




'''simple docstring'''

from statistics import mean

import numpy as np

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					list:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       0
   # Number of processes finished
   __SCREAMING_SNAKE_CASE      							=       0
   # Displays the finished process.
   # If it is 0, the performance is completed if it is 1, before the performance.
   __SCREAMING_SNAKE_CASE      							=       [0] * no_of_process
   # List to include calculation results
   __SCREAMING_SNAKE_CASE      							=       [0] * no_of_process

   # Sort by arrival time.
   __SCREAMING_SNAKE_CASE      							=       [burst_time[i] for i in np.argsort(__UpperCAmelCase      )]
   __SCREAMING_SNAKE_CASE      							=       [process_name[i] for i in np.argsort(__UpperCAmelCase      )]
   arrival_time.sort()

   while no_of_process > finished_process_count:
      __SCREAMING_SNAKE_CASE      							=       0
      while finished_process[i] == 1:
         i += 1
      if current_time < arrival_time[i]:
         __SCREAMING_SNAKE_CASE      							=       arrival_time[i]

      __SCREAMING_SNAKE_CASE      							=       0
      # Index showing the location of the process being performed
      __SCREAMING_SNAKE_CASE      							=       0
      # Saves the current response ratio.
      __SCREAMING_SNAKE_CASE      							=       0
      for i in range(0							,	__UpperCAmelCase      ):
         if finished_process[i] == 0 and arrival_time[i] <= current_time:
            __SCREAMING_SNAKE_CASE      							=       (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                i
            ]
         if response_ratio < temp:
            __SCREAMING_SNAKE_CASE      							=       temp
            __SCREAMING_SNAKE_CASE      							=       i

        # Calculate the turn around time
      __SCREAMING_SNAKE_CASE      							=       current_time + burst_time[loc] - arrival_time[loc]
      current_time += burst_time[loc]
      # Indicates that the process has been performed.
      __SCREAMING_SNAKE_CASE      							=       1
      # Increase finished_process_count by 1
      finished_process_count += 1

   return turn_around_time

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					list:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       [0] * no_of_process
   for i in range(0							,	__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       turn_around_time[i] - burst_time[i]
   return waiting_time


if __name__ == "__main__":
    a        =						5
    a        =						["A", "B", "C", "D", "E"]
    a        =						[1, 2, 3, 4, 5]
    a        =						[1, 2, 3, 4, 5]

    a        =						calculate_turn_around_time(
        process_name, arrival_time, burst_time, no_of_process
    )
    a        =						calculate_waiting_time(
        process_name, turn_around_time, burst_time, no_of_process
    )

    print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
    for i in range(0, no_of_process):
        print(
            F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
            F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
        )

    print(F'''average waiting time : {mean(waiting_time):.5f}''')
    print(F'''average turn around time : {mean(turn_around_time):.5f}''')












'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
    from ..tf_utils import stable_softmax

a        =						logging.get_logger(__name__)







@add_end_docstrings(_snake_case			)
class __a (   _snake_case			):





   def __init__(					self				:			Union[str, Any]				,**lowerCamelCase				:			str    ):

      '''simple docstring'''

      super().__init__(**lowerCamelCase    )

      requires_backends(self				,"""vision"""    )
      self.check_model_type(
          TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == """tf"""
          else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )





   def __call__(					self				:			Dict				,lowerCamelCase				:			Union[str, List[str], "Image", List["Image"]]				,**lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,**lowerCamelCase				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       {}
      if "candidate_labels" in kwargs:
         __SCREAMING_SNAKE_CASE      							=       kwargs["""candidate_labels"""]
      if "hypothesis_template" in kwargs:
         __SCREAMING_SNAKE_CASE      							=       kwargs["""hypothesis_template"""]

      return preprocess_params, {}, {}





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			Union[str, Any]=None				,lowerCamelCase				:			Union[str, Any]="This is a photo of {}."    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       load_image(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.image_processor(images=[image]				,return_tensors=self.framework    )
      __SCREAMING_SNAKE_CASE      							=       candidate_labels
      __SCREAMING_SNAKE_CASE      							=       [hypothesis_template.format(lowerCamelCase    ) for x in candidate_labels]
      __SCREAMING_SNAKE_CASE      							=       self.tokenizer(lowerCamelCase				,return_tensors=self.framework				,padding=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       [text_inputs]
      return inputs





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       model_inputs.pop("""candidate_labels"""    )
      __SCREAMING_SNAKE_CASE      							=       model_inputs.pop("""text_inputs"""    )
      if isinstance(text_inputs[0]				,lowerCamelCase    ):
         __SCREAMING_SNAKE_CASE      							=       text_inputs[0]
      else:
         # Batching case.
         __SCREAMING_SNAKE_CASE      							=       text_inputs[0][0]

      __SCREAMING_SNAKE_CASE      							=       self.model(**lowerCamelCase				,**lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       {
          """candidate_labels""": candidate_labels,
          """logits""": outputs.logits_per_image,
      }
      return model_outputs





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       model_outputs.pop("""candidate_labels"""    )
      __SCREAMING_SNAKE_CASE      							=       model_outputs["""logits"""][0]
      if self.framework == "pt":
         __SCREAMING_SNAKE_CASE      							=       logits.softmax(dim=-1    ).squeeze(-1    )
         __SCREAMING_SNAKE_CASE      							=       probs.tolist()
         if not isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       [scores]
      elif self.framework == "tf":
         __SCREAMING_SNAKE_CASE      							=       stable_softmax(lowerCamelCase				,axis=-1    )
         __SCREAMING_SNAKE_CASE      							=       probs.numpy().tolist()
      else:
         raise ValueError(f"""Unsupported framework: {self.framework}"""    )

      __SCREAMING_SNAKE_CASE      							=       [
          {"""score""": score, """label""": candidate_label}
          for score, candidate_label in sorted(zip(lowerCamelCase				,lowerCamelCase    )				,key=lambda lowerCamelCase    : -x[0]    )
      ]
      return result





