





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


__UpperCAmelCase       :   Union[str, Any]				       =							logging.get_logger(__name__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     ["""pixel_values"""]




       def __init__(  self :    str							,				A :    bool = True							,				A :    Dict[str, int] = None							,				A :    int = 0.9							,				A :    PILImageResampling = PILImageResampling.BICUBIC							,				A :    bool = True							,				A :    Dict[str, int] = None							,				A :    Union[int, float] = 1 / 255							,				A :    bool = True							,				A :    bool = True							,				A :    Optional[Union[float, List[float]]] = None							,				A :    Optional[Union[float, List[float]]] = None							,				**A :    Tuple							,				):
         super().__init__(**A		)
         __snake_case:						Optional[Any]   =						size if size is not None else {"""shortest_edge""": 224}
         __snake_case:						str   =						get_size_dict(A							,				default_to_square=A		)
         __snake_case:						List[Any]   =						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
         __snake_case:						Dict   =						get_size_dict(A							,				param_name="""crop_size"""		)

         __snake_case:						Union[str, Any]   =						do_resize
         __snake_case:						int   =						size
         __snake_case:						List[str]   =						crop_pct
         __snake_case:						int   =						resample
         __snake_case:						Union[str, Any]   =						do_center_crop
         __snake_case:						Tuple   =						crop_size
         __snake_case:						str   =						do_rescale
         __snake_case:						List[Any]   =						rescale_factor
         __snake_case:						Union[str, Any]   =						do_normalize
         __snake_case:						List[str]   =						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __snake_case:						Optional[Any]   =						image_std if image_std is not None else IMAGENET_DEFAULT_STD




       def 							UpperCAmelCase__ (  self :    str							,				A :    np.ndarray							,				A :    Dict[str, int]							,				A :    Optional[float] = None							,				A :    PILImageResampling = PILImageResampling.BICUBIC							,				A :    Optional[Union[str, ChannelDimension]] = None							,				**A :    Optional[int]							,				):
         __snake_case:						int   =						get_size_dict(A							,				default_to_square=A		)
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
           raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''		)
         if crop_pct is not None:
           if "shortest_edge" in size:
             __snake_case:						Optional[Any]   =						int(size["""shortest_edge"""] / crop_pct		)
           elif "height" in size and "width" in size:
             if size["height"] == size["width"]:
               __snake_case:						List[str]   =						int(size["""height"""] / crop_pct		)
             else:
               __snake_case:						Dict   =						(int(size["""height"""] / crop_pct		), int(size["""width"""] / crop_pct		))
           else:
             raise ValueError("""Invalid size for resize: {}""".format(A		)		)

           __snake_case:						Tuple   =						get_resize_output_image_size(A							,				size=A							,				default_to_square=A		)
         else:
           if "shortest_edge" in size:
             __snake_case:						int   =						get_resize_output_image_size(A							,				size=size["""shortest_edge"""]							,				default_to_square=A		)
           elif "height" in size and "width" in size:
             __snake_case:						Dict   =						(size["""height"""], size["""width"""])
           else:
             raise ValueError("""Invalid size for resize: {}""".format(A		)		)

         return resize(A							,				size=A							,				resample=A							,				data_format=A							,				**A		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    np.ndarray							,				A :    Dict[str, int]							,				A :    Optional[Union[str, ChannelDimension]] = None							,				**A :    Optional[Any]							,				):
         __snake_case:						Union[str, Any]   =						get_size_dict(A		)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''		)
         return center_crop(A							,				size=(size["""height"""], size["""width"""])							,				data_format=A							,				**A		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    np.ndarray							,				A :    Union[int, float]							,				A :    Optional[Union[str, ChannelDimension]] = None							,				**A :    int							,				):
         return rescale(A							,				scale=A							,				data_format=A							,				**A		)




       def 							UpperCAmelCase__ (  self :    Tuple							,				A :    np.ndarray							,				A :    Union[float, List[float]]							,				A :    Union[float, List[float]]							,				A :    Optional[Union[str, ChannelDimension]] = None							,				**A :    int							,				):
         return normalize(A							,				mean=A							,				std=A							,				data_format=A							,				**A		)






