





import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						_SCREAMING_SNAKE_CASE 		=						False

if is_vision_available():
						from PIL import Image

						from transformers import PixaStructImageProcessor
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def __init__( self : Union[str, Any]						,				_A : Any						,				_A : str=7						,				_A : Dict=3						,				_A : int=18						,				_A : Optional[int]=30						,				_A : Optional[int]=400						,				_A : int=None						,				_A : List[str]=True						,				_A : Dict=True						,				_A : int=None						,				)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     int     =		size if size is not None else {'height': 20, 'width': 20}
							snake_case_						:     str     =		parent
							snake_case_						:     Optional[Any]     =		batch_size
							snake_case_						:     Dict     =		num_channels
							snake_case_						:     Any     =		image_size
							snake_case_						:     Union[str, Any]     =		min_resolution
							snake_case_						:     Tuple     =		max_resolution
							snake_case_						:     Optional[int]     =		size
							snake_case_						:     Optional[Any]     =		do_normalize
							snake_case_						:     Optional[int]     =		do_convert_rgb
							snake_case_						:     Dict     =		[512, 1024, 2048, 4096]
							snake_case_						:     str     =		patch_size if patch_size is not None else {'height': 16, 'width': 16}






						def UpperCAmelCase_			( self : int		)							->      Optional[Any]:







							"""simple docstring"""


							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






						def UpperCAmelCase_			( self : Optional[int]		)							->      int:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
							snake_case_						:     str     =		Image.open(requests.get(_A						,				stream=_A		).raw		).convert('RGB'		)
							return raw_image
@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,			reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."    ,			)
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     str	 =    PixaStructImageProcessor if is_vision_available() else None






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     str     =		PixaStructImageProcessingTester(self		)






						@property
						def UpperCAmelCase_			( self : Tuple		)							->      Dict:







							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()






						def UpperCAmelCase_			( self : Dict		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Any     =		self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(_A						,				'do_normalize'		)		)
							self.assertTrue(hasattr(_A						,				'do_convert_rgb'		)		)






						def UpperCAmelCase_			( self : List[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Dict     =		self.image_processor_tester.prepare_dummy_image()

							snake_case_						:     int     =		self.image_processing_class(**self.image_processor_dict		)
							snake_case_						:     str     =		2048

							snake_case_						:     List[Any]     =		image_processor(_A						,				return_tensors='pt'						,				max_patches=_A		)
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,				torch.tensor(0.0_6_0_6		)						,				atol=1E-3						,				rtol=1E-3		)		)






						def UpperCAmelCase_			( self : Tuple		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							snake_case_						:     Optional[int]     =		prepare_image_inputs(self.image_processor_tester						,				equal_resolution=_A		)
							for image in image_inputs:
								self.assertIsInstance(_A						,				Image.Image		)

							# Test not batched input
							snake_case_						:     str     =		(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
								# Test not batched input
								snake_case_						:     str     =		image_processor(
								    image_inputs[0]						,				return_tensors='pt'						,				max_patches=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(1, max_patch, expected_hidden_dim)						,				)

								# Test batched
								snake_case_						:     Union[str, Any]     =		image_processor(
								    _A						,				return_tensors='pt'						,				max_patches=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,				)






						def UpperCAmelCase_			( self : str		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     str     =		self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							snake_case_						:     str     =		prepare_image_inputs(self.image_processor_tester						,				equal_resolution=_A		)
							for image in image_inputs:
								self.assertIsInstance(_A						,				Image.Image		)

							# Test not batched input
							snake_case_						:     List[Any]     =		(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * self.image_processor_tester.num_channels
							) + 2

							snake_case_						:     List[str]     =		True

							for max_patch in self.image_processor_tester.max_patches:
								# Test not batched input
								with self.assertRaises(_A		):
									snake_case_						:     Optional[Any]     =		image_processor(
									    image_inputs[0]						,				return_tensors='pt'						,				max_patches=_A		).flattened_patches

								snake_case_						:     Optional[int]     =		'Hello'

								snake_case_						:     int     =		image_processor(
								    image_inputs[0]						,				return_tensors='pt'						,				max_patches=_A						,				header_text=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(1, max_patch, expected_hidden_dim)						,				)

