

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


_a		       =   logging.get_logger(__name__)

_a		       =   '''▁'''

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

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

_a		       =   {
    '''facebook/xglm-564M''': 2_0_4_8,
}






class 							A_ (		snake_case__					):
     _lowercase			:     List[str]								=	VOCAB_FILES_NAMES
     _lowercase			:     Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
     _lowercase			:     int								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowercase			:     Union[str, Any]								=	['input_ids', 'attention_mask']


     def __init__(							self			:		Tuple			,  UpperCAmelCase			:		List[str]			,  UpperCAmelCase			:		Union[str, Any]="<s>"			,  UpperCAmelCase			:		List[Any]="</s>"			,  UpperCAmelCase			:		Optional[Any]="</s>"			,  UpperCAmelCase			:		Dict="<s>"			,  UpperCAmelCase			:		Any="<unk>"			,  UpperCAmelCase			:		Optional[Any]="<pad>"			,  UpperCAmelCase			:		Tuple = None			,  **UpperCAmelCase			:		List[str]			,  )							->	None:
      __lowerCAmelCase:   Union[str, Any]   	=      {} if sp_model_kwargs is None else sp_model_kwargs

      # Compatibility with the original tokenizer
      __lowerCAmelCase:   Optional[Any]   	=      7
      __lowerCAmelCase:   Dict   	=      [F'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

      __lowerCAmelCase:   Tuple   	=      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=UpperCAmelCase			,  eos_token=UpperCAmelCase			,  unk_token=UpperCAmelCase			,  sep_token=UpperCAmelCase			,  cls_token=UpperCAmelCase			,  pad_token=UpperCAmelCase			,  sp_model_kwargs=self.sp_model_kwargs			,  **UpperCAmelCase			,  )

      __lowerCAmelCase:   Optional[Any]   	=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
      self.sp_model.Load(str(UpperCAmelCase					)					)
      __lowerCAmelCase:   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
      __lowerCAmelCase:   int   	=      1

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

      __lowerCAmelCase:   Any   	=      len(self.sp_model					)
      __lowerCAmelCase:   Dict   	=      {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words					)}
      self.fairseq_tokens_to_ids.update(UpperCAmelCase					)

      __lowerCAmelCase:   Union[str, Any]   	=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}


     def __getstate__(							self			:		Union[str, Any]					)							->	List[str]:
      __lowerCAmelCase:   Optional[int]   	=      self.__dict__.copy()
      __lowerCAmelCase:   List[Any]   	=      None
      __lowerCAmelCase:   Any   	=      self.sp_model.serialized_model_proto()
      return state


     def __setstate__(							self			:		int			,  UpperCAmelCase			:		Optional[Any]					)							->	Any:
      __lowerCAmelCase:   Dict   	=      d

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

      __lowerCAmelCase:   Tuple   	=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)


     def   UpperCAmelCase  (							self			:		int			,  UpperCAmelCase			:		int			,  UpperCAmelCase			:		Tuple = None					)							->	List[int]:

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


     def   UpperCAmelCase  (							self			:		Optional[int]			,  UpperCAmelCase			:		Tuple			,  UpperCAmelCase			:		Optional[Any] = None			,  UpperCAmelCase			:		List[Any] = False					)							->	List[int]:

      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					))
      return [1] + ([0] * len(UpperCAmelCase					)) + [1, 1] + ([0] * len(UpperCAmelCase					))


     def   UpperCAmelCase  (							self			:		Tuple			,  UpperCAmelCase			:		Dict			,  UpperCAmelCase			:		Optional[Any] = None					)							->	List[int]:
      __lowerCAmelCase:   Optional[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			:		Dict					)							->	int:
      return len(self.sp_model					) + self.fairseq_offset + self.num_madeup_words


     def   UpperCAmelCase  (							self			:		int					)							->	Union[str, Any]:
      __lowerCAmelCase:   Dict   	=      {self.convert_ids_to_tokens(UpperCAmelCase					): i for i in range(self.vocab_size					)}
      vocab.update(self.added_tokens_encoder					)
      return vocab


     def   UpperCAmelCase  (							self			:		Optional[int]			,  UpperCAmelCase			:		int					)							->	List[str]:
      return self.sp_model.encode(UpperCAmelCase			,  out_type=UpperCAmelCase					)


     def   UpperCAmelCase  (							self			:		List[Any]			,  UpperCAmelCase			:		Optional[int]					)							->	List[str]:
      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      __lowerCAmelCase:   Optional[Any]   	=      self.sp_model.PieceToId(UpperCAmelCase					)

      # 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			:		str			,  UpperCAmelCase			:		Union[str, Any]					)							->	Tuple:
      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			:		Tuple			,  UpperCAmelCase			:		Optional[int]					)							->	Dict:
      __lowerCAmelCase:   Any   	=      ''.join(UpperCAmelCase					).replace(UpperCAmelCase			,  ' '					).strip()
      return out_string


     def   UpperCAmelCase  (							self			:		Optional[int]			,  UpperCAmelCase			:		Optional[Any]			,  UpperCAmelCase			:		List[Any] = None					)							->	Tuple[str]:
      if not os.path.isdir(UpperCAmelCase					):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
       return
      __lowerCAmelCase:   Union[str, Any]   	=      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:   Optional[int]   	=      self.sp_model.serialized_model_proto()
        fi.write(UpperCAmelCase					)

      return (out_vocab_file,)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLMForMultipleChoice''',
      '''XLMForQuestionAnswering''',
      '''XLMForQuestionAnsweringSimple''',
      '''XLMForSequenceClassification''',
      '''XLMForTokenClassification''',
      '''XLMModel''',
      '''XLMPreTrainedModel''',
      '''XLMWithLMHeadModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLMForMultipleChoice''',
      '''TFXLMForQuestionAnsweringSimple''',
      '''TFXLMForSequenceClassification''',
      '''TFXLMForTokenClassification''',
      '''TFXLMMainLayer''',
      '''TFXLMModel''',
      '''TFXLMPreTrainedModel''',
      '''TFXLMWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

