
"""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			:       List[str]									=				logging.get_logger(__name__)

UpperCamelCase			:       List[Any]									=				{"vocab_file": "spiece.model"}

UpperCamelCase			:       Dict									=				{
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

UpperCamelCase			:       Tuple									=				{"bert_for_seq_generation": 5_1_2}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		VOCAB_FILES_NAMES
     lowercase		     =		PRETRAINED_VOCAB_FILES_MAP
     lowercase		     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase		     =		[]
     lowercase		     =		["input_ids", "attention_mask"]





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

      '''simple docstring'''




      __UpperCamelCase       						= {} if sp_model_kwargs is None else sp_model_kwargs

      # Add extra_ids to the special token list
      super().__init__(
          bos_token=__UpperCAmelCase			,  eos_token=__UpperCAmelCase			,  unk_token=__UpperCAmelCase			,  pad_token=__UpperCAmelCase			,  sep_token=__UpperCAmelCase			,  sp_model_kwargs=self.sp_model_kwargs			,  **__UpperCAmelCase			,  )

      __UpperCamelCase       						= vocab_file

      __UpperCamelCase       						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(__UpperCAmelCase   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.sp_model.get_piece_size()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= {self.convert_ids_to_tokens(__UpperCAmelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





     def __getstate__(			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.__dict__.copy()
      __UpperCamelCase       						= None
      return state





     def __setstate__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= d

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

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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.sp_model.encode(__UpperCAmelCase			,  out_type=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.sp_model.piece_to_id(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.sp_model.IdToPiece(__UpperCAmelCase   )
      return token





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= []
      __UpperCamelCase       						= ''
      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(__UpperCAmelCase   ) + token
        __UpperCamelCase       						= []
       else:
        current_sub_tokens.append(__UpperCAmelCase   )
      out_string += self.sp_model.decode(__UpperCAmelCase   )
      return out_string.strip()







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

      '''simple docstring'''




      if not os.path.isdir(__UpperCAmelCase   ):
       logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
       return
      __UpperCamelCase       						= 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:
        __UpperCamelCase       						= self.sp_model.serialized_model_proto()
        fi.write(__UpperCAmelCase   )

      return (out_vocab_file,)





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase			:       List[Any]									=				{
    "configuration_xlm_roberta": [
        "XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaConfig",
        "XLMRobertaOnnxConfig",
    ],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				["XLMRobertaTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       int									=				["XLMRobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[int]									=				[
          "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMRobertaForCausalLM",
          "XLMRobertaForMaskedLM",
          "XLMRobertaForMultipleChoice",
          "XLMRobertaForQuestionAnswering",
          "XLMRobertaForSequenceClassification",
          "XLMRobertaForTokenClassification",
          "XLMRobertaModel",
          "XLMRobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				[
          "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFXLMRobertaForCausalLM",
          "TFXLMRobertaForMaskedLM",
          "TFXLMRobertaForMultipleChoice",
          "TFXLMRobertaForQuestionAnswering",
          "TFXLMRobertaForSequenceClassification",
          "TFXLMRobertaForTokenClassification",
          "TFXLMRobertaModel",
          "TFXLMRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				[
          "FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxXLMRobertaForMaskedLM",
          "FlaxXLMRobertaForCausalLM",
          "FlaxXLMRobertaForMultipleChoice",
          "FlaxXLMRobertaForQuestionAnswering",
          "FlaxXLMRobertaForSequenceClassification",
          "FlaxXLMRobertaForTokenClassification",
          "FlaxXLMRobertaModel",
          "FlaxXLMRobertaPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_xlm_roberta import (
          XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLMRobertaConfig,
          XLMRobertaOnnxConfig,
      )

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta import XLMRobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_roberta import (
                XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMRobertaForCausalLM,
                XLMRobertaForMaskedLM,
                XLMRobertaForMultipleChoice,
                XLMRobertaForQuestionAnswering,
                XLMRobertaForSequenceClassification,
                XLMRobertaForTokenClassification,
                XLMRobertaModel,
                XLMRobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_xlm_roberta import (
                TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFXLMRobertaForCausalLM,
                TFXLMRobertaForMaskedLM,
                TFXLMRobertaForMultipleChoice,
                TFXLMRobertaForQuestionAnswering,
                TFXLMRobertaForSequenceClassification,
                TFXLMRobertaForTokenClassification,
                TFXLMRobertaModel,
                TFXLMRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_xlm_roberta import (
                FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxXLMRobertaForCausalLM,
                FlaxXLMRobertaForMaskedLM,
                FlaxXLMRobertaForMultipleChoice,
                FlaxXLMRobertaForQuestionAnswering,
                FlaxXLMRobertaForSequenceClassification,
                FlaxXLMRobertaForTokenClassification,
                FlaxXLMRobertaModel,
                FlaxXLMRobertaPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       Dict									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



