"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      int     					=  ["""pixel_values"""]

    def __init__(			self						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = PILImageResampling.BICUBIC						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      lowerCAmelCase = 1 / 2_55						,      lowerCAmelCase = True						,      lowerCAmelCase = IMAGENET_DEFAULT_MEAN						,      lowerCAmelCase = IMAGENET_DEFAULT_STD						,      **lowerCAmelCase						,      ):






          """simple docstring"""

          super().__init__(**lowerCAmelCase      )
          snake_case        =     size if size is not None else {'shortest_edge': 2_24}
          snake_case        =     get_size_dict(lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
          snake_case        =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
          snake_case        =     get_size_dict(lowerCAmelCase						,      param_name='crop_size'      )

          snake_case        =     do_resize
          snake_case        =     size
          snake_case        =     resample
          snake_case        =     do_center_crop
          snake_case        =     crop_size
          snake_case        =     do_rescale
          snake_case        =     rescale_factor
          snake_case        =     do_normalize
          snake_case        =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          snake_case        =     image_std if image_std is not None else IMAGENET_DEFAULT_STD

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = PILImageResampling.BICUBIC						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






          """simple docstring"""

          snake_case        =     get_size_dict(lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
          # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
          if "shortest_edge" in size:
                snake_case        =     int((2_56 / 2_24) * size['shortest_edge']      )
                snake_case        =     get_resize_output_image_size(lowerCAmelCase						,      size=lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
                snake_case        =     {'height': output_size[0], 'width': output_size[1]}
          if "height" not in size_dict or "width" not in size_dict:
                raise ValueError(
                    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""      )
          return resize(
              lowerCAmelCase						,      size=(size_dict['height'], size_dict['width'])						,      resample=lowerCAmelCase						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






          """simple docstring"""

          snake_case        =     get_size_dict(lowerCAmelCase      )
          if "height" not in size or "width" not in size:
                raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""      )
          return center_crop(lowerCAmelCase						,      size=(size['height'], size['width'])						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






          """simple docstring"""

          return rescale(lowerCAmelCase						,      scale=lowerCAmelCase						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






          """simple docstring"""

          return normalize(lowerCAmelCase						,      mean=lowerCAmelCase						,      std=lowerCAmelCase						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = ChannelDimension.FIRST						,      **lowerCAmelCase						,      ):






          """simple docstring"""

          snake_case        =     do_resize if do_resize is not None else self.do_resize
          snake_case        =     resample if resample is not None else self.resample
          snake_case        =     do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case        =     do_rescale if do_rescale is not None else self.do_rescale
          snake_case        =     rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case        =     do_normalize if do_normalize is not None else self.do_normalize
          snake_case        =     image_mean if image_mean is not None else self.image_mean
          snake_case        =     image_std if image_std is not None else self.image_std

          snake_case        =     size if size is not None else self.size
          snake_case        =     get_size_dict(lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
          snake_case        =     crop_size if crop_size is not None else self.crop_size
          snake_case        =     get_size_dict(lowerCAmelCase						,      param_name='crop_size'      )

          snake_case        =     make_list_of_images(lowerCAmelCase      )

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

          if do_resize and size is None:
                raise ValueError('Size must be specified if do_resize is True.'      )

          if do_center_crop and crop_size is None:
                raise ValueError('Crop size must be specified if do_center_crop is True.'      )

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

          if do_normalize and (image_mean is None or image_std is None):
                raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

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

          if do_resize:
                snake_case        =     [self.resize(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

          if do_center_crop:
                snake_case        =     [self.center_crop(lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

          if do_rescale:
                snake_case        =     [self.rescale(lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

          if do_normalize:
                snake_case        =     [self.normalize(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

          snake_case        =     [to_channel_dimension_format(lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

          snake_case        =     {'pixel_values': images}
          return BatchFeature(data=lowerCAmelCase						,      tensor_type=lowerCAmelCase      )



"""simple docstring"""
import requests
from bsa import BeautifulSoup





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





						"""simple docstring"""






						snake_case        =     BeautifulSoup(requests.get(_UpperCamelCase   ).text	,     'html.parser'   )
						snake_case        =     soup.findAll('h1'   )
						snake_case        =     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(_UpperCamelCase	,     _UpperCamelCase   )}


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""")


