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 .hash_table import HashTable
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     *lowercase_       ,     **lowercase_						):
  
       """simple docstring"""
       super().__init__(*lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_=None						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Tuple      =					1
       UpperCAmelCase_      :      List[str]      =					self.hash_function(key + i * i						)
  
       while self.values[new_key] is not None and self.values[new_key] != key:
          i += 1
          UpperCAmelCase_      :      List[Any]      =					(
              self.hash_function(key + i * i						)
              if not self.balanced_factor() >= self.lim_charge
              else None
          )
  
          if new_key is None:
             break
  
       return new_key
  
  
  