



'''simple docstring'''



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					# to overwrite at feature extractactor specific tests
					a      :     int        =							None
					a      :     Any        =							None



					@property
					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						return self.feat_extract_tester.prepare_feat_extract_dict()



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.feature_extraction_class(**self.feat_extract_dict     )
						self.assertTrue(hasattr(UpperCamelCase			,				"feature_size"     )     )
						self.assertTrue(hasattr(UpperCamelCase			,				"sampling_rate"     )     )
						self.assertTrue(hasattr(UpperCamelCase			,				"padding_value"     )     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase												=  self.feature_extraction_class(**self.feat_extract_dict     )
						__lowerCAmelCase												=  feat_extract.model_input_names[0]

						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}     )

						self.assertTrue(all(len(UpperCamelCase     ) == len(UpperCamelCase     ) for x, y in zip(UpperCamelCase			,				processed_features[input_name]     )     )     )

						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase     )
						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}			,				tensor_type="np"     )

						__lowerCAmelCase												=  processed_features[input_name]

						if len(batch_features_input.shape     ) < 3:
							__lowerCAmelCase												=  batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.feature_size)     )



					@require_torch
					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase     )
						__lowerCAmelCase												=  self.feature_extraction_class(**self.feat_extract_dict     )
						__lowerCAmelCase												=  feat_extract.model_input_names[0]

						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}			,				tensor_type="pt"     )

						__lowerCAmelCase												=  processed_features[input_name]

						if len(batch_features_input.shape     ) < 3:
							__lowerCAmelCase												=  batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.feature_size)     )



					@require_tf
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase     )
						__lowerCAmelCase												=  self.feature_extraction_class(**self.feat_extract_dict     )
						__lowerCAmelCase												=  feat_extract.model_input_names[0]

						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}			,				tensor_type="tf"     )

						__lowerCAmelCase												=  processed_features[input_name]

						if len(batch_features_input.shape     ) < 3:
							__lowerCAmelCase												=  batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.feature_size)     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase=False     )     ->			Any:
						def _inputs_have_equal_length(UpperCamelCase     ):
							__lowerCAmelCase												=  len(input[0]     )
							for input_slice in input[1:]:
								if len(UpperCamelCase     ) != length:
									return False
							return True

						def _inputs_are_equal(UpperCamelCase			,				UpperCamelCase     ):
							if len(UpperCamelCase     ) != len(UpperCamelCase     ):
								return False

							for input_slice_a, input_slice_a in zip(UpperCamelCase			,				UpperCamelCase     ):
								if not np.allclose(np.asarray(UpperCamelCase     )			,				np.asarray(UpperCamelCase     )			,				atol=1E-3     ):
									return False
							return True

						__lowerCAmelCase												=  self.feature_extraction_class(**self.feat_extract_dict     )
						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase     )
						__lowerCAmelCase												=  feat_extract.model_input_names[0]

						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}     )

						__lowerCAmelCase												=  self.feat_extract_tester.seq_length_diff
						__lowerCAmelCase												=  self.feat_extract_tester.max_seq_length + pad_diff
						__lowerCAmelCase												=  self.feat_extract_tester.min_seq_length
						__lowerCAmelCase												=  self.feat_extract_tester.batch_size
						__lowerCAmelCase												=  self.feat_extract_tester.feature_size

						# test padding for List[int] + numpy
						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding=UpperCamelCase     )
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="longest"     )
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[-1]     )     )
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="longest"			,				return_tensors="np"     )
						__lowerCAmelCase												=  input_a[input_name]

						# max_length parameter has to be provided when setting `padding="max_length"`
						with self.assertRaises(UpperCamelCase     ):
							feat_extract.pad(UpperCamelCase			,				padding="max_length"     )[input_name]

						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=UpperCamelCase			,				return_tensors="np"     )
						__lowerCAmelCase												=  input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(UpperCamelCase     )     )
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase     )     )
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase     )     )
						self.assertTrue(_inputs_are_equal(UpperCamelCase			,				UpperCamelCase     )     )
						self.assertTrue(len(input_a[0]     ) == pad_min_length     )
						self.assertTrue(len(input_a[1]     ) == pad_min_length + pad_diff     )
						self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]     ))     )
						self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)     )

						if feature_size > 1:
							self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size     )

