



'''simple docstring'''



import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image
else:






	class 					UpperCAmelCase__	:



						@staticmethod
						def        UpperCAmelCase_ (    *UpperCamelCase			,				**UpperCamelCase     )     ->			Union[str, Any]:
							pass



def   __lowerCAmelCase					(							lowerCamelCase   :						Image					):






	'''simple docstring'''




	__lowerCAmelCase												=  hashlib.mda(image.tobytes()					)
	return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					UpperCAmelCase__	(		unittest.TestCase ):
					a      :     List[Any]        =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[str]:
						__lowerCAmelCase												=  DepthEstimationPipeline(model=UpperCamelCase			,				image_processor=UpperCamelCase     )
						return depth_estimator, [
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						]



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[int]:
						__lowerCAmelCase												=  depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
						self.assertEqual({"predicted_depth": ANY(torch.Tensor     ), "depth": ANY(Image.Image     )}			,				UpperCamelCase     )
						import datasets

						__lowerCAmelCase												=  datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,				"image"			,				split="test"     )
						__lowerCAmelCase												=  depth_estimator(
						    [
						        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
						        "http://images.cocodataset.org/val2017/000000039769.jpg",
						        # RGBA
						        dataset[0]["file"],
						        # LA
						        dataset[1]["file"],
						        # L
						        dataset[2]["file"],
						    ]     )
						self.assertEqual(
						    [
						        {"predicted_depth": ANY(torch.Tensor     ), "depth": ANY(Image.Image     )},
						        {"predicted_depth": ANY(torch.Tensor     ), "depth": ANY(Image.Image     )},
						        {"predicted_depth": ANY(torch.Tensor     ), "depth": ANY(Image.Image     )},
						        {"predicted_depth": ANY(torch.Tensor     ), "depth": ANY(Image.Image     )},
						        {"predicted_depth": ANY(torch.Tensor     ), "depth": ANY(Image.Image     )},
						    ]			,				UpperCamelCase			,				)



					@require_tf
					@unittest.skip("Depth estimation is not implemented in TF"     )
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						pass



					@slow
					@require_torch
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  "Intel/dpt-large"
						__lowerCAmelCase												=  pipeline("depth-estimation"			,				model=UpperCamelCase     )
						__lowerCAmelCase												=  depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"     )
						__lowerCAmelCase												=  hashimage(outputs["depth"]     )

						# This seems flaky.
						# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
						self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()     )			,				29.3_04     )
						self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()     )			,				2.6_62     )



					@require_torch
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						# This is highly irregular to have no small tests.
						self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"     )




'''simple docstring'''



import requests
from bsa import BeautifulSoup



def   __lowerCAmelCase					(							lowerCamelCase   :						str = "https://www.worldometers.info/coronavirus"					):






	'''simple docstring'''




	__lowerCAmelCase												=  BeautifulSoup(requests.get(lowerCamelCase					).text				,		"html.parser"					)
	__lowerCAmelCase												=  soup.findAll("h1"					)
	__lowerCAmelCase												=  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(lowerCamelCase				,		lowerCamelCase					)}


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')