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,
    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, is_vision_available, logging


if is_vision_available():
      import PIL


SCREAMING_SNAKE_CASE__		:					int					   =					logging.get_logger(__name__)

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     ['pixel_values']




  def __init__(      self     :	int     , a_     :	bool = True     , a_     :	Dict[str, int] = None     , a_     :	int = 0.9     , a_     :	PILImageResampling = PILImageResampling.BICUBIC     , a_     :	bool = True     , a_     :	Dict[str, int] = None     , a_     :	Union[int, float] = 1 / 255     , a_     :	bool = True     , a_     :	bool = True     , a_     :	Optional[Union[float, List[float]]] = None     , a_     :	Optional[Union[float, List[float]]] = None     , **a_     :	List[Any]     , )->       None:




       """simple docstring"""
       super().__init__(**a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		size if size is not None else {'shortest_edge': 224}
       SCREAMING_SNAKE_CASE__				:				Tuple			=		get_size_dict(a_     , default_to_square=a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		crop_size if crop_size is not None else {'height': 224, 'width': 224}
       SCREAMING_SNAKE_CASE__				:				str			=		get_size_dict(a_     , param_name='crop_size'				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		do_resize
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		crop_pct
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		resample
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_center_crop
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		crop_size
       SCREAMING_SNAKE_CASE__				:				Any			=		do_rescale
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		rescale_factor
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				List[str]			=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       SCREAMING_SNAKE_CASE__				:				Any			=		image_std if image_std is not None else IMAGENET_DEFAULT_STD




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	np.ndarray     , a_     :	Dict[str, int]     , a_     :	Optional[float] = None     , a_     :	PILImageResampling = PILImageResampling.BICUBIC     , a_     :	Optional[Union[str, ChannelDimension]] = None     , **a_     :	Dict     , )->       np.ndarray:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		get_size_dict(a_     , default_to_square=a_				)
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
            raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''				)
       if crop_pct is not None:
            if "shortest_edge" in size:
                 SCREAMING_SNAKE_CASE__				:				Dict			=		int(size['shortest_edge'] / crop_pct				)
            elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                      SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		int(size['height'] / crop_pct				)
                 else:
                      SCREAMING_SNAKE_CASE__				:				Any			=		(int(size['height'] / crop_pct				), int(size['width'] / crop_pct				))
            else:
                 raise ValueError('Invalid size for resize: {}'.format(a_				)				)

            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		get_resize_output_image_size(a_     , size=a_     , default_to_square=a_				)
       else:
            if "shortest_edge" in size:
                 SCREAMING_SNAKE_CASE__				:				str			=		get_resize_output_image_size(a_     , size=size['shortest_edge']     , default_to_square=a_				)
            elif "height" in size and "width" in size:
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(size['height'], size['width'])
            else:
                 raise ValueError('Invalid size for resize: {}'.format(a_				)				)

       return resize(a_     , size=a_     , resample=a_     , data_format=a_     , **a_				)




  def 	__lowercase(      self     :	List[str]     , a_     :	np.ndarray     , a_     :	Dict[str, int]     , a_     :	Optional[Union[str, ChannelDimension]] = None     , **a_     :	Dict     , )->       np.ndarray:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		get_size_dict(a_				)
       if "height" not in size or "width" not in size:
            raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''				)
       return center_crop(a_     , size=(size['height'], size['width'])     , data_format=a_     , **a_				)




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




       """simple docstring"""
       return rescale(a_     , scale=a_     , data_format=a_     , **a_				)




  def 	__lowercase(      self     :	List[Any]     , a_     :	np.ndarray     , a_     :	Union[float, List[float]]     , a_     :	Union[float, List[float]]     , a_     :	Optional[Union[str, ChannelDimension]] = None     , **a_     :	List[Any]     , )->       np.ndarray:




       """simple docstring"""
       return normalize(a_     , mean=a_     , std=a_     , data_format=a_     , **a_				)




  def 	__lowercase(      self     :	Optional[int]     , a_     :	ImageInput     , a_     :	bool = None     , a_     :	Dict[str, int] = None     , a_     :	int = None     , a_     :	PILImageResampling = None     , a_     :	bool = None     , a_     :	Dict[str, int] = None     , a_     :	bool = None     , a_     :	float = None     , a_     :	bool = None     , a_     :	Optional[Union[float, List[float]]] = None     , a_     :	Optional[Union[float, List[float]]] = None     , a_     :	Optional[Union[str, TensorType]] = None     , a_     :	ChannelDimension = ChannelDimension.FIRST     , **a_     :	str     , )->       PIL.Image.Image:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__				:				str			=		crop_pct if crop_pct is not None else self.crop_pct
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__				:				int			=		rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__				:				str			=		do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__				:				Any			=		image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		size if size is not None else self.size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		get_size_dict(a_     , default_to_square=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__				:				Any			=		get_size_dict(a_     , param_name='crop_size'				)

       SCREAMING_SNAKE_CASE__				:				int			=		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_resize and size is None or resample is None:
            raise ValueError('Size and resample must be specified if do_resize is True.'				)

       if do_center_crop and crop_pct is None:
            raise ValueError('Crop_pct 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.
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[to_numpy_array(a_				) for image in images]

       if do_resize:
            SCREAMING_SNAKE_CASE__				:				int			=		[self.resize(image=a_     , size=a_     , crop_pct=a_     , resample=a_				) for image in images]

       if do_center_crop:
            SCREAMING_SNAKE_CASE__				:				str			=		[self.center_crop(image=a_     , size=a_				) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__				:				Any			=		[self.rescale(image=a_     , scale=a_				) for image in images]

       if do_normalize:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[self.normalize(image=a_     , mean=a_     , std=a_				) for image in images]

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[to_channel_dimension_format(a_     , a_				) for image in images]

       SCREAMING_SNAKE_CASE__				:				Any			=		{'pixel_values': images}
       return BatchFeature(data=a_     , tensor_type=a_				)




import requests

SCREAMING_SNAKE_CASE__		:					int						=					"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		requests.get(_NEWS_API + bbc_news_api_key ).json()
     # each article in the list is a dict
     for i, article in enumerate(bbc_news_page['articles']     ,  1 ):
          print(f'''{i}.) {article['title']}''' )


if __name__ == "__main__":
      fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")



