code: 
  """simple docstring"""
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _a	 =							{
      'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
      'tokenization_led': ['LEDTokenizer'],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _a	 =							['LEDTokenizerFast']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _a	 =							[
          'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LEDForConditionalGeneration',
          'LEDForQuestionAnswering',
          'LEDForSequenceClassification',
          'LEDModel',
          'LEDPreTrainedModel',
      ]
  
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _a	 =							['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']
  
  
  if TYPE_CHECKING:
      from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
      from .tokenization_led import LEDTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_led_fast import LEDTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_led import (
              LED_PRETRAINED_MODEL_ARCHIVE_LIST,
              LEDForConditionalGeneration,
              LEDForQuestionAnswering,
              LEDForSequenceClassification,
              LEDModel,
              LEDPreTrainedModel,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel
  
  else:
      import sys
  
      _a	 =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from hashlib import mda, shaaaa
  
  _a	 =							(shaaaa, mda)
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_ = 8						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					0B0
       UpperCAmelCase_      :      Union[str, Any]      =					size
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.hash_(lowercase_						)
       self.bitarray |= h
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					self.hash_(lowercase_						)
       return (h & self.bitarray) == h
  
  
  
  
  
  
  
    def __contains__(	self       ,     lowercase_						):
  
       """simple docstring"""
       return self.exists(lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					bin(lowercase_						)[2:]
       return res.zfill(self.size						)
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return self.format_bin(self.bitarray						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					0B0
       for func in HASH_FUNCTIONS:
          UpperCAmelCase_      :      str      =					(
              int.from_bytes(func(value.encode()						).digest()       ,     "little"						) % self.size
          )
          res |= 2**position
       return res
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       return self.format_bin(self.hash_(lowercase_						)						)
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					bin(self.bitarray						).count("1"						)
       return (n_ones / self.size) ** len(lowercase_						)
  
  
  