


'''simple docstring'''






from __future__ import annotations

from typing import Any






class    __magic_name__				:






       def __init__(     self    ,				snake_case = 6)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  Node | None				       =None
          _UpperCAmelCase  :  Node | None				       =None
          self.create_linked_list(snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =Node()
          _UpperCAmelCase  :  str				       =current_node
          _UpperCAmelCase  :  Optional[int]				       =current_node
          _UpperCAmelCase  :  Tuple				       =current_node
          for _ in range(1    ,				snake_case):
             _UpperCAmelCase  :  Optional[Any]				       =Node()
             _UpperCAmelCase  :  Optional[int]				       =current_node
             _UpperCAmelCase  :  List[Any]				       =previous_node
             _UpperCAmelCase  :  Optional[Any]				       =current_node
          _UpperCAmelCase  :  List[str]				       =self.front
          _UpperCAmelCase  :  Optional[Any]				       =previous_node






       def      lowerCAmelCase						(     self)		->    bool:




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






       def      lowerCAmelCase						(     self)		->    Any | None:




          '''simple docstring'''
          self.check_can_perform_operation()
          return self.front.data if self.front else None






       def      lowerCAmelCase						(     self    ,				snake_case)		->    None:




          '''simple docstring'''
          if self.rear is None:
             return

          self.check_is_full()
          if not self.is_empty():
             _UpperCAmelCase  :  str				       =self.rear.next
          if self.rear:
             _UpperCAmelCase  :  Optional[int]				       =data






       def      lowerCAmelCase						(     self)		->    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:
             _UpperCAmelCase  :  Optional[Any]				       =self.front.data
             _UpperCAmelCase  :  List[Any]				       =None
             return data

          _UpperCAmelCase  :  Union[str, Any]				       =self.front
          _UpperCAmelCase  :  Optional[int]				       =old_front.next
          _UpperCAmelCase  :  Tuple				       =old_front.data
          _UpperCAmelCase  :  str				       =None
          return data






       def      lowerCAmelCase						(     self)		->    None:




          '''simple docstring'''
          if self.is_empty():
             raise Exception('Empty Queue')






       def      lowerCAmelCase						(     self)		->    None:




          '''simple docstring'''
          if self.rear and self.rear.next == self.front:
             raise Exception('Full Queue')






class    __magic_name__				:






       def __init__(     self)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  Any | None				       =None
          _UpperCAmelCase  :  Node | None				       =None
          _UpperCAmelCase  :  Node | None				       =None


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

 lowercase				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

