





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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

if is_vision_available():
						from PIL import Image

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






						def __init__( self : Optional[int]						,				_A : Dict						,				_A : List[Any]=7						,				_A : Optional[int]=3						,				_A : int=30						,				_A : Optional[int]=400						,				_A : str=True						,				_A : Optional[Any]=None						,				_A : Optional[Any]=True						,				_A : Optional[int]=1 / 255						,				_A : Optional[Any]=True						,				_A : int=[0.5, 0.5, 0.5]						,				_A : Union[str, Any]=[0.5, 0.5, 0.5]						,				_A : Union[str, Any]=True						,				)							->      Any:







							"""simple docstring"""


							snake_case_						:     Tuple     =		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
							snake_case_						:     Union[str, Any]     =		parent
							snake_case_						:     str     =		batch_size
							snake_case_						:     Any     =		num_channels
							snake_case_						:     Any     =		min_resolution
							snake_case_						:     Tuple     =		max_resolution
							snake_case_						:     Tuple     =		do_resize
							snake_case_						:     List[Any]     =		size
							snake_case_						:     List[str]     =		do_rescale
							snake_case_						:     Tuple     =		rescale_factor
							snake_case_						:     Dict     =		do_normalize
							snake_case_						:     List[str]     =		image_mean
							snake_case_						:     int     =		image_std
							snake_case_						:     Optional[int]     =		do_pad






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







							"""simple docstring"""


							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_pad": self.do_pad,
							}






						def UpperCAmelCase_			( self : int						,				_A : Optional[Any]						,				_A : Union[str, Any]=False		)							->      List[str]:







							"""simple docstring"""


							if not batched:
								snake_case_						:     Any     =		image_inputs[0]
								if isinstance(_A						,				Image.Image		):
									snake_case_  ,snake_case_						:     Union[str, Any]     =		image.size
								else:
									snake_case_  ,snake_case_						:     Dict     =		image.shape[1], image.shape[2]
								if w < h:
									snake_case_						:     List[str]     =		int(self.size['shortest_edge'] * h / w		)
									snake_case_						:     Optional[Any]     =		self.size['shortest_edge']
								elif w > h:
									snake_case_						:     Optional[int]     =		self.size['shortest_edge']
									snake_case_						:     Optional[int]     =		int(self.size['shortest_edge'] * w / h		)
								else:
									snake_case_						:     Dict     =		self.size['shortest_edge']
									snake_case_						:     Optional[Any]     =		self.size['shortest_edge']

							else:
								snake_case_						:     Optional[int]     =		[]
								for image in image_inputs:
									snake_case_  ,snake_case_						:     Optional[int]     =		self.get_expected_values([image]		)
									expected_values.append((expected_height, expected_width)		)
								snake_case_						:     Optional[Any]     =		max(_A						,				key=lambda _A		: item[0]		)[0]
								snake_case_						:     Union[str, Any]     =		max(_A						,				key=lambda _A		: item[1]		)[1]

							return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     Optional[int]	 =    DetrImageProcessor if is_vision_available() else None






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







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		DetrImageProcessingTester(self		)






						@property
						def UpperCAmelCase_			( self : List[str]		)							->      Dict:







							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()






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







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(_A						,				'image_mean'		)		)
							self.assertTrue(hasattr(_A						,				'image_std'		)		)
							self.assertTrue(hasattr(_A						,				'do_normalize'		)		)
							self.assertTrue(hasattr(_A						,				'do_rescale'		)		)
							self.assertTrue(hasattr(_A						,				'rescale_factor'		)		)
							self.assertTrue(hasattr(_A						,				'do_resize'		)		)
							self.assertTrue(hasattr(_A						,				'size'		)		)
							self.assertTrue(hasattr(_A						,				'do_pad'		)		)






						def UpperCAmelCase_			( self : str		)							->      Any:







							"""simple docstring"""


							snake_case_						:     int     =		self.image_processing_class.from_dict(self.image_processor_dict		)
							self.assertEqual(image_processor.size						,				{'shortest_edge': 18, 'longest_edge': 1333}		)
							self.assertEqual(image_processor.do_pad						,				_A		)

							snake_case_						:     List[Any]     =		self.image_processing_class.from_dict(
							    self.image_processor_dict						,				size=42						,				max_size=84						,				pad_and_return_pixel_mask=_A		)
							self.assertEqual(image_processor.size						,				{'shortest_edge': 42, 'longest_edge': 84}		)
							self.assertEqual(image_processor.do_pad						,				_A		)






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







							"""simple docstring"""


							pass






						def UpperCAmelCase_			( self : Tuple		)							->      Any:







							"""simple docstring"""


							snake_case_						:     str     =		self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							snake_case_						:     Optional[Any]     =		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     =		image_processing(image_inputs[0]						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     List[str]     =		self.image_processor_tester.get_expected_values(_A		)

							self.assertEqual(
							    encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

