
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowercase__		:int   =    logging.get_logger(__name__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Dict       					=['''pixel_values''']




							def __init__(				self   ,A__ = True   ,A__ = 1 / 2_5_5   ,A__ = True   ,A__ = 8   ,**A__   ,):
												super().__init__(**A__)

												lowercase							=						do_rescale
												lowercase							=						rescale_factor
												lowercase							=						do_pad
												lowercase							=						pad_size




							def  A__      (				self   ,A__   ,A__   ,A__ = None   ,**A__):
												return rescale(A__   ,scale=A__   ,data_format=A__   ,**A__)




							def  A__      (				self   ,A__   ,A__   ,A__ = None):
												lowercase	,						lowercase							=						get_image_size(A__)
												lowercase							=						(old_height // size + 1) * size - old_height
												lowercase							=						(old_width // size + 1) * size - old_width

												return pad(A__   ,((0, pad_height), (0, pad_width))   ,mode='''symmetric'''   ,data_format=A__)





							def  A__      (				self   ,A__   ,A__ = None   ,A__ = None   ,A__ = None   ,A__ = None   ,A__ = None   ,A__ = ChannelDimension.FIRST   ,**A__   ,):
												lowercase							=						do_rescale if do_rescale is not None else self.do_rescale
												lowercase							=						rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase							=						do_pad if do_pad is not None else self.do_pad
												lowercase							=						pad_size if pad_size is not None else self.pad_size

												lowercase							=						make_list_of_images(A__)

												if not valid_images(A__):
																	raise ValueError(
																	    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																	    '''torch.Tensor, tf.Tensor or jax.ndarray.''')

												if do_rescale and rescale_factor is None:
																	raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

												# All transformations expect numpy arrays.
												lowercase							=						[to_numpy_array(A__) for image in images]

												if do_rescale:
																	lowercase							=						[self.rescale(image=A__   ,scale=A__) for image in images]

												if do_pad:
																	lowercase							=						[self.pad(A__   ,size=A__) for image in images]

												lowercase							=						[to_channel_dimension_format(A__   ,A__) for image in images]

												lowercase							=						{'''pixel_values''': images}
												return BatchFeature(data=A__   ,tensor_type=A__)






import requests
from bsa import BeautifulSoup



def        UpperCamelCase    (							lowerCAmelCase__ = "https://www.worldometers.info/coronavirus"		):
					'''simple docstring'''
					lowercase							=						BeautifulSoup(requests.get(lowerCAmelCase__		).text ,			'''html.parser'''		)
					lowercase							=						soup.findAll('''h1'''		)
					lowercase							=						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')




