

'''simple docstring'''

from collections import namedtuple

import requests
from lxml import html  # type: ignore

lowercase__         =      namedtuple("covid_data", "cases deaths recovered")




def 				UpperCamelCase(					UpperCAmelCase_ = "https://www.worldometers.info/coronavirus/"     ):
 UpperCAmelCase	:     Dict						 =			'//div[@class = "maincounter-number"]/span/text()'
 return covid_data(*html.fromstring(requests.get(__lowerCAmelCase     ).content     ).xpath(__lowerCAmelCase     )     )


lowercase__         =      '''Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}'''




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






'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

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


lowercase__         =      logging.get_logger(__name__)


if is_vision_available():
 import PIL




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  ["""pixel_values"""]


     def __init__(		self			:					Dict					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BICUBIC					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Union[int, float] = 1 / 255					,      lowercase_			:					bool = True					,      lowercase_			:					Optional[Union[float, List[float]]] = None					,      lowercase_			:					Optional[Union[float, List[float]]] = None					,      lowercase_			:					bool = True					,      **lowercase_			:					Any					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Dict						 =			size if size is not None else {'shortest_edge': 224}
      UpperCAmelCase	:     Any						 =			get_size_dict(lowercase_					,      default_to_square=lowercase_	)
      UpperCAmelCase	:     List[str]						 =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
      UpperCAmelCase	:     Any						 =			get_size_dict(lowercase_					,      default_to_square=lowercase_					,      param_name='crop_size'	)

      UpperCAmelCase	:     Optional[int]						 =			do_resize
      UpperCAmelCase	:     str						 =			size
      UpperCAmelCase	:     str						 =			resample
      UpperCAmelCase	:     Union[str, Any]						 =			do_center_crop
      UpperCAmelCase	:     Any						 =			crop_size
      UpperCAmelCase	:     Optional[Any]						 =			do_rescale
      UpperCAmelCase	:     List[Any]						 =			rescale_factor
      UpperCAmelCase	:     Any						 =			do_normalize
      UpperCAmelCase	:     int						 =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      UpperCAmelCase	:     str						 =			image_std if image_std is not None else OPENAI_CLIP_STD
      UpperCAmelCase	:     Union[str, Any]						 =			do_convert_rgb


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BICUBIC					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					int					,      )						->     np.ndarray:
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_					,      default_to_square=lowercase_	)
      if "shortest_edge" not in size:
       raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
      UpperCAmelCase	:     str						 =			get_resize_output_image_size(lowercase_					,      size=size['shortest_edge']					,      default_to_square=lowercase_	)
      return resize(lowercase_					,      size=lowercase_					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Tuple					,      )						->     np.ndarray:
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""	)
      return center_crop(lowercase_					,      size=(size['height'], size['width'])					,      data_format=lowercase_					,      **lowercase_	)


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


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Union[float, List[float]]					,      lowercase_			:					Union[float, List[float]]					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Optional[int]					,      )						->     np.ndarray:
      return normalize(lowercase_					,      mean=lowercase_					,      std=lowercase_					,      data_format=lowercase_					,      **lowercase_	)




     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					int = None					,      lowercase_			:					bool = None					,      lowercase_			:					float = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[Union[float, List[float]]] = None					,      lowercase_			:					Optional[Union[float, List[float]]] = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[ChannelDimension] = ChannelDimension.FIRST					,      **lowercase_			:					List[Any]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Any						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     List[str]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_					,      param_name='size'					,      default_to_square=lowercase_	)
      UpperCAmelCase	:     int						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     List[Any]						 =			do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCAmelCase	:     List[Any]						 =			crop_size if crop_size is not None else self.crop_size
      UpperCAmelCase	:     Union[str, Any]						 =			get_size_dict(lowercase_					,      param_name='crop_size'					,      default_to_square=lowercase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			do_rescale if do_rescale is not None else self.do_rescale
      UpperCAmelCase	:     Optional[int]						 =			rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCAmelCase	:     Tuple						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			image_mean if image_mean is not None else self.image_mean
      UpperCAmelCase	:     Optional[Any]						 =			image_std if image_std is not None else self.image_std
      UpperCAmelCase	:     List[Any]						 =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      UpperCAmelCase	:     Union[str, Any]						 =			make_list_of_images(lowercase_	)

      if not valid_images(lowercase_	):
       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.'	)

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
       UpperCAmelCase	:     int						 =			[convert_to_rgb(lowercase_	) for image in images]

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     List[Any]						 =			[to_numpy_array(lowercase_	) for image in images]

      if do_resize:
       UpperCAmelCase	:     Dict						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

      if do_center_crop:
       UpperCAmelCase	:     List[str]						 =			[self.center_crop(image=lowercase_					,      size=lowercase_	) for image in images]

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

      if do_normalize:
       UpperCAmelCase	:     List[Any]						 =			[self.normalize(image=lowercase_					,      mean=lowercase_					,      std=lowercase_	) for image in images]

      UpperCAmelCase	:     Optional[int]						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     str						 =			{'pixel_values': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




