



from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( __UpperCAmelCase):
       lowerCamelCase__         =	['pixel_values']







       def __init__(     self,    __a = True,    __a = None,    __a = PILImageResampling.BILINEAR,    __a = True,    __a = 1 / 255,    __a = True,    __a = None,    __a = True,    **__a,    ):

              '''simple docstring'''

              super().__init__(**lowerCAmelCase_)
              _lowerCAmelCase  :							Any				     =					size if size is not None else {"shortest_edge": 224}
              _lowerCAmelCase  :							int				     =					get_size_dict(lowerCAmelCase_,    default_to_square=lowerCAmelCase_)
              _lowerCAmelCase  :							List[str]				     =					crop_size if crop_size is not None else {"height": 256, "width": 256}
              _lowerCAmelCase  :							Optional[Any]				     =					get_size_dict(lowerCAmelCase_,    param_name="crop_size")

              _lowerCAmelCase  :							Tuple				     =					do_resize
              _lowerCAmelCase  :							Optional[Any]				     =					size
              _lowerCAmelCase  :							str				     =					resample
              _lowerCAmelCase  :							List[Any]				     =					do_rescale
              _lowerCAmelCase  :							Dict				     =					rescale_factor
              _lowerCAmelCase  :							str				     =					do_center_crop
              _lowerCAmelCase  :							int				     =					crop_size
              _lowerCAmelCase  :							str				     =					do_flip_channel_order







       def      snake_case__   (     self,    __a,    __a,    __a = PIL.Image.BILINEAR,    __a = None,    **__a,    ):

              '''simple docstring'''

              _lowerCAmelCase  :							List[Any]				     =					get_size_dict(lowerCAmelCase_,    default_to_square=lowerCAmelCase_)
              if "shortest_edge" not in size:
                     raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}")
              _lowerCAmelCase  :							str				     =					get_resize_output_image_size(lowerCAmelCase_,    size=size["shortest_edge"],    default_to_square=lowerCAmelCase_)
              return resize(lowerCAmelCase_,    size=lowerCAmelCase_,    resample=lowerCAmelCase_,    data_format=lowerCAmelCase_,    **lowerCAmelCase_)







       def      snake_case__   (     self,    __a,    __a,    __a = None,    **__a,    ):

              '''simple docstring'''

              _lowerCAmelCase  :							int				     =					get_size_dict(lowerCAmelCase_)
              if "height" not in size or "width" not in size:
                     raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
              return center_crop(lowerCAmelCase_,    size=(size["height"], size["width"]),    data_format=lowerCAmelCase_,    **lowerCAmelCase_)







       def      snake_case__   (     self,    __a,    __a,    __a = None,    **__a,    ):

              '''simple docstring'''

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







       def      snake_case__   (     self,    __a,    __a = None):

              '''simple docstring'''

              return flip_channel_order(lowerCAmelCase_,    data_format=lowerCAmelCase_)







       def      snake_case__   (     self,    __a,    __a = None,    __a = None,    __a = None,    __a = None,    __a = None,    __a = None,    __a = None,    __a = None,    __a = None,    __a = ChannelDimension.FIRST,    **__a,    ):

              '''simple docstring'''

              _lowerCAmelCase  :							Optional[Any]				     =					do_resize if do_resize is not None else self.do_resize
              _lowerCAmelCase  :							List[str]				     =					resample if resample is not None else self.resample
              _lowerCAmelCase  :							Any				     =					do_rescale if do_rescale is not None else self.do_rescale
              _lowerCAmelCase  :							List[str]				     =					rescale_factor if rescale_factor is not None else self.rescale_factor
              _lowerCAmelCase  :							Union[str, Any]				     =					do_center_crop if do_center_crop is not None else self.do_center_crop
              _lowerCAmelCase  :							int				     =					(
                  do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
              )

              _lowerCAmelCase  :							Union[str, Any]				     =					size if size is not None else self.size
              _lowerCAmelCase  :							Any				     =					get_size_dict(lowerCAmelCase_,    default_to_square=lowerCAmelCase_)
              _lowerCAmelCase  :							Tuple				     =					crop_size if crop_size is not None else self.crop_size
              _lowerCAmelCase  :							Union[str, Any]				     =					get_size_dict(lowerCAmelCase_,    param_name="crop_size")

              _lowerCAmelCase  :							Optional[int]				     =					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_rescale and rescale_factor is None:
                     raise ValueError("Rescale factor must be specified if do_rescale is True.")

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

              # All transformations expect numpy arrays.
              _lowerCAmelCase  :							List[str]				     =					[to_numpy_array(lowerCAmelCase_) for image in images]

              if do_resize:
                     _lowerCAmelCase  :							int				     =					[self.resize(image=lowerCAmelCase_,    size=lowerCAmelCase_,    resample=lowerCAmelCase_) for image in images]

              if do_center_crop:
                     _lowerCAmelCase  :							List[Any]				     =					[self.center_crop(image=lowerCAmelCase_,    size=lowerCAmelCase_) for image in images]

              if do_rescale:
                     _lowerCAmelCase  :							str				     =					[self.rescale(image=lowerCAmelCase_,    scale=lowerCAmelCase_) for image in images]

              # the pretrained checkpoints assume images are BGR, not RGB
              if do_flip_channel_order:
                     _lowerCAmelCase  :							Union[str, Any]				     =					[self.flip_channel_order(image=lowerCAmelCase_) for image in images]

              _lowerCAmelCase  :							Any				     =					[to_channel_dimension_format(lowerCAmelCase_,    lowerCAmelCase_) for image in images]

              _lowerCAmelCase  :							List[str]				     =					{"pixel_values": images}
              return BatchFeature(data=lowerCAmelCase_,    tensor_type=lowerCAmelCase_)







       def      snake_case__   (     self,    __a,    __a = None):

              '''simple docstring'''

              _lowerCAmelCase  :							Any				     =					outputs.logits

              # Resize logits and compute semantic segmentation maps
              if target_sizes is not None:
                     if len(lowerCAmelCase_) != len(lowerCAmelCase_):
                            raise ValueError(
                                "Make sure that you pass in as many target sizes as the batch dimension of the logits")

                     if is_torch_tensor(lowerCAmelCase_):
                            _lowerCAmelCase  :							Union[str, Any]				     =					target_sizes.numpy()

                     _lowerCAmelCase  :							str				     =					[]

                     for idx in range(len(lowerCAmelCase_)):
                            _lowerCAmelCase  :							Tuple				     =					torch.nn.functional.interpolate(
                                logits[idx].unsqueeze(dim=0),    size=target_sizes[idx],    mode="bilinear",    align_corners=lowerCAmelCase_)
                            _lowerCAmelCase  :							str				     =					resized_logits[0].argmax(dim=0)
                            semantic_segmentation.append(lowerCAmelCase_)
              else:
                     _lowerCAmelCase  :							Tuple				     =					logits.argmax(dim=1)
                     _lowerCAmelCase  :							Any				     =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

              return semantic_segmentation





import requests
from bsa import BeautifulSoup






def        A       (      _lowerCamelCase = "https://www.worldometers.info/coronavirus"						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					BeautifulSoup(requests.get(_lowerCamelCase						).text       ,				"html.parser"						)
							_lowerCAmelCase  :							str				     =					soup.findAll("h1"						)
							_lowerCAmelCase  :							Optional[int]				     =					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''')
