




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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase__				:     Dict			       =							logging.get_logger(__name__)





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 lowercase_       					=					["pixel_values"]



 def __init__(							self						:	str       ,       _lowerCAmelCase						:	bool = True       ,       _lowerCAmelCase						:	Optional[Dict[str, int]] = None       ,       _lowerCAmelCase						:	PILImageResampling = PILImageResampling.BILINEAR       ,       _lowerCAmelCase						:	bool = True       ,       _lowerCAmelCase						:	Union[int, float] = 1 / 255       ,       _lowerCAmelCase						:	bool = True       ,       _lowerCAmelCase						:	Optional[Union[float, List[float]]] = None       ,       _lowerCAmelCase						:	Optional[Union[float, List[float]]] = None       ,       **_lowerCAmelCase						:	Tuple       ,       ):
       super().__init__(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    size if size is not None else {'height': 224, 'width': 224}
       SCREAMING_SNAKE_CASE_					=    get_size_dict(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    do_resize
       SCREAMING_SNAKE_CASE_					=    do_rescale
       SCREAMING_SNAKE_CASE_					=    do_normalize
       SCREAMING_SNAKE_CASE_					=    size
       SCREAMING_SNAKE_CASE_					=    resample
       SCREAMING_SNAKE_CASE_					=    rescale_factor
       SCREAMING_SNAKE_CASE_					=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE_					=    image_std if image_std is not None else IMAGENET_STANDARD_STD



 def    lowerCAmelCase_						(							self						:	List[Any]       ,       _lowerCAmelCase						:	np.ndarray       ,       _lowerCAmelCase						:	Dict[str, int]       ,       _lowerCAmelCase						:	PILImageResampling = PILImageResampling.BILINEAR       ,       _lowerCAmelCase						:	Optional[Union[str, ChannelDimension]] = None       ,       **_lowerCAmelCase						:	List[Any]       ,       ):
       SCREAMING_SNAKE_CASE_					=    get_size_dict(_lowerCAmelCase		)
       if "height" not in size or "width" not in size:
             raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"		)
       return resize(
           _lowerCAmelCase       ,       size=(size['height'], size['width'])       ,       resample=_lowerCAmelCase       ,       data_format=_lowerCAmelCase       ,       **_lowerCAmelCase		)



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



 def    lowerCAmelCase_						(							self						:	str       ,       _lowerCAmelCase						:	np.ndarray       ,       _lowerCAmelCase						:	Union[float, List[float]]       ,       _lowerCAmelCase						:	Union[float, List[float]]       ,       _lowerCAmelCase						:	Optional[Union[str, ChannelDimension]] = None       ,       **_lowerCAmelCase						:	Union[str, Any]       ,       ):
       return normalize(_lowerCAmelCase       ,       mean=_lowerCAmelCase       ,       std=_lowerCAmelCase       ,       data_format=_lowerCAmelCase       ,       **_lowerCAmelCase		)




 def    lowerCAmelCase_						(							self						:	List[str]       ,       _lowerCAmelCase						:	ImageInput       ,       _lowerCAmelCase						:	Optional[bool] = None       ,       _lowerCAmelCase						:	Dict[str, int] = None       ,       _lowerCAmelCase						:	PILImageResampling = None       ,       _lowerCAmelCase						:	Optional[bool] = None       ,       _lowerCAmelCase						:	Optional[float] = None       ,       _lowerCAmelCase						:	Optional[bool] = None       ,       _lowerCAmelCase						:	Optional[Union[float, List[float]]] = None       ,       _lowerCAmelCase						:	Optional[Union[float, List[float]]] = None       ,       _lowerCAmelCase						:	Optional[Union[str, TensorType]] = None       ,       _lowerCAmelCase						:	Union[str, ChannelDimension] = ChannelDimension.FIRST       ,       **_lowerCAmelCase						:	Optional[int]       ,       ):
       SCREAMING_SNAKE_CASE_					=    do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE_					=    do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE_					=    do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE_					=    resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE_					=    rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE_					=    image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE_					=    image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE_					=    size if size is not None else self.size
       SCREAMING_SNAKE_CASE_					=    get_size_dict(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    make_list_of_images(_lowerCAmelCase		)

       if not valid_images(_lowerCAmelCase		):
             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:
             raise ValueError('Size must be specified if do_resize is True.'		)

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

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE_					=    [to_numpy_array(_lowerCAmelCase		) for image in images]

       if do_resize:
             SCREAMING_SNAKE_CASE_					=    [self.resize(image=_lowerCAmelCase       ,       size=_lowerCAmelCase       ,       resample=_lowerCAmelCase		) for image in images]

       if do_rescale:
             SCREAMING_SNAKE_CASE_					=    [self.rescale(image=_lowerCAmelCase       ,       scale=_lowerCAmelCase		) for image in images]

       if do_normalize:
             SCREAMING_SNAKE_CASE_					=    [self.normalize(image=_lowerCAmelCase       ,       mean=_lowerCAmelCase       ,       std=_lowerCAmelCase		) for image in images]

       SCREAMING_SNAKE_CASE_					=    [to_channel_dimension_format(_lowerCAmelCase       ,       _lowerCAmelCase		) for image in images]

       SCREAMING_SNAKE_CASE_					=    {'pixel_values': images}
       return BatchFeature(data=_lowerCAmelCase       ,       tensor_type=_lowerCAmelCase		)





from __future__ import annotations

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      SCREAMING_SNAKE_CASE_					=    [0] * len(__UpperCAmelCase							)
      for i in range(1    ,    len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_					=    duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]    ,    __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(__UpperCAmelCase							)
      ]

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)


if __name__ == "__main__":
   # process id's
   lowerCamelCase__				:     Union[str, Any]			       =							[1, 2, 3]

   # ensure that we actually have processes
   if len(processes) == 0:
      print('Zero amount of processes')
      raise SystemExit(0)

   # duration time of all processes
   lowerCamelCase__				:     Optional[Any]			       =							[19, 8, 9]

   # ensure we can match each id to a duration time
   if len(duration_times) != len(processes):
      print('Unable to match all id\'s with their duration time')
      raise SystemExit(0)

   # get the waiting times and the turnaround times
   lowerCamelCase__				:     Dict			       =							calculate_waiting_times(duration_times)
   lowerCamelCase__				:     int			       =							calculate_turnaround_times(duration_times, waiting_times)

   # get the average times
   lowerCamelCase__				:     Any			       =							calculate_average_waiting_time(waiting_times)
   lowerCamelCase__				:     Tuple			       =							calculate_average_turnaround_time(turnaround_times)

   # print all the results
   print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
   for i, process in enumerate(processes):
      print(
          f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
          f'''{turnaround_times[i]}'''
      )
   print(f'''Average waiting time = {average_waiting_time}''')
   print(f'''Average turn around time = {average_turnaround_time}''')