       def 							UpperCAmelCase__ (  self :    Dict							,				A :    ImageInput							,				A :    bool = None							,				A :    Dict[str, int] = None							,				A :    int = None							,				A :    PILImageResampling = None							,				A :    bool = None							,				A :    Dict[str, int] = None							,				A :    bool = None							,				A :    float = None							,				A :    bool = None							,				A :    Optional[Union[float, List[float]]] = None							,				A :    Optional[Union[float, List[float]]] = None							,				A :    Optional[Union[str, TensorType]] = None							,				A :    ChannelDimension = ChannelDimension.FIRST							,				**A :    int							,				):
         __snake_case:						List[str]   =						do_resize if do_resize is not None else self.do_resize
         __snake_case:						Tuple   =						crop_pct if crop_pct is not None else self.crop_pct
         __snake_case:						int   =						resample if resample is not None else self.resample
         __snake_case:						int   =						do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case:						Optional[Any]   =						do_rescale if do_rescale is not None else self.do_rescale
         __snake_case:						Optional[int]   =						rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case:						int   =						do_normalize if do_normalize is not None else self.do_normalize
         __snake_case:						Any   =						image_mean if image_mean is not None else self.image_mean
         __snake_case:						Optional[Any]   =						image_std if image_std is not None else self.image_std

         __snake_case:						int   =						size if size is not None else self.size
         __snake_case:						List[str]   =						get_size_dict(A							,				default_to_square=A		)
         __snake_case:						Optional[int]   =						crop_size if crop_size is not None else self.crop_size
         __snake_case:						Optional[int]   =						get_size_dict(A							,				param_name="""crop_size"""		)

         __snake_case:						Tuple   =						make_list_of_images(A		)

         if not valid_images(A		):
           raise ValueError(
               """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
               """torch.Tensor, tf.Tensor or jax.ndarray."""		)

         if do_resize and size is None or resample is None:
           raise ValueError("""Size and resample must be specified if do_resize is True."""		)

         if do_center_crop and crop_pct is None:
           raise ValueError("""Crop_pct must be specified if do_center_crop is True."""		)

         if do_rescale and rescale_factor is None:
           raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

         if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("""Image mean and std must be specified if do_normalize is True."""		)

         # All transformations expect numpy arrays.
         __snake_case:						List[str]   =						[to_numpy_array(A		) for image in images]

         if do_resize:
           __snake_case:						List[str]   =						[self.resize(image=A							,				size=A							,				crop_pct=A							,				resample=A		) for image in images]

         if do_center_crop:
           __snake_case:						Tuple   =						[self.center_crop(image=A							,				size=A		) for image in images]

         if do_rescale:
           __snake_case:						Union[str, Any]   =						[self.rescale(image=A							,				scale=A		) for image in images]

         if do_normalize:
           __snake_case:						Union[str, Any]   =						[self.normalize(image=A							,				mean=A							,				std=A		) for image in images]

         __snake_case:						Dict   =						[to_channel_dimension_format(A							,				A		) for image in images]

         __snake_case:						List[Any]   =						{"""pixel_values""": images}
         return BatchFeature(data=A							,				tensor_type=A		)













from statistics import mean

import numpy as np







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						List[Any]   =						0
  # Number of processes finished
  __snake_case:						Union[str, Any]   =						0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  __snake_case:						Dict   =						[0] * no_of_process
  # List to include calculation results
  __snake_case:						Tuple   =						[0] * no_of_process

  # Sort by arrival time.
  __snake_case:						int   =						[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  __snake_case:						Any   =						[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  arrival_time.sort()

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

    __snake_case:						List[Any]   =						0
    # Index showing the location of the process being performed
    __snake_case:						Union[str, Any]   =						0
    # Saves the current response ratio.
    __snake_case:						Optional[Any]   =						0
    for i in range(0					,					SCREAMING_SNAKE_CASE__):
      if finished_process[i] == 0 and arrival_time[i] <= current_time:
        __snake_case:						Tuple   =						(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
            i
        ]
      if response_ratio < temp:
        __snake_case:						Union[str, Any]   =						temp
        __snake_case:						Optional[int]   =						i

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

  return turn_around_time







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						Union[str, Any]   =						[0] * no_of_process
  for i in range(0					,					SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]   =						turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[Any]				       =							5
      __UpperCAmelCase       :   Tuple				       =							["A", "B", "C", "D", "E"]
      __UpperCAmelCase       :   str				       =							[1, 2, 3, 4, 5]
      __UpperCAmelCase       :   Dict				       =							[1, 2, 3, 4, 5]

      __UpperCAmelCase       :   List[str]				       =							calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      __UpperCAmelCase       :   List[str]				       =							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}')






