

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 				__UpperCamelCase				(					unittest.TestCase			):







	def __init__(   self: int  , __UpperCamelCase: List[Any]  , __UpperCamelCase: List[Any]=7  , __UpperCamelCase: str=3  , __UpperCamelCase: Optional[int]=30  , __UpperCamelCase: List[Any]=4_00  , __UpperCamelCase: Union[str, Any]=True  , __UpperCamelCase: Optional[int]=None  , __UpperCamelCase: List[Any]=True  , __UpperCamelCase: Optional[Any]=1 / 2_55  , __UpperCamelCase: Union[str, Any]=True  , __UpperCamelCase: List[str]=[0.5, 0.5, 0.5]  , __UpperCamelCase: List[str]=[0.5, 0.5, 0.5]  , __UpperCamelCase: Dict=True  , ):

						'''simple docstring'''





						__magic_name__							=    size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
						__magic_name__							=    parent
						__magic_name__							=    batch_size
						__magic_name__							=    num_channels
						__magic_name__							=    min_resolution
						__magic_name__							=    max_resolution
						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    do_rescale
						__magic_name__							=    rescale_factor
						__magic_name__							=    do_normalize
						__magic_name__							=    image_mean
						__magic_name__							=    image_std
						__magic_name__							=    do_pad







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''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 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: int=False      ):

						'''simple docstring'''





						if not batched:
											__magic_name__							=    image_inputs[0]
											if isinstance(__UpperCamelCase  , Image.Image      ):
																__magic_name__,	__magic_name__							=    image.size
											else:
																__magic_name__,	__magic_name__							=    image.shape[1], image.shape[2]
											if w < h:
																__magic_name__							=    int(self.size['shortest_edge'] * h / w      )
																__magic_name__							=    self.size['shortest_edge']
											elif w > h:
																__magic_name__							=    self.size['shortest_edge']
																__magic_name__							=    int(self.size['shortest_edge'] * w / h      )
											else:
																__magic_name__							=    self.size['shortest_edge']
																__magic_name__							=    self.size['shortest_edge']

						else:
											__magic_name__							=    []
											for image in image_inputs:
																__magic_name__,	__magic_name__							=    self.get_expected_values([image]      )
																expected_values.append((expected_height, expected_width)      )
											__magic_name__							=    max(__UpperCamelCase  , key=lambda __UpperCamelCase      : item[0]      )[0]
											__magic_name__							=    max(__UpperCamelCase  , key=lambda __UpperCamelCase      : item[1]      )[1]

						return expected_height, expected_width




@require_torch
@require_vision
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  DetrImageProcessor if is_vision_available() else None







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    DetrImageProcessingTester(self      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'image_mean'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'image_std'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_normalize'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_rescale'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'rescale_factor'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_resize'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'size'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_pad'      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class.from_dict(self.image_processor_dict      )
						self.assertEqual(image_processor.size  , {'shortest_edge': 18, 'longest_edge': 13_33}      )
						self.assertEqual(image_processor.do_pad  , __UpperCamelCase      )

						__magic_name__							=    self.image_processing_class.from_dict(
						    self.image_processor_dict  , size=42  , max_size=84  , pad_and_return_pixel_mask=__UpperCamelCase      )
						self.assertEqual(image_processor.size  , {'shortest_edge': 42, 'longest_edge': 84}      )
						self.assertEqual(image_processor.do_pad  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						pass







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





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

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

						__magic_name__,	__magic_name__							=    self.image_processor_tester.get_expected_values(__UpperCamelCase      )

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

						# Test batched
						__magic_name__,	__magic_name__							=    self.image_processor_tester.get_expected_values(__UpperCamelCase  , batched=__UpperCamelCase      )

						__magic_name__							=    image_processing(__UpperCamelCase  , 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 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase  , numpify=__UpperCamelCase      )
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , np.ndarray      )

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

						__magic_name__,	__magic_name__							=    self.image_processor_tester.get_expected_values(__UpperCamelCase      )

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

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      ).pixel_values

						__magic_name__,	__magic_name__							=    self.image_processor_tester.get_expected_values(__UpperCamelCase  , batched=__UpperCamelCase      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase  , torchify=__UpperCamelCase      )
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , torch.Tensor      )

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

						__magic_name__,	__magic_name__							=    self.image_processor_tester.get_expected_values(__UpperCamelCase      )

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

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      ).pixel_values

						__magic_name__,	__magic_name__							=    self.image_processor_tester.get_expected_values(__UpperCamelCase  , batched=__UpperCamelCase      )

