


from __future__ import annotations

from typing import Any






class _lowerCamelCase:
       def __init__(      self,   lowerCamelCase = 6) ->     None:




           """simple docstring"""

           _lowercase :							Node | None   	=	None
           _lowercase :							Node | None   	=	None
           self.create_linked_list(lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     None:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	Node()
           _lowercase :							List[Any]   	=	current_node
           _lowercase :							str   	=	current_node
           _lowercase :							int   	=	current_node
           for _ in range(1,   lowerCamelCase):
               _lowercase :							Tuple   	=	Node()
               _lowercase :							int   	=	current_node
               _lowercase :							Any   	=	previous_node
               _lowercase :							Any   	=	current_node
           _lowercase :							str   	=	self.front
           _lowercase :							List[str]   	=	previous_node
       def     UpperCamelCase  (      self) ->     bool:




           """simple docstring"""

           return (
               self.front == self.rear
               and self.front is not None
               and self.front.data is None
           )
       def     UpperCamelCase  (      self) ->     Any | None:




           """simple docstring"""

           self.check_can_perform_operation()
           return self.front.data if self.front else None
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     None:




           """simple docstring"""

           if self.rear is None:
               return

           self.check_is_full()
           if not self.is_empty():
               _lowercase :							List[str]   	=	self.rear.next
           if self.rear:
               _lowercase :							Any   	=	data
       def     UpperCamelCase  (      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:
               _lowercase :							Union[str, Any]   	=	self.front.data
               _lowercase :							Union[str, Any]   	=	None
               return data

           _lowercase :							Dict   	=	self.front
           _lowercase :							str   	=	old_front.next
           _lowercase :							List[Any]   	=	old_front.data
           _lowercase :							Any   	=	None
           return data
       def     UpperCamelCase  (      self) ->     None:




           """simple docstring"""

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




       def     UpperCamelCase  (      self) ->     None:




           """simple docstring"""

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






class _lowerCamelCase:
       def __init__(      self) ->     None:




           """simple docstring"""

           _lowercase :							Any | None   	=	None
           _lowercase :							Node | None   	=	None
           _lowercase :							Node | None   	=	None


if __name__ == "__main__":
 import doctest

 doctest.testmod()






from typing import Dict

from .base import GenericTensor, Pipeline






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     int:




           """simple docstring"""

           if tokenize_kwargs is None:
               _lowercase :							Any   	=	{}

           if truncation is not None:
               if "truncation" in tokenize_kwargs:
                   raise ValueError(
                       'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)')
               _lowercase :							List[Any]   	=	truncation

           _lowercase :							Optional[Any]   	=	tokenize_kwargs

           _lowercase :							Tuple   	=	{}
           if return_tensors is not None:
               _lowercase :							Union[str, Any]   	=	return_tensors

           return preprocess_params, {}, postprocess_params
       def     UpperCamelCase  (      self,   lowerCamelCase,   **lowerCamelCase) ->     Dict[str, GenericTensor]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.framework
           _lowercase :							Dict   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)
           return model_inputs
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							str   	=	self.model(**lowerCamelCase)
           return model_outputs
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=False) ->     Dict:




           """simple docstring"""

           if return_tensors:
               return model_outputs[0]
           if self.framework == "pt":
               return model_outputs[0].tolist()
           elif self.framework == "tf":
               return model_outputs[0].numpy().tolist()




       def __call__(      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return super().__call__(*lowerCamelCase,   **lowerCamelCase)


