from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class        UpperCAmelCase_					(				_a     ):
    UpperCAmelCase_      :							List[str]    			=				['image_processor', 'tokenizer']
    UpperCAmelCase_      :							List[Any]    			=				'EfficientNetImageProcessor'
    UpperCAmelCase_      :							Any    			=				('BertTokenizer', 'BertTokenizerFast')




    def __init__(       self	: List[str]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: int )						->			Optional[Any]:
      super().__init__(lowerCAmelCase   ,   lowerCAmelCase )




    def __call__(       self	: int   ,   lowerCAmelCase	: str=None   ,   lowerCAmelCase	: Optional[int]=None   ,   lowerCAmelCase	: Any="max_length"   ,   lowerCAmelCase	: Any=64   ,   lowerCAmelCase	: Optional[int]=None   ,   **lowerCAmelCase	: Tuple )						->			Tuple:
      if text is None and images is None:
        raise ValueError("You have to specify either text or images. Both cannot be none." )

      if text is not None:
        a__  		=  self.tokenizer(
            lowerCAmelCase   ,   padding=lowerCAmelCase   ,   max_length=lowerCAmelCase   ,   return_tensors=lowerCAmelCase   ,   **lowerCAmelCase )

      if images is not None:
        a__  		=  self.image_processor(lowerCAmelCase   ,   return_tensors=lowerCAmelCase   ,   **lowerCAmelCase )

      if text is not None and images is not None:
        a__  		=  image_features.pixel_values
        return encoding
      elif text is not None:
        return encoding
      else:
        return BatchEncoding(data=dict(**lowerCAmelCase )   ,   tensor_type=lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Union[str, Any]   ,   *lowerCAmelCase	: Optional[int]   ,   **lowerCAmelCase	: List[Any] )						->			str:
      return self.tokenizer.batch_decode(*lowerCAmelCase   ,   **lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Any   ,   *lowerCAmelCase	: int   ,   **lowerCAmelCase	: Dict )						->			Dict:
      return self.tokenizer.decode(*lowerCAmelCase   ,   **lowerCAmelCase )




    @property
    def     __lowerCamelCase			(       self	: int )						->			Optional[Any]:
      a__  		=  self.tokenizer.model_input_names
      a__  		=  self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )


from __future__ import annotations

from statistics import mean
def 					__lowercase						(    lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  a__  		=  2
  a__  		=  list(lowerCamelCase__       )
  a__  		=  [0] * len(lowerCamelCase__       )
  a__  		=  0
  while True:
    a__  		=  True
    for i, burst_time in enumerate(lowerCamelCase__       ):
      if rem_burst_times[i] > 0:
        a__  		=  False
        if rem_burst_times[i] > quantum:
          t += quantum
          rem_burst_times[i] -= quantum
        else:
          t += rem_burst_times[i]
          a__  		=  t - burst_time
          a__  		=  0
    if done is True:
      return waiting_times
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  return [burst + waiting for burst, waiting in zip(lowerCamelCase__			,					lowerCamelCase__       )]


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     [3, 5, 7]
     _SCREAMING_SNAKE_CASE     =     calculate_waiting_times(burst_times)
     _SCREAMING_SNAKE_CASE     =     calculate_turn_around_times(burst_times, waiting_times)
     print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
     for i, burst_time in enumerate(burst_times):
          print(
              F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
              F"""{turn_around_times[i]}"""
          )
     print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
     print(F"""Average turn around time = {mean(turn_around_times):.5f}""")