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







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





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

						__magic_name__							=    {'image_id': 3_97_69, 'annotations': target}

						# encode them
						__magic_name__							=    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'      )
						__magic_name__							=    image_processing(images=__UpperCamelCase  , annotations=__UpperCamelCase  , return_tensors='pt'      )

						# verify pixel values
						__magic_name__							=    torch.Size([1, 3, 8_00, 10_66]      )
						self.assertEqual(encoding['pixel_values'].shape  , __UpperCamelCase      )

						__magic_name__							=    torch.tensor([0.2796, 0.3138, 0.3481]      )
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]  , __UpperCamelCase  , atol=1E-4      )      )

						# verify area
						__magic_name__							=    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']  , __UpperCamelCase      )      )
						# verify boxes
						__magic_name__							=    torch.Size([6, 4]      )
						self.assertEqual(encoding['labels'][0]['boxes'].shape  , __UpperCamelCase      )
						__magic_name__							=    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  , __UpperCamelCase  , atol=1E-3      )      )
						# verify image_id
						__magic_name__							=    torch.tensor([3_97_69]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  , __UpperCamelCase      )      )
						# verify is_crowd
						__magic_name__							=    torch.tensor([0, 0, 0, 0, 0, 0]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  , __UpperCamelCase      )      )
						# verify class_labels
						__magic_name__							=    torch.tensor([75, 75, 63, 65, 17, 17]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  , __UpperCamelCase      )      )
						# verify orig_size
						__magic_name__							=    torch.tensor([4_80, 6_40]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  , __UpperCamelCase      )      )
						# verify size
						__magic_name__							=    torch.tensor([8_00, 10_66]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']  , __UpperCamelCase      )      )







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





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

						__magic_name__							=    {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

						__magic_name__							=    pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'      )

						# encode them
						__magic_name__							=    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'      )
						__magic_name__							=    image_processing(images=__UpperCamelCase  , annotations=__UpperCamelCase  , masks_path=__UpperCamelCase  , return_tensors='pt'      )

						# verify pixel values
						__magic_name__							=    torch.Size([1, 3, 8_00, 10_66]      )
						self.assertEqual(encoding['pixel_values'].shape  , __UpperCamelCase      )

						__magic_name__							=    torch.tensor([0.2796, 0.3138, 0.3481]      )
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]  , __UpperCamelCase  , atol=1E-4      )      )

						# verify area
						__magic_name__							=    torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']  , __UpperCamelCase      )      )
						# verify boxes
						__magic_name__							=    torch.Size([6, 4]      )
						self.assertEqual(encoding['labels'][0]['boxes'].shape  , __UpperCamelCase      )
						__magic_name__							=    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  , __UpperCamelCase  , atol=1E-3      )      )
						# verify image_id
						__magic_name__							=    torch.tensor([3_97_69]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  , __UpperCamelCase      )      )
						# verify is_crowd
						__magic_name__							=    torch.tensor([0, 0, 0, 0, 0, 0]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  , __UpperCamelCase      )      )
						# verify class_labels
						__magic_name__							=    torch.tensor([17, 17, 63, 75, 75, 93]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  , __UpperCamelCase      )      )
						# verify masks
						__magic_name__							=    82_28_73
						self.assertEqual(encoding['labels'][0]['masks'].sum().item()  , __UpperCamelCase      )
						# verify orig_size
						__magic_name__							=    torch.tensor([4_80, 6_40]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  , __UpperCamelCase      )      )
						# verify size
						__magic_name__							=    torch.tensor([8_00, 10_66]      )
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']  , __UpperCamelCase      )      )







import requests
from bsa import BeautifulSoup




def 					_lowercase     (				a_				:					str = "https://www.worldometers.info/coronavirus"				)				->	dict:




					'''simple docstring'''







					__magic_name__							=    BeautifulSoup(requests.get(a_				).text					,'html.parser'				)
					__magic_name__							=    soup.findAll('h1'				)
					__magic_name__							=    soup.findAll('div'					,{'class': 'maincounter-number'}				)
					keys += soup.findAll('span'					,{'class': 'panel-title'}				)
					values += soup.findAll('div'					,{'class': 'number-table-main'}				)
					return {key.text.strip(): value.text.strip() for key, value in zip(a_					,a_				)}


if __name__ == "__main__":
						print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
						for key, value in world_covidaa_stats().items():
												print(f'''{key}\n{value}\n''')