								# Test batched
								snake_case_						:     Any     =		image_processor(
								    _A						,				return_tensors='pt'						,				max_patches=_A						,				header_text=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,				)






						def UpperCAmelCase_			( self : int		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.image_processing_class(**self.image_processor_dict		)
							# create random numpy tensors
							snake_case_						:     str     =		prepare_image_inputs(self.image_processor_tester						,				equal_resolution=_A						,				numpify=_A		)
							for image in image_inputs:
								self.assertIsInstance(_A						,				np.ndarray		)

							snake_case_						:     Optional[Any]     =		(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
								# Test not batched input
								snake_case_						:     str     =		image_processor(
								    image_inputs[0]						,				return_tensors='pt'						,				max_patches=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(1, max_patch, expected_hidden_dim)						,				)

								# Test batched
								snake_case_						:     Any     =		image_processor(
								    _A						,				return_tensors='pt'						,				max_patches=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,				)






						def UpperCAmelCase_			( self : Optional[int]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							snake_case_						:     Any     =		prepare_image_inputs(self.image_processor_tester						,				equal_resolution=_A						,				torchify=_A		)
							for image in image_inputs:
								self.assertIsInstance(_A						,				torch.Tensor		)

							# Test not batched input
							snake_case_						:     Union[str, Any]     =		(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
								# Test not batched input
								snake_case_						:     Union[str, Any]     =		image_processor(
								    image_inputs[0]						,				return_tensors='pt'						,				max_patches=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(1, max_patch, expected_hidden_dim)						,				)

								# Test batched
								snake_case_						:     List[Any]     =		image_processor(
								    _A						,				return_tensors='pt'						,				max_patches=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,				)
@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,			reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."    ,			)
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     Optional[int]	 =    PixaStructImageProcessor if is_vision_available() else None






						def UpperCAmelCase_			( self : Dict		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		PixaStructImageProcessingTester(self						,				num_channels=4		)
							snake_case_						:     Optional[int]     =		3






						@property
						def UpperCAmelCase_			( self : str		)							->      Tuple:







							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()






						def UpperCAmelCase_			( self : Dict		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(_A						,				'do_normalize'		)		)
							self.assertTrue(hasattr(_A						,				'do_convert_rgb'		)		)






						def UpperCAmelCase_			( self : Dict		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							snake_case_						:     str     =		prepare_image_inputs(self.image_processor_tester						,				equal_resolution=_A		)
							for image in image_inputs:
								self.assertIsInstance(_A						,				Image.Image		)

							# Test not batched input
							snake_case_						:     List[Any]     =		(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
								# Test not batched input
								snake_case_						:     List[str]     =		image_processor(
								    image_inputs[0]						,				return_tensors='pt'						,				max_patches=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(1, max_patch, expected_hidden_dim)						,				)

								# Test batched
								snake_case_						:     Any     =		image_processor(
								    _A						,				return_tensors='pt'						,				max_patches=_A		).flattened_patches
								self.assertEqual(
								    encoded_images.shape						,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,				)








from __future__ import annotations

import pandas as pd


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Optional[Any]     =		[0] * no_of_processes
	snake_case_						:     Tuple     =		[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		burst_time[i]

	snake_case_						:     Optional[Any]     =		0
	snake_case_						:     Dict     =		0
	snake_case_						:     Any     =		9_99_99_99_99
	snake_case_						:     Tuple     =		0
	snake_case_						:     List[Any]     =		False

	# Process until all processes are completed
	while complete != no_of_processes:
		for j in range(__a				):
			if arrival_time[j] <= increment_time and remaining_time[j] > 0:
				if remaining_time[j] < minm:
					snake_case_						:     str     =		remaining_time[j]
					snake_case_						:     Any     =		j
					snake_case_						:     List[str]     =		True

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

		snake_case_						:     Any     =		remaining_time[short]
		if minm == 0:
			snake_case_						:     Dict     =		9_99_99_99_99

		if remaining_time[short] == 0:
			complete += 1
			snake_case_						:     List[str]     =		False

			# Find finish time of current process
			snake_case_						:     List[str]     =		increment_time + 1

			# Calculate waiting time
			snake_case_						:     Any     =		finish_time - arrival_time[short]
			snake_case_						:     Any     =		finar - burst_time[short]

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

        # Increment time
		increment_time += 1
	return waiting_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Tuple     =		[0] * no_of_processes
	for i in range(__a				):
		snake_case_						:     str     =		burst_time[i] + waiting_time[i]
	return turn_around_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     int     =		0
	snake_case_						:     Optional[Any]     =		0
	for i in range(__a				):
		snake_case_						:     int     =		total_waiting_time + waiting_time[i]
		snake_case_						:     Optional[Any]     =		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)

