"""simple docstring"""






from typing import Dict, Iterable, 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, to_pil_image
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, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
						import PIL

# soft dependency
if is_pytesseract_available():
						import pytesseract

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



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				return [
				    int(1_0_0_0 * (box[0] / width)       ),
				    int(1_0_0_0 * (box[1] / height)       ),
				    int(1_0_0_0 * (box[2] / width)       ),
				    int(1_0_0_0 * (box[3] / height)       ),
				]



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							to_pil_image(lowerCamelCase       )
				UpperCAmelCase__      ,	UpperCAmelCase__             =							pil_image.size
				UpperCAmelCase__             =							pytesseract.image_to_data(lowerCamelCase					,			lang=lowerCamelCase					,			output_type='dict'					,			config=lowerCamelCase       )
				UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							data['text'], data['left'], data['top'], data['width'], data['height']

				# filter empty words and corresponding coordinates
				UpperCAmelCase__             =							[idx for idx, word in enumerate(lowerCamelCase       ) if not word.strip()]
				UpperCAmelCase__             =							[word for idx, word in enumerate(lowerCamelCase       ) if idx not in irrelevant_indices]
				UpperCAmelCase__             =							[coord for idx, coord in enumerate(lowerCamelCase       ) if idx not in irrelevant_indices]
				UpperCAmelCase__             =							[coord for idx, coord in enumerate(lowerCamelCase       ) if idx not in irrelevant_indices]
				UpperCAmelCase__             =							[coord for idx, coord in enumerate(lowerCamelCase       ) if idx not in irrelevant_indices]
				UpperCAmelCase__             =							[coord for idx, coord in enumerate(lowerCamelCase       ) if idx not in irrelevant_indices]

				# turn coordinates into (left, top, left+width, top+height) format
				UpperCAmelCase__             =							[]
				for x, y, w, h in zip(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
								UpperCAmelCase__             =							[x, y, x + w, y + h]
								actual_boxes.append(lowerCamelCase       )

				# finally, normalize the bounding boxes
				UpperCAmelCase__             =							[]
				for box in actual_boxes:
								normalized_boxes.append(normalize_box(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )       )

				assert len(lowerCamelCase       ) == len(lowerCamelCase       ), "Not as many words as there are bounding boxes"

				return words, normalized_boxes







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["pixel_values"]







					def __init__(				self   :	int    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BILINEAR    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	float = 1 / 255    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Union[float, Iterable[float]] = None    ,lowerCamelCase__   :	Union[float, Iterable[float]] = None    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[str] = None    ,lowerCamelCase__   :	Optional[str] = ""    ,**lowerCamelCase__   :	Any    ,):
									super().__init__(**lowerCamelCase__			)
									UpperCAmelCase__             =							size if size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__			)

									UpperCAmelCase__             =							do_resize
									UpperCAmelCase__             =							size
									UpperCAmelCase__             =							resample
									UpperCAmelCase__             =							do_rescale
									UpperCAmelCase__             =							rescale_value
									UpperCAmelCase__             =							do_normalize
									UpperCAmelCase__             =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									UpperCAmelCase__             =							image_std if image_std is not None else IMAGENET_STANDARD_STD
									UpperCAmelCase__             =							apply_ocr
									UpperCAmelCase__             =							ocr_lang
									UpperCAmelCase__             =							tesseract_config







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Dict[str, int]    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BILINEAR    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	str    ,):
									UpperCAmelCase__             =							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()}'''			)
									UpperCAmelCase__             =							(size['height'], size['width'])
									return resize(lowerCamelCase__    ,size=lowerCamelCase__    ,resample=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