						# test padding for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				pad_to_multiple_of=10     )
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="longest"			,				pad_to_multiple_of=10     )
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				pad_to_multiple_of=10			,				max_length=UpperCamelCase     )
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				pad_to_multiple_of=10			,				max_length=UpperCamelCase			,				return_tensors="np"			,				)
						__lowerCAmelCase												=  input_a[input_name]

						self.assertTrue(all(len(UpperCamelCase     ) % 10 == 0 for x in input_a     )     )
						self.assertTrue(_inputs_are_equal(UpperCamelCase			,				UpperCamelCase     )     )

						__lowerCAmelCase												=  pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
						self.assertTrue(all(len(UpperCamelCase     ) == expected_mult_pad_length for x in input_a     )     )
						self.assertEqual(input_a.shape[:2]			,				(batch_size, expected_mult_pad_length)     )

						if feature_size > 1:
							self.assertTrue(input_a.shape[2] == feature_size     )

						# Check padding value is correct
						__lowerCAmelCase												=  (np.ones(self.feat_extract_tester.feature_size     ) * feat_extract.padding_value).sum()
						self.assertTrue(
						    abs(np.asarray(input_a[0]     )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)     )
						    < 1E-3     )
						self.assertTrue(
						    abs(
						        np.asarray(input_a[1]     )[pad_min_length + pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)     )
						    < 1E-3     )
						self.assertTrue(
						    abs(
						        np.asarray(input_a[2]     )[pad_min_length + 2 * pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)     )
						    < 1E-3     )
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)     ) < 1E-3     )
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)     )
						    < 1E-3     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase=False     )     ->			List[Any]:
						def _inputs_have_equal_length(UpperCamelCase     ):
							__lowerCAmelCase												=  len(input[0]     )
							for input_slice in input[1:]:
								if len(UpperCamelCase     ) != length:
									return False
							return True

						def _inputs_are_equal(UpperCamelCase			,				UpperCamelCase     ):
							if len(UpperCamelCase     ) != len(UpperCamelCase     ):
								return False

							for input_slice_a, input_slice_a in zip(UpperCamelCase			,				UpperCamelCase     ):
								if not np.allclose(np.asarray(UpperCamelCase     )			,				np.asarray(UpperCamelCase     )			,				atol=1E-3     ):
									return False
							return True

						__lowerCAmelCase												=  self.feature_extraction_class(**self.feat_extract_dict     )
						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase     )
						__lowerCAmelCase												=  feat_extract.model_input_names[0]

						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}     )

						# truncate to smallest
						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[0]     )			,				truncation=UpperCamelCase     )
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[0]     )     )
						__lowerCAmelCase												=  input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase     )     )
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase     )     )

						# truncate to smallest with np
						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[0]     )			,				return_tensors="np"			,				truncation=UpperCamelCase			,				)
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[0]     )			,				return_tensors="np"     )
						__lowerCAmelCase												=  input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase     )     )
						self.assertTrue(input_a.shape[1] == len(speech_inputs[0]     )     )

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase     )     )

						# truncate to middle
						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[1]     )			,				truncation=UpperCamelCase			,				return_tensors="np"			,				)
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[1]     )			,				truncation=UpperCamelCase     )
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[1]     )			,				return_tensors="np"     )
						__lowerCAmelCase												=  input_a[input_name]

						self.assertTrue(input_a.shape[1] == len(speech_inputs[1]     )     )
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase     )     )
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase     )     )
						self.assertTrue(_inputs_are_equal(UpperCamelCase			,				UpperCamelCase     )     )

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase     )     )
						self.assertTrue(len(input_a[-1]     ) == len(speech_inputs[-1]     )     )

