


"""simple docstring"""
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


__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      logging.get_logger(__name__)





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Dict							 =      ["""pixel_values"""]



							def __init__(			self							: List[Any]						,    UpperCAmelCase_							: bool = True						,    UpperCAmelCase_							: Union[int, float] = 1 / 255						,    UpperCAmelCase_							: bool = True						,    UpperCAmelCase_							: int = 8						,    **UpperCAmelCase_							: Any						,    )   ->None:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_    )

														snake_case_          =						do_rescale
														snake_case_          =						rescale_factor
														snake_case_          =						do_pad
														snake_case_          =						pad_size



							def 						lowerCAmelCase    (			self							: Union[str, Any]						,    UpperCAmelCase_							: np.ndarray						,    UpperCAmelCase_							: float						,    UpperCAmelCase_							: Optional[Union[str, ChannelDimension]] = None						,    **UpperCAmelCase_							: int    )   ->np.ndarray:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: np.ndarray						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: Optional[Union[str, ChannelDimension]] = None    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_    ,  snake_case_          =						get_image_size(UpperCAmelCase_    )
														snake_case_          =						(old_height // size + 1) * size - old_height
														snake_case_          =						(old_width // size + 1) * size - old_width

														return pad(UpperCAmelCase_						,    ((0, pad_height), (0, pad_width))						,    mode="""symmetric"""						,    data_format=UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: ImageInput						,    UpperCAmelCase_							: Optional[bool] = None						,    UpperCAmelCase_							: Optional[float] = None						,    UpperCAmelCase_							: Optional[bool] = None						,    UpperCAmelCase_							: Optional[int] = None						,    UpperCAmelCase_							: Optional[Union[str, TensorType]] = None						,    UpperCAmelCase_							: Union[str, ChannelDimension] = ChannelDimension.FIRST						,    **UpperCAmelCase_							: Union[str, Any]						,    )   ->int:



														"""simple docstring"""



														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_pad if do_pad is not None else self.do_pad
														snake_case_          =						pad_size if pad_size is not None else self.pad_size

														snake_case_          =						make_list_of_images(UpperCAmelCase_    )

														if not valid_images(UpperCAmelCase_    ):
																					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.
														snake_case_          =						[to_numpy_array(UpperCAmelCase_    ) for image in images]

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

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

														snake_case_          =						[to_channel_dimension_format(UpperCAmelCase_						,    UpperCAmelCase_    ) for image in images]

														snake_case_          =						{"""pixel_values""": images}
														return BatchFeature(data=UpperCAmelCase_						,    tensor_type=UpperCAmelCase_    )







"""simple docstring"""
from collections import namedtuple

import requests
from lxml import html  # type: ignore

__SCREAMING_SNAKE_CASE							:      List[str]						=      namedtuple('covid_data', 'cases deaths recovered')







def   _a			( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/"			)      ->			covid_data:
							snake_case_          =						"""//div[@class = \"maincounter-number\"]/span/text()"""
							return covid_data(*html.fromstring(requests.get(_SCREAMING_SNAKE_CASE			).content			).xpath(_SCREAMING_SNAKE_CASE			)			)


__SCREAMING_SNAKE_CASE							:      List[str]						=      'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'





print(fmt.format(*covid_stats()))



