code: 
  from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  snake_case				:	Tuple							   =						{
      "configuration_utils": ["GenerationConfig"],
      "streamers": ["TextIteratorStreamer", "TextStreamer"],
  }
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[Any]							   =						[
  				    "Constraint",
  				    "ConstraintListState",
  				    "DisjunctiveConstraint",
  				    "PhrasalConstraint",
  				]
  				snake_case				:	List[str]							   =						[
  				    "BeamHypotheses",
  				    "BeamScorer",
  				    "BeamSearchScorer",
  				    "ConstrainedBeamSearchScorer",
  				]
  				snake_case				:	Optional[int]							   =						[
  				    "EpsilonLogitsWarper",
  				    "EtaLogitsWarper",
  				    "ForcedBOSTokenLogitsProcessor",
  				    "ForcedEOSTokenLogitsProcessor",
  				    "HammingDiversityLogitsProcessor",
  				    "InfNanRemoveLogitsProcessor",
  				    "LogitsProcessor",
  				    "LogitsProcessorList",
  				    "LogitsWarper",
  				    "MinLengthLogitsProcessor",
  				    "MinNewTokensLengthLogitsProcessor",
  				    "NoBadWordsLogitsProcessor",
  				    "NoRepeatNGramLogitsProcessor",
  				    "PrefixConstrainedLogitsProcessor",
  				    "RepetitionPenaltyLogitsProcessor",
  				    "SequenceBiasLogitsProcessor",
  				    "EncoderRepetitionPenaltyLogitsProcessor",
  				    "TemperatureLogitsWarper",
  				    "TopKLogitsWarper",
  				    "TopPLogitsWarper",
  				    "TypicalLogitsWarper",
  				    "EncoderNoRepeatNGramLogitsProcessor",
  				    "ExponentialDecayLengthPenalty",
  				    "LogitNormalization",
  				]
  				snake_case				:	List[Any]							   =						[
  				    "MaxNewTokensCriteria",
  				    "MaxLengthCriteria",
  				    "MaxTimeCriteria",
  				    "StoppingCriteria",
  				    "StoppingCriteriaList",
  				    "validate_stopping_criteria",
  				]
  				snake_case				:	Union[str, Any]							   =						[
  				    "GenerationMixin",
  				    "top_k_top_p_filtering",
  				    "GreedySearchEncoderDecoderOutput",
  				    "GreedySearchDecoderOnlyOutput",
  				    "SampleEncoderDecoderOutput",
  				    "SampleDecoderOnlyOutput",
  				    "BeamSearchEncoderDecoderOutput",
  				    "BeamSearchDecoderOnlyOutput",
  				    "BeamSampleEncoderDecoderOutput",
  				    "BeamSampleDecoderOnlyOutput",
  				    "ContrastiveSearchEncoderDecoderOutput",
  				    "ContrastiveSearchDecoderOnlyOutput",
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	List[Any]							   =						[
  				    "TFForcedBOSTokenLogitsProcessor",
  				    "TFForcedEOSTokenLogitsProcessor",
  				    "TFLogitsProcessor",
  				    "TFLogitsProcessorList",
  				    "TFLogitsWarper",
  				    "TFMinLengthLogitsProcessor",
  				    "TFNoBadWordsLogitsProcessor",
  				    "TFNoRepeatNGramLogitsProcessor",
  				    "TFRepetitionPenaltyLogitsProcessor",
  				    "TFTemperatureLogitsWarper",
  				    "TFTopKLogitsWarper",
  				    "TFTopPLogitsWarper",
  				    "TFForceTokensLogitsProcessor",
  				    "TFSuppressTokensAtBeginLogitsProcessor",
  				    "TFSuppressTokensLogitsProcessor",
  				]
  				snake_case				:	Optional[int]							   =						[
  				    "TFGenerationMixin",
  				    "tf_top_k_top_p_filtering",
  				    "TFGreedySearchDecoderOnlyOutput",
  				    "TFGreedySearchEncoderDecoderOutput",
  				    "TFSampleEncoderDecoderOutput",
  				    "TFSampleDecoderOnlyOutput",
  				    "TFBeamSearchEncoderDecoderOutput",
  				    "TFBeamSearchDecoderOnlyOutput",
  				    "TFBeamSampleEncoderDecoderOutput",
  				    "TFBeamSampleDecoderOnlyOutput",
  				    "TFContrastiveSearchEncoderDecoderOutput",
  				    "TFContrastiveSearchDecoderOnlyOutput",
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	List[Any]							   =						[
  				    "FlaxForcedBOSTokenLogitsProcessor",
  				    "FlaxForcedEOSTokenLogitsProcessor",