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase     ):
							feat_extract.pad(UpperCamelCase			,				truncation=UpperCamelCase     )[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase     ):
							feat_extract.pad(UpperCamelCase			,				padding="longest"			,				truncation=UpperCamelCase     )[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase     ):
							feat_extract.pad(UpperCamelCase			,				padding="longest"			,				truncation=UpperCamelCase     )[input_name]

						# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
						with self.assertRaises(UpperCamelCase     ):
							feat_extract.pad(UpperCamelCase			,				padding="max_length"			,				truncation=UpperCamelCase     )[input_name]

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase												=  12
						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[0]     )			,				pad_to_multiple_of=UpperCamelCase			,				truncation=UpperCamelCase			,				)
						__lowerCAmelCase												=  input_a[input_name]

						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=len(speech_inputs[0]     )			,				pad_to_multiple_of=UpperCamelCase			,				)
						__lowerCAmelCase												=  input_a[input_name]

						# retrieve expected_length as multiple of pad_to_multiple_of
						__lowerCAmelCase												=  len(speech_inputs[0]     )
						if expected_length % pad_to_multiple_of != 0:
							__lowerCAmelCase												=  ((len(speech_inputs[0]     ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

						self.assertTrue(len(input_a[0]     ) == expected_length     )
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase     )     )
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase     )     )



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						self._check_padding(numpify=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						self._check_padding(numpify=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						self._check_truncation(numpify=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						self._check_truncation(numpify=UpperCamelCase     )



					@require_torch
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  self.feature_extraction_class(**self.feat_extract_dict     )
						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase												=  feat_extract.model_input_names[0]

						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}     )

						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="longest"			,				return_tensors="np"     )[input_name]
						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="longest"			,				return_tensors="pt"     )[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa     ).sum() - input_pt.numpy().astype(np.floataa     ).sum()     ) < 1E-2     )



					@require_tf
					def        UpperCAmelCase_ (    self     )     ->			int:
						__lowerCAmelCase												=  self.feature_extraction_class(**self.feat_extract_dict     )
						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase												=  feat_extract.model_input_names[0]

						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}     )

						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="longest"			,				return_tensors="np"     )[input_name]
						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="longest"			,				return_tensors="tf"     )[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa     ).sum() - input_tf.numpy().astype(np.floataa     ).sum()     ) < 1E-2     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.feat_extract_dict
						__lowerCAmelCase												=  True
						__lowerCAmelCase												=  self.feature_extraction_class(**UpperCamelCase     )
						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase												=  [len(UpperCamelCase     ) for x in speech_inputs]
						__lowerCAmelCase												=  feat_extract.model_input_names[0]

						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}     )

						__lowerCAmelCase												=  feat_extract.pad(UpperCamelCase			,				padding="longest"			,				return_tensors="np"     )
						self.assertIn("attention_mask"			,				UpperCamelCase     )
						self.assertListEqual(list(processed.attention_mask.shape     )			,				list(processed[input_name].shape[:2]     )     )
						self.assertListEqual(processed.attention_mask.sum(-1     ).tolist()			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.feat_extract_dict
						__lowerCAmelCase												=  True
						__lowerCAmelCase												=  self.feature_extraction_class(**UpperCamelCase     )
						__lowerCAmelCase												=  self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase												=  [len(UpperCamelCase     ) for x in speech_inputs]
						__lowerCAmelCase												=  feat_extract.model_input_names[0]

						__lowerCAmelCase												=  BatchFeature({input_name: speech_inputs}     )
						__lowerCAmelCase												=  min(UpperCamelCase     )

						__lowerCAmelCase												=  feat_extract.pad(
						    UpperCamelCase			,				padding="max_length"			,				max_length=UpperCamelCase			,				truncation=UpperCamelCase			,				return_tensors="np"     )
						self.assertIn("attention_mask"			,				UpperCamelCase     )
						self.assertListEqual(
						    list(processed_pad.attention_mask.shape     )			,				[processed_pad[input_name].shape[0], max_length]     )
						self.assertListEqual(
						    processed_pad.attention_mask[:, :max_length].sum(-1     ).tolist()			,				[max_length for x in speech_inputs]     )




'''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')