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







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Union[float, Iterable[float]]    ,lowerCamelCase__   :	Union[float, Iterable[float]]    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	Optional[int]    ,):
									return normalize(lowerCamelCase__    ,mean=lowerCamelCase__    ,std=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	ImageInput    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	Union[str, Any]=None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Union[float, Iterable[float]] = None    ,lowerCamelCase__   :	Union[float, Iterable[float]] = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Optional[str] = None    ,lowerCamelCase__   :	Optional[str] = None    ,lowerCamelCase__   :	Optional[Union[str, TensorType]] = None    ,lowerCamelCase__   :	ChannelDimension = ChannelDimension.FIRST    ,**lowerCamelCase__   :	str    ,):
									UpperCAmelCase__             =							do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__             =							size if size is not None else self.size
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__			)
									UpperCAmelCase__             =							resample if resample is not None else self.resample
									UpperCAmelCase__             =							do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__             =							rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__             =							do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__             =							image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__             =							image_std if image_std is not None else self.image_std
									UpperCAmelCase__             =							apply_ocr if apply_ocr is not None else self.apply_ocr
									UpperCAmelCase__             =							ocr_lang if ocr_lang is not None else self.ocr_lang
									UpperCAmelCase__             =							tesseract_config if tesseract_config is not None else self.tesseract_config

									UpperCAmelCase__             =							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.'			)

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'			)

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

									# Tesseract OCR to get words + normalized bounding boxes
									if apply_ocr:
													requires_backends(self    ,'pytesseract'			)
													UpperCAmelCase__             =							[]
													UpperCAmelCase__             =							[]
													for image in images:
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							apply_tesseract(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)
																	words_batch.append(lowerCamelCase__			)
																	boxes_batch.append(lowerCamelCase__			)

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

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

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

									UpperCAmelCase__             =							[to_channel_dimension_format(lowerCamelCase__    ,lowerCamelCase__			) for image in images]

									UpperCAmelCase__             =							BatchFeature(data={'pixel_values': images}    ,tensor_type=lowerCamelCase__			)

									if apply_ocr:
													UpperCAmelCase__             =							words_batch
													UpperCAmelCase__             =							boxes_batch
									return data







'''simple docstring'''




from __future__ import annotations

from statistics import mean





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     list[int]	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			[0] * no_of_processes
   lowerCAmelCase     =			[0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i]
   lowerCAmelCase     =			[]

   lowerCAmelCase     =			0
   lowerCAmelCase     =			0

   # When processes are not completed,
   # A process whose arrival time has passed \
   # and has remaining execution time is put into the ready_process.
   # The shortest process in the ready_process, target_process is executed.

   while completed != no_of_processes:
      lowerCAmelCase     =			[]
      lowerCAmelCase     =			-1

      for i in range(lowerCamelCase				):
         if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
            ready_process.append(lowerCamelCase				)

      if len(lowerCamelCase				) > 0:
         lowerCAmelCase     =			ready_process[0]
         for i in ready_process:
            if remaining_time[i] < remaining_time[target_process]:
               lowerCAmelCase     =			i
         total_time += burst_time[target_process]
         completed += 1
         lowerCAmelCase     =			0
         lowerCAmelCase     =			(
             total_time - arrival_time[target_process] - burst_time[target_process]
         )
      else:
         total_time += 1

   return waiting_time





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     int	, lowerCamelCase	:     list[int]				):
   lowerCAmelCase     =			[0] * no_of_processes
   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i] + waiting_time[i]
   return turn_around_time


if __name__ == "__main__":
       print("""[TEST CASE 01]""")

       __snake_case    						=4
       __snake_case    						=[2, 5, 3, 7]
       __snake_case    						=[0, 0, 0, 0]
       __snake_case    						=calculate_waitingtime(arrival_time, burst_time, no_of_processes)
       __snake_case    						=calculate_turnaroundtime(
           burst_time, no_of_processes, waiting_time
       )

       # Printing the Result
       print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
       for i, process_id in enumerate(list(range(1, 5))):
              print(
                  F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
                  F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
              )
       print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''')
       print(F'''Average turnaround time = {mean(turn_around_time):.5f}''')





