
                           __lowerCamelCase   =						[]
                    else:
                           current_sub_tokens.append(UpperCamelCase_   )
                           __lowerCamelCase   =						False
             out_string += self.sp_model.decode(UpperCamelCase_   )
             return out_string.strip()
      def __getstate__(				self: str   ):
             __lowerCamelCase   =						self.__dict__.copy()
             __lowerCamelCase   =						None
             return state
      def __setstate__(				self: Optional[int]    ,       UpperCamelCase_: List[Any]   ):
             __lowerCamelCase   =						d

             # for backward compatibility
             if not hasattr(self    ,       """sp_model_kwargs"""   ):
                    __lowerCamelCase   =						{}

             __lowerCamelCase   =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
             self.sp_model.Load(self.vocab_file   )




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Optional[str] = None   ):
             if not os.path.isdir(UpperCamelCase_   ):
                    logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
                    return
             __lowerCamelCase   =						os.path.join(
                 UpperCamelCase_    ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

             if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCamelCase_   ) and os.path.isfile(self.vocab_file   ):
                    copyfile(self.vocab_file    ,       UpperCamelCase_   )
             elif not os.path.isfile(self.vocab_file   ):
                    with open(UpperCamelCase_    ,       """wb"""   ) as fi:
                           __lowerCamelCase   =						self.sp_model.serialized_model_proto()
                           fi.write(UpperCamelCase_   )

             return (out_vocab_file,)


def    lowerCAmelCase_ (     __A,  __A							)     ->				None:







      '''simple docstring'''


      UpperCAmelCase__								=  len(__A							)
      print("The following activities are selected:"							)

      # The first activity is always selected
      UpperCAmelCase__								=  0
      print(__A,  end=","							)

      # Consider rest of the activities
      for j in range(__A							):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(__A,  end=","							)
                  UpperCAmelCase__								=  j


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					[1, 3, 0, 5, 8, 5]
      UpperCamelCase__											=					[2, 4, 6, 7, 9, 9]
      print_max_activities(start, finish)