  				    "FlaxLogitsProcessor",
  				    "FlaxLogitsProcessorList",
  				    "FlaxLogitsWarper",
  				    "FlaxMinLengthLogitsProcessor",
  				    "FlaxTemperatureLogitsWarper",
  				    "FlaxTopKLogitsWarper",
  				    "FlaxTopPLogitsWarper",
  				]
  				snake_case				:	List[Any]							   =						[
  				    "FlaxGenerationMixin",
  				    "FlaxGreedySearchOutput",
  				    "FlaxSampleOutput",
  				    "FlaxBeamSearchOutput",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_utils import GenerationConfig
  				from .streamers import TextIteratorStreamer, TextStreamer
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
  								from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
  								from .logits_process import (
  								    EncoderNoRepeatNGramLogitsProcessor,
  								    EncoderRepetitionPenaltyLogitsProcessor,
  								    EpsilonLogitsWarper,
  								    EtaLogitsWarper,
  								    ExponentialDecayLengthPenalty,
  								    ForcedBOSTokenLogitsProcessor,
  								    ForcedEOSTokenLogitsProcessor,
  								    HammingDiversityLogitsProcessor,
  								    InfNanRemoveLogitsProcessor,
  								    LogitNormalization,
  								    LogitsProcessor,
  								    LogitsProcessorList,
  								    LogitsWarper,
  								    MinLengthLogitsProcessor,
  								    MinNewTokensLengthLogitsProcessor,
  								    NoBadWordsLogitsProcessor,
  								    NoRepeatNGramLogitsProcessor,
  								    PrefixConstrainedLogitsProcessor,
  								    RepetitionPenaltyLogitsProcessor,
  								    SequenceBiasLogitsProcessor,
  								    TemperatureLogitsWarper,
  								    TopKLogitsWarper,
  								    TopPLogitsWarper,
  								    TypicalLogitsWarper,
  								)
  								from .stopping_criteria import (
  								    MaxLengthCriteria,
  								    MaxNewTokensCriteria,
  								    MaxTimeCriteria,
  								    StoppingCriteria,
  								    StoppingCriteriaList,
  								    validate_stopping_criteria,
  								)
  								from .utils import (
  								    BeamSampleDecoderOnlyOutput,
  								    BeamSampleEncoderDecoderOutput,
  								    BeamSearchDecoderOnlyOutput,
  								    BeamSearchEncoderDecoderOutput,
  								    ContrastiveSearchDecoderOnlyOutput,
  								    ContrastiveSearchEncoderDecoderOutput,
  								    GenerationMixin,
  								    GreedySearchDecoderOnlyOutput,
  								    GreedySearchEncoderDecoderOutput,
  								    SampleDecoderOnlyOutput,
  								    SampleEncoderDecoderOutput,
  								    top_k_top_p_filtering,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tf_logits_process import (
  								    TFForcedBOSTokenLogitsProcessor,
  								    TFForcedEOSTokenLogitsProcessor,
  								    TFForceTokensLogitsProcessor,
  								    TFLogitsProcessor,
  								    TFLogitsProcessorList,
  								    TFLogitsWarper,
  								    TFMinLengthLogitsProcessor,
  								    TFNoBadWordsLogitsProcessor,
  								    TFNoRepeatNGramLogitsProcessor,
  								    TFRepetitionPenaltyLogitsProcessor,
  								    TFSuppressTokensAtBeginLogitsProcessor,
  								    TFSuppressTokensLogitsProcessor,
  								    TFTemperatureLogitsWarper,
  								    TFTopKLogitsWarper,
  								    TFTopPLogitsWarper,
  								)
  								from .tf_utils import (
  								    TFBeamSampleDecoderOnlyOutput,
  								    TFBeamSampleEncoderDecoderOutput,
  								    TFBeamSearchDecoderOnlyOutput,
  								    TFBeamSearchEncoderDecoderOutput,
  								    TFContrastiveSearchDecoderOnlyOutput,
  								    TFContrastiveSearchEncoderDecoderOutput,
  								    TFGenerationMixin,
  								    TFGreedySearchDecoderOnlyOutput,
  								    TFGreedySearchEncoderDecoderOutput,
  								    TFSampleDecoderOnlyOutput,
  								    TFSampleEncoderDecoderOutput,
  								    tf_top_k_top_p_filtering,
  								)
  