							# Test batched
							snake_case_  ,snake_case_						:     List[Any]     =		self.image_processor_tester.get_expected_values(_A						,				batched=_A		)

							snake_case_						:     Union[str, Any]     =		image_processing(_A						,				return_tensors='pt'		).pixel_values
							self.assertEqual(
							    encoded_images.shape						,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )						,				)






						def UpperCAmelCase_			( self : Dict		)							->      str:







							"""simple docstring"""


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

							# Test not batched input
							snake_case_						:     Any     =		image_processing(image_inputs[0]						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     Tuple     =		self.image_processor_tester.get_expected_values(_A		)

							self.assertEqual(
							    encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

							# Test batched
							snake_case_						:     List[str]     =		image_processing(_A						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     str     =		self.image_processor_tester.get_expected_values(_A						,				batched=_A		)

							self.assertEqual(
							    encoded_images.shape						,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )						,				)






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







							"""simple docstring"""


							snake_case_						:     List[Any]     =		self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							snake_case_						:     List[str]     =		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_						:     Any     =		image_processing(image_inputs[0]						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     Union[str, Any]     =		self.image_processor_tester.get_expected_values(_A		)

							self.assertEqual(
							    encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

							# Test batched
							snake_case_						:     str     =		image_processing(_A						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     int     =		self.image_processor_tester.get_expected_values(_A						,				batched=_A		)

							self.assertEqual(
							    encoded_images.shape						,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )						,				)






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







							"""simple docstring"""


							snake_case_						:     Tuple     =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,				'r'		) as f:
								snake_case_						:     int     =		json.loads(f.read()		)

							snake_case_						:     Any     =		{'image_id': 39769, 'annotations': target}

							# encode them
							snake_case_						:     str     =		DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'		)
							snake_case_						:     Any     =		image_processing(images=_A						,				annotations=_A						,				return_tensors='pt'		)

							# verify pixel values
							snake_case_						:     Dict     =		torch.Size([1, 3, 800, 1066]		)
							self.assertEqual(encoding['pixel_values'].shape						,				_A		)

							snake_case_						:     Any     =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,				_A						,				atol=1E-4		)		)

							# verify area
							snake_case_						:     List[str]     =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,				_A		)		)
							# verify boxes
							snake_case_						:     Union[str, Any]     =		torch.Size([6, 4]		)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,				_A		)
							snake_case_						:     Any     =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,				_A						,				atol=1E-3		)		)
							# verify image_id
							snake_case_						:     Optional[Any]     =		torch.tensor([39769]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,				_A		)		)
							# verify is_crowd
							snake_case_						:     Optional[int]     =		torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,				_A		)		)
							# verify class_labels
							snake_case_						:     Dict     =		torch.tensor([75, 75, 63, 65, 17, 17]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,				_A		)		)
							# verify orig_size
							snake_case_						:     Optional[Any]     =		torch.tensor([480, 640]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,				_A		)		)
							# verify size
							snake_case_						:     Optional[Any]     =		torch.tensor([800, 1066]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,				_A		)		)






						@slow
						def UpperCAmelCase_			( self : int		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,				'r'		) as f:
								snake_case_						:     Tuple     =		json.loads(f.read()		)

							snake_case_						:     Any     =		{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

							snake_case_						:     int     =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'		)

							# encode them
							snake_case_						:     Union[str, Any]     =		DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'		)
							snake_case_						:     Any     =		image_processing(images=_A						,				annotations=_A						,				masks_path=_A						,				return_tensors='pt'		)

							# verify pixel values
							snake_case_						:     Optional[Any]     =		torch.Size([1, 3, 800, 1066]		)
							self.assertEqual(encoding['pixel_values'].shape						,				_A		)

							snake_case_						:     str     =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,				_A						,				atol=1E-4		)		)

							# verify area
							snake_case_						:     List[Any]     =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,				_A		)		)
							# verify boxes
							snake_case_						:     Union[str, Any]     =		torch.Size([6, 4]		)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,				_A		)
							snake_case_						:     Optional[Any]     =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,				_A						,				atol=1E-3		)		)
							# verify image_id
							snake_case_						:     List[str]     =		torch.tensor([39769]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,				_A		)		)
							# verify is_crowd
							snake_case_						:     Dict     =		torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,				_A		)		)
							# verify class_labels
							snake_case_						:     str     =		torch.tensor([17, 17, 63, 75, 75, 93]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,				_A		)		)
							# verify masks
							snake_case_						:     List[Any]     =		822873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,				_A		)
							# verify orig_size
							snake_case_						:     List[str]     =		torch.tensor([480, 640]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,				_A		)		)
							# verify size
							snake_case_						:     Any     =		torch.tensor([800, 1066]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,				_A		)		)








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)

