



from __future__ import annotations

from typing import Any







class 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			str				,		lowerCAmelCase							:			int = 6)				->				None:

    """simple docstring"""







    _snake_case    :  Node | None					=					None
    _snake_case    :  Node | None					=					None
    self.create_linked_list(lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			int)				->				None:

    """simple docstring"""







    _snake_case    :  Any					=					Node()
    _snake_case    :  List[Any]					=					current_node
    _snake_case    :  Union[str, Any]					=					current_node
    _snake_case    :  str					=					current_node
    for _ in range(1				,		lowerCAmelCase):
     _snake_case    :  Optional[int]					=					Node()
     _snake_case    :  Dict					=					current_node
     _snake_case    :  Optional[Any]					=					previous_node
     _snake_case    :  Tuple					=					current_node
    _snake_case    :  List[str]					=					self.front
    _snake_case    :  Optional[Any]					=					previous_node

   def      UpperCamelCase_  (					self							:			str)				->				bool:

    """simple docstring"""







    return (
        self.front == self.rear
        and self.front is not None
        and self.front.data is None
    )

   def      UpperCamelCase_  (					self							:			Any)				->				Any | None:

    """simple docstring"""







    self.check_can_perform_operation()
    return self.front.data if self.front else None

   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			Any)				->				None:

    """simple docstring"""







    if self.rear is None:
     return

    self.check_is_full()
    if not self.is_empty():
     _snake_case    :  Optional[Any]					=					self.rear.next
    if self.rear:
     _snake_case    :  Tuple					=					data

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Any:

    """simple docstring"""







    self.check_can_perform_operation()
    if self.rear is None or self.front is None:
     return None
    if self.front == self.rear:
     _snake_case    :  List[str]					=					self.front.data
     _snake_case    :  Union[str, Any]					=					None
     return data

    _snake_case    :  List[Any]					=					self.front
    _snake_case    :  List[str]					=					old_front.next
    _snake_case    :  str					=					old_front.data
    _snake_case    :  Dict					=					None
    return data

   def      UpperCamelCase_  (					self							:			str)				->				None:

    """simple docstring"""







    if self.is_empty():
     raise Exception("""Empty Queue""")


   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				None:

    """simple docstring"""







    if self.rear and self.rear.next == self.front:
     raise Exception("""Full Queue""")







class 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			Any)				->				None:

    """simple docstring"""







    _snake_case    :  Any | None					=					None
    _snake_case    :  Node | None					=					None
    _snake_case    :  Node | None					=					None


if __name__ == "__main__":
  import doctest

  doctest.testmod()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__    =	{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CanineForMultipleChoice""",
      """CanineForQuestionAnswering""",
      """CanineForSequenceClassification""",
      """CanineForTokenClassification""",
      """CanineLayer""",
      """CanineModel""",
      """CaninePreTrainedModel""",
      """load_tf_weights_in_canine""",
  ]


if TYPE_CHECKING:
  from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
  from .tokenization_canine import CanineTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_canine import (
        CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
        CanineForMultipleChoice,
        CanineForQuestionAnswering,
        CanineForSequenceClassification,
        CanineForTokenClassification,
        CanineLayer,
        CanineModel,
        CaninePreTrainedModel,
        load_tf_weights_in_canine,
    )


else:
  import sys

  a__    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
