

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)


if is_vision_available():
					import PIL

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["pixel_values"]


						def __init__(       self						,					A_ = True						,					A_ = None						,					A_ = PILImageResampling.BICUBIC						,					A_ = True						,					A_ = None						,					A_ = True						,					A_ = 1 / 255						,					A_ = True						,					A_ = None						,					A_ = None						,					A_ = True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)
										_UpperCAmelCase       :		Any 		=    size if size is not None else {"shortest_edge": 224}
										_UpperCAmelCase       :		List[str] 		=    get_size_dict(A_						,					default_to_square=A_	)
										_UpperCAmelCase       :		List[str] 		=    crop_size if crop_size is not None else {"height": 224, "width": 224}
										_UpperCAmelCase       :		List[str] 		=    get_size_dict(A_						,					default_to_square=A_						,					param_name="crop_size"	)

										_UpperCAmelCase       :		Union[str, Any] 		=    do_resize
										_UpperCAmelCase       :		Optional[int] 		=    size
										_UpperCAmelCase       :		Optional[Any] 		=    resample
										_UpperCAmelCase       :		Dict 		=    do_center_crop
										_UpperCAmelCase       :		Union[str, Any] 		=    crop_size
										_UpperCAmelCase       :		List[str] 		=    do_rescale
										_UpperCAmelCase       :		Union[str, Any] 		=    rescale_factor
										_UpperCAmelCase       :		Union[str, Any] 		=    do_normalize
										_UpperCAmelCase       :		Optional[Any] 		=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										_UpperCAmelCase       :		Any 		=    image_std if image_std is not None else OPENAI_CLIP_STD
										_UpperCAmelCase       :		List[str] 		=    do_convert_rgb


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = PILImageResampling.BICUBIC						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    get_size_dict(A_						,					default_to_square=A_	)
										if "shortest_edge" not in size:
														raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'	)
										_UpperCAmelCase       :		int 		=    get_resize_output_image_size(A_						,					size=size["shortest_edge"]						,					default_to_square=A_	)
										return resize(A_						,					size=A_						,					resample=A_						,					data_format=A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    get_size_dict(A_	)
										if "height" not in size or "width" not in size:
														raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'	)
										return center_crop(A_						,					size=(size["height"], size["width"])						,					data_format=A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										return rescale(A_						,					scale=A_						,					data_format=A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										return normalize(A_						,					mean=A_						,					std=A_						,					data_format=A_						,					**A_	)





						def        _UpperCAmelCase				(       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_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase       :		Dict 		=    size if size is not None else self.size
										_UpperCAmelCase       :		Union[str, Any] 		=    get_size_dict(A_						,					param_name="size"						,					default_to_square=A_	)
										_UpperCAmelCase       :		Any 		=    resample if resample is not None else self.resample
										_UpperCAmelCase       :		Union[str, Any] 		=    do_center_crop if do_center_crop is not None else self.do_center_crop
										_UpperCAmelCase       :		str 		=    crop_size if crop_size is not None else self.crop_size
										_UpperCAmelCase       :		Dict 		=    get_size_dict(A_						,					param_name="crop_size"						,					default_to_square=A_	)
										_UpperCAmelCase       :		Dict 		=    do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase       :		Dict 		=    rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase       :		Optional[Any] 		=    do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase       :		Dict 		=    image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase       :		Optional[int] 		=    image_std if image_std is not None else self.image_std
										_UpperCAmelCase       :		Optional[Any] 		=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										_UpperCAmelCase       :		Dict 		=    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:
														raise ValueError("Size 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."	)

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
														_UpperCAmelCase       :		List[Any] 		=    [convert_to_rgb(A_	) for image in images]

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

										if do_resize:
														_UpperCAmelCase       :		Any 		=    [self.resize(image=A_						,					size=A_						,					resample=A_	) for image in images]

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

										if do_rescale:
														_UpperCAmelCase       :		int 		=    [self.rescale(image=A_						,					scale=A_	) for image in images]

										if do_normalize:
														_UpperCAmelCase       :		List[Any] 		=    [self.normalize(image=A_						,					mean=A_						,					std=A_	) for image in images]

										_UpperCAmelCase       :		Optional[Any] 		=    [to_channel_dimension_format(A_						,					A_	) for image in images]

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





from __future__ import annotations

import pandas as pd

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       list[int]:
				_UpperCAmelCase       :		List[Any] 		=    [0] * no_of_processes
				_UpperCAmelCase       :		Dict 		=    [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		List[str] 		=    burst_time[i]

				_UpperCAmelCase       :		Any 		=    0
				_UpperCAmelCase       :		Union[str, Any] 		=    0
				_UpperCAmelCase       :		str 		=    9_9999_9999
				_UpperCAmelCase       :		List[Any] 		=    0
				_UpperCAmelCase       :		Any 		=    False

				# Process until all processes are completed
				while complete != no_of_processes:
								for j in range(lowerCAmelCase ):
												if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																if remaining_time[j] < minm:
																				_UpperCAmelCase       :		Optional[int] 		=    remaining_time[j]
																				_UpperCAmelCase       :		int 		=    j
																				_UpperCAmelCase       :		Tuple 		=    True

								if not check:
												increment_time += 1
												continue
								remaining_time[short] -= 1

								_UpperCAmelCase       :		List[Any] 		=    remaining_time[short]
								if minm == 0:
												_UpperCAmelCase       :		List[Any] 		=    9_9999_9999

								if remaining_time[short] == 0:
												complete += 1
												_UpperCAmelCase       :		int 		=    False

												# Find finish time of current process
												_UpperCAmelCase       :		Optional[int] 		=    increment_time + 1

												# Calculate waiting time
												_UpperCAmelCase       :		str 		=    finish_time - arrival_time[short]
												_UpperCAmelCase       :		Union[str, Any] 		=    finar - burst_time[short]

												if waiting_time[short] < 0:
																_UpperCAmelCase       :		Optional[int] 		=    0

        # Increment time
								increment_time += 1
				return waiting_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  list[int] )   ->       list[int]:
				_UpperCAmelCase       :		str 		=    [0] * no_of_processes
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    burst_time[i] + waiting_time[i]
				return turn_around_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       None:
				_UpperCAmelCase       :		Dict 		=    0
				_UpperCAmelCase       :		List[str] 		=    0
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Union[str, Any] 		=    total_waiting_time + waiting_time[i]
								_UpperCAmelCase       :		int 		=    total_turn_around_time + turn_around_time[i]
				print(F'Average waiting time = {total_waiting_time / no_of_processes:.5f}' )
				print("Average turn around time ="  ,      total_turn_around_time / no_of_processes )


if __name__ == "__main__":
					print('Enter how many process you want to analyze')
					SCREAMING_SNAKE_CASE_        =      int(input())
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print('Enter the arrival time and burst time for process:--' + str(i + 1))
										SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_        =      map(int, input().split())

					SCREAMING_SNAKE_CASE_        =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      burst_time
					SCREAMING_SNAKE_CASE_        =      no_of_processes
					SCREAMING_SNAKE_CASE_        =      waiting_time
					SCREAMING_SNAKE_CASE_        =      calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        'Process',
					        'BurstTime',
					        'ArrivalTime',
					        'WaitingTime',
					        'TurnAroundTime',
					    ],
					)

					# Printing the dataFrame
					pd.set_option('display.max_rows', fcfs.shape[0] + 1)
					print(fcfs)