  				try:
  								if not is_flax_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .flax_logits_process import (
  								    FlaxForcedBOSTokenLogitsProcessor,
  								    FlaxForcedEOSTokenLogitsProcessor,
  								    FlaxLogitsProcessor,
  								    FlaxLogitsProcessorList,
  								    FlaxLogitsWarper,
  								    FlaxMinLengthLogitsProcessor,
  								    FlaxTemperatureLogitsWarper,
  								    FlaxTopKLogitsWarper,
  								    FlaxTopPLogitsWarper,
  								)
  								from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
  				import sys
  
  				snake_case				:	Any							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
style_context: 
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
  				from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
  				snake_case				:	Any							   =						None
  
  
  snake_case				:	Union[str, Any]							   =						logging.get_logger(__name__)
  
  snake_case				:	Any							   =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  snake_case				:	Optional[Any]							   =						{
      "vocab_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
          ),
      },
      "tokenizer_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
          ),
      },
  }
  
  snake_case				:	Optional[int]							   =						{
      "xlm-roberta-base": 512,
      "xlm-roberta-large": 512,
      "xlm-roberta-large-finetuned-conll02-dutch": 512,
      "xlm-roberta-large-finetuned-conll02-spanish": 512,
      "xlm-roberta-large-finetuned-conll03-english": 512,
      "xlm-roberta-large-finetuned-conll03-german": 512,
  }
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					VOCAB_FILES_NAMES
  			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
  			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			UpperCamelCase__					   =					['input_ids', 'attention_mask']
  			UpperCamelCase__					   =					XLMRobertaTokenizer
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=None		,     _a=None		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<s>"		,     _a="<unk>"		,     _a="<pad>"		,     _a="<mask>"		,     **_a		,     ):
  				# Mask token behave like a normal word, i.e. include the space before it
  				__magic_name__	:	List[str]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else mask_token
  
  				super().__init__(
  				    _a		,     tokenizer_file=_a		,     bos_token=_a		,     eos_token=_a		,     sep_token=_a		,     cls_token=_a		,     unk_token=_a		,     pad_token=_a		,     mask_token=_a		,     **_a		,     )
  
  				__magic_name__	:	Optional[int]			     =  vocab_file
  				__magic_name__	:	List[str]			     =  False if not self.vocab_file else True
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  
  				if token_ids_a is None:
  					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				__magic_name__	:	Union[str, Any]			     =  [self.cls_token_id]
  				__magic_name__	:	str			     =  [self.sep_token_id]
  				return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	List[str]			     =  [self.sep_token_id]
  				__magic_name__	:	List[str]			     =  [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]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				if not self.can_save_slow_tokenizer:
  					raise ValueError(
  					    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
  					    "tokenizer."	)
  
  				if not os.path.isdir(_a	):
  					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''	)
  					return
  				__magic_name__	:	Any			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  
  				if os.path.abspath(self.vocab_file	) != os.path.abspath(_a	):
  					copyfile(self.vocab_file		,     _a	)
  
  				return (out_vocab_file,)
  