




'''simple docstring'''





from typing import TYPE_CHECKING

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


a		:	List[str]       =      {
    '''configuration_openai''': ['''OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenAIGPTConfig'''],
    '''tokenization_openai''': ['''OpenAIGPTTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:	int       =      ['''OpenAIGPTTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:	Dict       =      [
      '''OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''OpenAIGPTDoubleHeadsModel''',
      '''OpenAIGPTForSequenceClassification''',
      '''OpenAIGPTLMHeadModel''',
      '''OpenAIGPTModel''',
      '''OpenAIGPTPreTrainedModel''',
      '''load_tf_weights_in_openai_gpt''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:	Optional[int]       =      [
      '''TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFOpenAIGPTDoubleHeadsModel''',
      '''TFOpenAIGPTForSequenceClassification''',
      '''TFOpenAIGPTLMHeadModel''',
      '''TFOpenAIGPTMainLayer''',
      '''TFOpenAIGPTModel''',
      '''TFOpenAIGPTPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .tokenization_openai import OpenAIGPTTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_openai_fast import OpenAIGPTTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_openai import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
        OpenAIGPTPreTrainedModel,
        load_tf_weights_in_openai_gpt,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
        TFOpenAIGPTForSequenceClassification,
        TFOpenAIGPTLMHeadModel,
        TFOpenAIGPTMainLayer,
        TFOpenAIGPTModel,
        TFOpenAIGPTPreTrainedModel,
    )

else:
  import sys

  a		:	Dict       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








'''simple docstring'''





import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  a		:	Optional[int]       =      None


a		:	Optional[Any]       =      logging.get_logger(__name__)


a		:	Union[str, Any]       =      '''▁'''

a		:	Any       =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:	List[Any]       =      {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

a		:	Any       =      {
    '''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}




class      UpperCamelCase       (	snake_case__       ):
  a     :str          =					VOCAB_FILES_NAMES
  a     :List[Any]          =					PRETRAINED_VOCAB_FILES_MAP
  a     :Optional[int]          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     :Any          =					['input_ids', 'attention_mask']
  a     :int          =					ReformerTokenizer



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase="</s>"		,     __lowerCAmelCase="<unk>"		,     __lowerCAmelCase=[]		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__(
          __lowerCAmelCase		,     tokenizer_file=__lowerCAmelCase		,     eos_token=__lowerCAmelCase		,     unk_token=__lowerCAmelCase		,     additional_special_tokens=__lowerCAmelCase		,     **__lowerCAmelCase		,     )

      _a				:							List[str]							       =      vocab_file
      _a				:							Optional[int]							       =      False if not self.vocab_file else True



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      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(__lowerCAmelCase ):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
          return
      _a				:							List[str]							       =      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 ):
          copyfile(self.vocab_file		,     __lowerCAmelCase )

      return (out_vocab_file,)


