from statistics import mean

import numpy as np







def      _a					(   lowercase__	:      list  ,	lowercase__	:      list  ,	lowercase__	:      list  ,	lowercase__	:      int			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		0
     # Number of processes finished
     SCREAMING_SNAKE_CASE__					:					Tuple      =		0
     # Displays the finished process.
     # If it is 0, the performance is completed if it is 1, before the performance.
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		[0] * no_of_process
     # List to include calculation results
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[0] * no_of_process

     # Sort by arrival time.
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		[burst_time[i] for i in np.argsort(lowercase__			)]
     SCREAMING_SNAKE_CASE__					:					List[str]      =		[process_name[i] for i in np.argsort(lowercase__			)]
     arrival_time.sort()

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

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

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

     return turn_around_time







def      _a					(   lowercase__	:      list  ,	lowercase__	:      list  ,	lowercase__	:      list  ,	lowercase__	:      int			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					str      =		[0] * no_of_process
     for i in range(0  ,	lowercase__			):
          SCREAMING_SNAKE_CASE__					:					Any      =		turn_around_time[i] - burst_time[i]
     return waiting_time


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:       List[str]       						=       5
      SCREAMING_SNAKE_CASE__		:       Optional[int]       						=       ["A", "B", "C", "D", "E"]
      SCREAMING_SNAKE_CASE__		:       Dict       						=       [1, 2, 3, 4, 5]
      SCREAMING_SNAKE_CASE__		:       Dict       						=       [1, 2, 3, 4, 5]

      SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       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}""")











from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

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



def  a__ (__lowercase							:str   )		-> List[List[ImageInput]]:
				if isinstance(__lowercase					,  (list, tuple)   ) and isinstance(videos[0]					,  (list, tuple)   ) and is_valid_image(videos[0][0]   ):
								return videos

				elif isinstance(__lowercase					,  (list, tuple)   ) and is_valid_image(videos[0]   ):
								return [videos]

				elif is_valid_image(__lowercase   ):
								return [[videos]]

				raise ValueError(f"""Could not make batched video from {videos}"""   )






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BILINEAR       ,A__ = True       ,A__ = None       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = True       ,A__ = None       ,A__ = None       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				Tuple   =      size if size is not None else {'''shortest_edge''': 256}
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				str   =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
						_A	:				str   =      get_size_dict(A__       ,param_name='''crop_size'''		)

						_A	:				Tuple   =      do_resize
						_A	:				Optional[Any]   =      size
						_A	:				Optional[Any]   =      do_center_crop
						_A	:				List[str]   =      crop_size
						_A	:				Dict   =      resample
						_A	:				Tuple   =      do_rescale
						_A	:				int   =      rescale_factor
						_A	:				List[str]   =      offset
						_A	:				List[Any]   =      do_normalize
						_A	:				List[Any]   =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_A	:				List[str]   =      image_std if image_std is not None else IMAGENET_STANDARD_STD







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BILINEAR       ,A__ = None       ,**A__       ,):
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						if "shortest_edge" in size:
										_A	:				Optional[Any]   =      get_resize_output_image_size(A__       ,size['''shortest_edge''']       ,default_to_square=A__		)
						elif "height" in size and "width" in size:
										_A	:				str   =      (size['''height'''], size['''width'''])
						else:
										raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						_A	:				Optional[int]   =      get_size_dict(A__		)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""		)
						return center_crop(A__       ,size=(size['''height'''], size['''width'''])       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				Any   =      image.astype(np.floataa		)
						if offset:
										_A	:				List[str]   =      image - (scale / 2)
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,):
						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_size is None:
										raise ValueError('''Crop size 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.'''		)

						if offset and not do_rescale:
										raise ValueError('''For offset, do_rescale must also be set to True.'''		)

						# All transformations expect numpy arrays.
						_A	:				Dict   =      to_numpy_array(A__		)

						if do_resize:
										_A	:				Any   =      self.resize(image=A__       ,size=A__       ,resample=A__		)

						if do_center_crop:
										_A	:				str   =      self.center_crop(A__       ,size=A__		)

						if do_rescale:
										_A	:				Optional[int]   =      self.rescale(image=A__       ,scale=A__       ,offset=A__		)

						if do_normalize:
										_A	:				Dict   =      self.normalize(image=A__       ,mean=A__       ,std=A__		)

						_A	:				Union[str, Any]   =      to_channel_dimension_format(A__       ,A__		)
						return image







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				Optional[int]   =      do_resize if do_resize is not None else self.do_resize
						_A	:				List[str]   =      resample if resample is not None else self.resample
						_A	:				Union[str, Any]   =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A	:				Tuple   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				List[str]   =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A	:				List[str]   =      offset if offset is not None else self.offset
						_A	:				Optional[int]   =      do_normalize if do_normalize is not None else self.do_normalize
						_A	:				Optional[Any]   =      image_mean if image_mean is not None else self.image_mean
						_A	:				Dict   =      image_std if image_std is not None else self.image_std

						_A	:				str   =      size if size is not None else self.size
						_A	:				int   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				Any   =      crop_size if crop_size is not None else self.crop_size
						_A	:				Optional[int]   =      get_size_dict(A__       ,param_name='''crop_size'''		)

						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.'''		)

						_A	:				List[str]   =      make_batched(A__		)

						_A	:				Tuple   =      [
						    [
						        self._preprocess_image(
						            image=A__       ,do_resize=A__       ,size=A__       ,resample=A__       ,do_center_crop=A__       ,crop_size=A__       ,do_rescale=A__       ,rescale_factor=A__       ,offset=A__       ,do_normalize=A__       ,image_mean=A__       ,image_std=A__       ,data_format=A__       ,)
						        for img in video
						    ]
						    for video in videos
						]

						_A	:				Optional[int]   =      {'''pixel_values''': videos}
						return BatchFeature(data=A__       ,tensor_type=A__		)



