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


lowercase_     :   Dict 				=				'▁'

lowercase_     :   List[str] 				=				{'vocab_file': 'spiece.model'}

lowercase_     :   Any 				=				{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

lowercase_     :   List[str] 				=				{
    'google/pegasus-xsum': 5_1_2,
}


lowercase_     :   List[Any] 				=				logging.get_logger(__name__)






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      VOCAB_FILES_NAMES

    __a  =      VOCAB_FILES_NAMES
    __a  =      PRETRAINED_VOCAB_FILES_MAP
    __a  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __a  =      ["input_ids", "attention_mask"]
    def __init__(  self , lowerCAmelCase , lowerCAmelCase="<pad>" , lowerCAmelCase="</s>" , lowerCAmelCase="<unk>" , lowerCAmelCase="<mask_2>" , lowerCAmelCase="<mask_1>" , lowerCAmelCase=None , lowerCAmelCase=103 , lowerCAmelCase = None , **lowerCAmelCase , )       ->					None:
     SCREAMING_SNAKE_CASE__:   Tuple=      offset
     if additional_special_tokens is not None:
      if not isinstance(lowerCAmelCase , lowerCAmelCase   ):
       raise TypeError(
           f'additional_special_tokens should be of type {type(lowerCAmelCase   )}, but is'
           f' {type(lowerCAmelCase   )}'   )

      SCREAMING_SNAKE_CASE__:   Any=      (
          ([mask_token_sent] + additional_special_tokens)
          if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
          else additional_special_tokens
      )
      # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
      additional_special_tokens_extended += [
          f'<unk_{i}>' for i in range(len(lowerCAmelCase   ) , self.offset - 1   )
      ]

      if len(set(lowerCAmelCase   )   ) != len(lowerCAmelCase   ):
       raise ValueError(
           '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
           f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'   )
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      additional_special_tokens_extended
     else:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      [mask_token_sent] if mask_token_sent is not None else []
      additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset   )]

     SCREAMING_SNAKE_CASE__:   List[Any]=      {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=lowerCAmelCase , unk_token=lowerCAmelCase , mask_token=lowerCAmelCase , pad_token=lowerCAmelCase , mask_token_sent=lowerCAmelCase , offset=lowerCAmelCase , additional_special_tokens=lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase , )
     SCREAMING_SNAKE_CASE__:   Dict=      mask_token_sent
     SCREAMING_SNAKE_CASE__:   Tuple=      vocab_file
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(lowerCAmelCase   )

     # add special tokens to encoder dict
     SCREAMING_SNAKE_CASE__:   Dict[int, str]=      {
         0: self.pad_token,
         1: self.eos_token,
     }

     if self.mask_token_sent is not None:
      self.encoder.update(
          {
              2: self.mask_token_sent,
              3: self.mask_token,
          }   )

     if self.offset > 0:
      # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
      # mask_token_sent is already added to list -> so start at 1
      self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1   )}   )

     SCREAMING_SNAKE_CASE__:   Dict[str, int]=      {v: k for k, v in self.encoder.items()}
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     return len(self.sp_model   ) + self.offset
    def 						UpperCamelCase_				(  self   )       ->					Dict[str, int]:
     SCREAMING_SNAKE_CASE__:   Any=      {self.convert_ids_to_tokens(lowerCAmelCase   ): i for i in range(self.vocab_size   )}
     vocab.update(self.added_tokens_encoder   )
     return vocab
    def __getstate__(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   str=      self.__dict__.copy()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      None
     return state
    def __setstate__(  self , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Tuple=      d

     # for backward compatibility
     if not hasattr(self , '''sp_model_kwargs'''   ):
      SCREAMING_SNAKE_CASE__:   List[str]=      {}

     SCREAMING_SNAKE_CASE__:   str=      spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(self.vocab_file   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
     return self.sp_model.encode(lowerCAmelCase , out_type=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					int:
     if token in self.decoder:
      return self.decoder[token]
     elif token in self.added_tokens_decoder:
      return self.added_tokens_decoder[token]
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.sp_model.piece_to_id(lowerCAmelCase   )
     return sp_id + self.offset
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					str:
     if index in self.encoder:
      return self.encoder[index]
     elif index in self.added_tokens_encoder:
      return self.added_tokens_encoder[index]
     else:
      SCREAMING_SNAKE_CASE__:   List[Any]=      self.sp_model.IdToPiece(index - self.offset   )
     return token
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      []
     SCREAMING_SNAKE_CASE__:   Dict=      ''''''
     for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
       out_string += self.sp_model.decode(lowerCAmelCase   ) + token
       SCREAMING_SNAKE_CASE__:   Optional[Any]=      []
      else:
       current_sub_tokens.append(lowerCAmelCase   )
     out_string += self.sp_model.decode(lowerCAmelCase   )
     return out_string.strip()
    def 						UpperCamelCase_				(  self , lowerCAmelCase=False   )       ->					List[Any]:
     return 1
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      set(self.all_special_ids   )  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special

     return [1 if x in all_special_ids else 0 for x in seq]
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = False   )       ->					List[int]:
     if already_has_special_tokens:
      return self._special_token_mask(lowerCAmelCase   )
     elif token_ids_a is None:
      return self._special_token_mask(lowerCAmelCase   ) + [1]
     else:
      return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=None   )       ->					List[int]:
     if token_ids_a is None:
      return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]



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

     if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase   ) and os.path.isfile(self.vocab_file   ):
      copyfile(self.vocab_file , lowerCAmelCase   )
     elif not os.path.isfile(self.vocab_file   ):
      with open(lowerCAmelCase , '''wb'''   ) as fi:
       SCREAMING_SNAKE_CASE__:   Optional[int]=      self.sp_model.serialized_model_proto()
       fi.write(lowerCAmelCase   )

     return (out_vocab_file,)


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_     :   List[Any] 				=				logging.get_logger(__name__)

lowercase_     :   List[Any] 				=				{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      "xlm-roberta-xl"
    def __init__(  self , lowerCAmelCase=250880 , lowerCAmelCase=2560 , lowerCAmelCase=36 , lowerCAmelCase=32 , lowerCAmelCase=10240 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=514 , lowerCAmelCase=1 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-05 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase="absolute" , lowerCAmelCase=True , lowerCAmelCase=None , **lowerCAmelCase , )       ->					Union[str, Any]:
     super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      vocab_size
     SCREAMING_SNAKE_CASE__:   Dict=      hidden_size
     SCREAMING_SNAKE_CASE__:   Tuple=      num_hidden_layers
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      num_attention_heads
     SCREAMING_SNAKE_CASE__:   int=      hidden_act
     SCREAMING_SNAKE_CASE__:   Any=      intermediate_size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__:   str=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__:   List[Any]=      max_position_embeddings
     SCREAMING_SNAKE_CASE__:   Dict=      type_vocab_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      initializer_range
     SCREAMING_SNAKE_CASE__:   Dict=      layer_norm_eps
     SCREAMING_SNAKE_CASE__:   Dict=      position_embedding_type
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      use_cache
     SCREAMING_SNAKE_CASE__:   str=      classifier_dropout






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    @property
    def 						UpperCamelCase_				(  self   )       ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
      SCREAMING_SNAKE_CASE__:   Dict=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
      SCREAMING_SNAKE_CASE__:   List[str]=      {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]   )

