



from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						"MarkupLMFeatureExtractor"
   A__      : Dict       =						("MarkupLMTokenizer", "MarkupLMTokenizerFast")
   A__      : int       =						True


   def __call__(    self:     str  ,lowerCamelCase_:     Tuple=None  ,lowerCamelCase_:     int=None  ,lowerCamelCase_:     List[Any]=None  ,lowerCamelCase_:     List[Any]=None  ,lowerCamelCase_:     Any=None  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Union[bool, str, PaddingStrategy] = False  ,lowerCamelCase_:     Union[bool, str, TruncationStrategy] = None  ,lowerCamelCase_:     Optional[int] = None  ,lowerCamelCase_:     int = 0  ,lowerCamelCase_:     Optional[int] = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     bool = False  ,lowerCamelCase_:     bool = False  ,lowerCamelCase_:     bool = False  ,lowerCamelCase_:     bool = False  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Union[str, TensorType]] = None  ,**lowerCamelCase_:     Optional[int]  ,)	->      BatchEncoding:
       # first, create nodes and xpaths
       if self.parse_html:
           if html_strings is None:
               raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""							)

           if nodes is not None or xpaths is not None or node_labels is not None:
               raise ValueError(
                   """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""							)

           UpperCAmelCase_     :       Any  		=					self.feature_extractor(lowerCamelCase_							)
           UpperCAmelCase_     :       List[str]  		=					features["""nodes"""]
           UpperCAmelCase_     :       int  		=					features["""xpaths"""]
       else:
           if html_strings is not None:
               raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""							)
           if nodes is None or xpaths is None:
               raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""							)

        # # second, apply the tokenizer
       if questions is not None and self.parse_html:
           if isinstance(lowerCamelCase_  ,lowerCamelCase_							):
               UpperCAmelCase_     :       Optional[Any]  		=					[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       UpperCAmelCase_     :       Any  		=					self.tokenizer(
           text=questions if questions is not None else nodes  ,text_pair=nodes if questions is not None else None  ,xpaths=lowerCamelCase_  ,node_labels=lowerCamelCase_  ,add_special_tokens=lowerCamelCase_  ,padding=lowerCamelCase_  ,truncation=lowerCamelCase_  ,max_length=lowerCamelCase_  ,stride=lowerCamelCase_  ,pad_to_multiple_of=lowerCamelCase_  ,return_token_type_ids=lowerCamelCase_  ,return_attention_mask=lowerCamelCase_  ,return_overflowing_tokens=lowerCamelCase_  ,return_special_tokens_mask=lowerCamelCase_  ,return_offsets_mapping=lowerCamelCase_  ,return_length=lowerCamelCase_  ,verbose=lowerCamelCase_  ,return_tensors=lowerCamelCase_  ,**lowerCamelCase_  ,)

       return encoded_inputs


   def       A__    (    self:     Any  ,*lowerCamelCase_:     Dict  ,**lowerCamelCase_:     Union[str, Any]							)	->      Any:
       return self.tokenizer.batch_decode(*lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     Tuple  ,*lowerCamelCase_:     Dict  ,**lowerCamelCase_:     str							)	->      Tuple:
       return self.tokenizer.decode(*lowerCamelCase_  ,**lowerCamelCase_							)






   @property
   def       A__    (    self:     List[Any]							)	->      List[Any]:
       UpperCAmelCase_     :       List[str]  		=					self.tokenizer.model_input_names
       return tokenizer_input_names


"""simple docstring"""
from __future__ import annotations

from statistics import mean
def 					a__ ( a :					list[int]		):

  """simple docstring"""







  _snake_case  :    List[str]            = 2
  _snake_case  :    Union[str, Any]            = list(a		)
  _snake_case  :    Any            = [0] * len(a		)
  _snake_case  :    Any            = 0
  while True:
    _snake_case  :    str            = True
    for i, burst_time in enumerate(a		):
      if rem_burst_times[i] > 0:
        _snake_case  :    str            = False
        if rem_burst_times[i] > quantum:
          t += quantum
          rem_burst_times[i] -= quantum
        else:
          t += rem_burst_times[i]
          _snake_case  :    int            = t - burst_time
          _snake_case  :    Dict            = 0
    if done is True:
      return waiting_times
def 					a__ ( a :					list[int]							,  a :					list[int]		):

  """simple docstring"""







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


if __name__ == "__main__":
    _a   :      Dict       =				[3, 5, 7]
    _a   :      Dict       =				calculate_waiting_times(burst_times)
    _a   :      Tuple       =				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}')

