code: 
  
  
  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:
  		lowercase_								=    None
  
  
  lowercase_								=    logging.get_logger(__name__)
  
  lowercase_								=    {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowercase_								=    {
      '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'
          ),
      },
  }
  
  lowercase_								=    {
      'xlm-roberta-base': 5_12,
      'xlm-roberta-large': 5_12,
      'xlm-roberta-large-finetuned-conll02-dutch': 5_12,
      'xlm-roberta-large-finetuned-conll02-spanish': 5_12,
      'xlm-roberta-large-finetuned-conll03-english': 5_12,
      'xlm-roberta-large-finetuned-conll03-german': 5_12,
  }
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  VOCAB_FILES_NAMES
  							_A  				=  PRETRAINED_VOCAB_FILES_MAP
  							_A  				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  							_A  				=  ["input_ids", "attention_mask"]
  							_A  				=  XLMRobertaTokenizer
  
  
  
  
  
  
  
  							def __init__(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Dict=None		,				SCREAMING_SNAKE_CASE_  :				int=None		,				SCREAMING_SNAKE_CASE_  :				Tuple="<s>"		,				SCREAMING_SNAKE_CASE_  :				List[Any]="</s>"		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]="</s>"		,				SCREAMING_SNAKE_CASE_  :				List[str]="<s>"		,				SCREAMING_SNAKE_CASE_  :				Any="<unk>"		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]="<pad>"		,				SCREAMING_SNAKE_CASE_  :				Any="<mask>"		,				**SCREAMING_SNAKE_CASE_  :				Tuple		,				):
  													# Mask token behave like a normal word, i.e. include the space before it
  													_a     =			AddedToken(UpperCamelCase_		,				lstrip=UpperCamelCase_		,				rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_		,				UpperCamelCase_		) else mask_token
  
  													super().__init__(
  													    UpperCamelCase_		,				tokenizer_file=UpperCamelCase_		,				bos_token=UpperCamelCase_		,				eos_token=UpperCamelCase_		,				sep_token=UpperCamelCase_		,				cls_token=UpperCamelCase_		,				unk_token=UpperCamelCase_		,				pad_token=UpperCamelCase_		,				mask_token=UpperCamelCase_		,				**UpperCamelCase_		,				)
  
  													_a     =			vocab_file
  													_a     =			False if not self.vocab_file else True
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		):
  
  													if token_ids_a is None:
  																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  													_a     =			[self.cls_token_id]
  													_a     =			[self.sep_token_id]
  													return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		):
  													_a     =			[self.sep_token_id]
  													_a     =			[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 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Optional[str] = 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(UpperCamelCase_		):
  																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""		)
  																			return
  													_a     =			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_		):
  																			copyfile(self.vocab_file		,				UpperCamelCase_		)
  
  													return (out_vocab_file,)
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  lowercase_								=    {
      'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
      'tokenization_codegen': ['CodeGenTokenizer'],
  }
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    ['CodeGenTokenizerFast']
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    [
  		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
  		    'CodeGenForCausalLM',
  		    'CodeGenModel',
  		    'CodeGenPreTrainedModel',
  		]
  
  if TYPE_CHECKING:
  		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  		from .tokenization_codegen import CodeGenTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_codegen_fast import CodeGenTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_codegen import (
  				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    CodeGenForCausalLM,
  				    CodeGenModel,
  				    CodeGenPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  