code: 
  """simple docstring"""
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _a	 =							{
      'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
  }
  
  try:
      if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _a	 =							['CamembertTokenizer']
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _a	 =							['CamembertTokenizerFast']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _a	 =							[
          'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'CamembertForCausalLM',
          'CamembertForMaskedLM',
          'CamembertForMultipleChoice',
          'CamembertForQuestionAnswering',
          'CamembertForSequenceClassification',
          'CamembertForTokenClassification',
          'CamembertModel',
          'CamembertPreTrainedModel',
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _a	 =							[
          'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFCamembertForCausalLM',
          'TFCamembertForMaskedLM',
          'TFCamembertForMultipleChoice',
          'TFCamembertForQuestionAnswering',
          'TFCamembertForSequenceClassification',
          'TFCamembertForTokenClassification',
          'TFCamembertModel',
          'TFCamembertPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
      try:
          if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_camembert import CamembertTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_camembert_fast import CamembertTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_camembert import (
              CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              CamembertForCausalLM,
              CamembertForMaskedLM,
              CamembertForMultipleChoice,
              CamembertForQuestionAnswering,
              CamembertForSequenceClassification,
              CamembertForTokenClassification,
              CamembertModel,
              CamembertPreTrainedModel,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_camembert import (
              TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCamembertForCausalLM,
              TFCamembertForMaskedLM,
              TFCamembertForMultipleChoice,
              TFCamembertForQuestionAnswering,
              TFCamembertForSequenceClassification,
              TFCamembertForTokenClassification,
              TFCamembertModel,
              TFCamembertPreTrainedModel,
          )
  
  else:
      import sys
  
      _a	 =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from __future__ import annotations
  
  from typing import Any
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_       ,     lowercase_						):  # noqa: A002
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					item
       UpperCAmelCase_      :      Optional[Any]      =					next
  
  
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Node | None      =					None
       UpperCAmelCase_      :      Optional[int]      =					0
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					Node(lowercase_       ,     self.head						)
       self.size += 1
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       # Switched 'self.is_empty()' to 'self.head is None'
       # because mypy was considering the possibility that 'self.head'
       # can be None in below else part and giving error
       if self.head is None:
          return None
       else:
          UpperCAmelCase_      :      int      =					self.head.item
          UpperCAmelCase_      :      str      =					self.head.next
          self.size -= 1
          return item
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return self.head is None
  
  
  
  
  
  
  
    def __str__(	self						):
  
       """simple docstring"""
       if self.is_empty():
          return ""
       else:
          UpperCAmelCase_      :      str      =					self.head
          UpperCAmelCase_      :      str      =					""
          UpperCAmelCase_      :      list[str]      =					[]
          while iterate:
             item_list.append(str(iterate.item						)						)
             UpperCAmelCase_      :      Tuple      =					iterate.next
  
          UpperCAmelCase_      :      Optional[Any]      =					" --> ".join(lowercase_						)
  
          return item_str
  
  
  
  
  
  
  
    def __len__(	self						):
  
       """simple docstring"""
       return self.size
  
  
  