






"""simple docstring"""





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						['RobertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE					:				Tuple					    =						[
      'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'RobertaForCausalLM',
      'RobertaForMaskedLM',
      'RobertaForMultipleChoice',
      'RobertaForQuestionAnswering',
      'RobertaForSequenceClassification',
      'RobertaForTokenClassification',
      'RobertaModel',
      'RobertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE					:				str					    =						[
      'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFRobertaForCausalLM',
      'TFRobertaForMaskedLM',
      'TFRobertaForMultipleChoice',
      'TFRobertaForQuestionAnswering',
      'TFRobertaForSequenceClassification',
      'TFRobertaForTokenClassification',
      'TFRobertaMainLayer',
      'TFRobertaModel',
      'TFRobertaPreTrainedModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE					:				str					    =						[
      'FlaxRobertaForCausalLM',
      'FlaxRobertaForMaskedLM',
      'FlaxRobertaForMultipleChoice',
      'FlaxRobertaForQuestionAnswering',
      'FlaxRobertaForSequenceClassification',
      'FlaxRobertaForTokenClassification',
      'FlaxRobertaModel',
      'FlaxRobertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
  from .tokenization_roberta import RobertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roberta_fast import RobertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta import (
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaForCausalLM,
        RobertaForMaskedLM,
        RobertaForMultipleChoice,
        RobertaForQuestionAnswering,
        RobertaForSequenceClassification,
        RobertaForTokenClassification,
        RobertaModel,
        RobertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta import (
        TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        TFRobertaForMultipleChoice,
        TFRobertaForQuestionAnswering,
        TFRobertaForSequenceClassification,
        TFRobertaForTokenClassification,
        TFRobertaMainLayer,
        TFRobertaModel,
        TFRobertaPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta import (
        FlaxRobertaForCausalLM,
        FlaxRobertaForMaskedLM,
        FlaxRobertaForMultipleChoice,
        FlaxRobertaForQuestionAnswering,
        FlaxRobertaForSequenceClassification,
        FlaxRobertaForTokenClassification,
        FlaxRobertaModel,
        FlaxRobertaPreTrainedModel,
    )

else:
  import sys

  _SCREAMING_SNAKE_CASE					:				Any					    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










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


__lowerCAmelCase		:				str       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				Dict       			=		{'vocab_file': 'spiece.model'}

__lowerCAmelCase		:				Optional[int]       			=		{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

__lowerCAmelCase		:				Dict       			=		{'bert_for_seq_generation': 512}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							int							 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							List[int]							 =					[]
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					["""input_ids""", """attention_mask"""]





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

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

							a    =			vocab_file

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





				@property
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							return self.sp_model.get_piece_size()





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[int]:
							a    =			{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				:     Dict							)					->     Optional[Any]:
							a    =			self.__dict__.copy()
							a    =			None
							return state





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

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

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





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     str							)					->     List[str]:
							return self.sp_model.encode(__lowerCamelCase					,      out_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Union[str, Any]							)					->     int:
							return self.sp_model.piece_to_id(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[str]							)					->     Any:
							a    =			self.sp_model.IdToPiece(__lowerCamelCase							)
							return token





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     Dict							)					->     Any:
							a    =			[]
							a    =			""
							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
													a    =			[]
										else:
													current_sub_tokens.append(__lowerCamelCase							)
							out_string += self.sp_model.decode(__lowerCamelCase							)
							return out_string.strip()





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[str] = None							)					->     Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a    =			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:
													a    =			self.sp_model.serialized_model_proto()
													fi.write(__lowerCamelCase							)

							return (out_vocab_file,)






