





"""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 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_vision_available, logging


if is_vision_available():
       import PIL


a_					=     logging.get_logger(__name__)







def 						__lowercase	(  snake_case_    :    Optional[int]							,snake_case_    :    Optional[int]		)  ->List[str]:



    '''simple docstring'''







    __A       :		Any							=	b.T
    __A       :		List[Any]							=	np.sum(np.square(snake_case_		)							,axis=1		)
    __A       :		Any							=	np.sum(np.square(snake_case_		)							,axis=0		)
    __A       :		List[Any]							=	np.matmul(snake_case_							,snake_case_		)
    __A       :		Optional[int]							=	aa[:, None] - 2 * ab + ba[None, :]
    return d







def 						__lowercase	(  snake_case_    :    Dict							,snake_case_    :    Optional[Any]		)  ->List[str]:



    '''simple docstring'''







    __A       :		Union[str, Any]							=	x.reshape(-1							,3		)
    __A       :		Optional[Any]							=	squared_euclidean_distance(snake_case_							,snake_case_		)
    return np.argmin(snake_case_							,axis=1		)






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	["""pixel_values"""]

 def __init__(       self   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = PILImageResampling.BILINEAR   ,		__lowerCamelCase = True   ,		__lowerCamelCase = True   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase    )
     __A       :		Union[str, Any]							=	size if size is not None else {'''height''': 256, '''width''': 256}
     __A       :		str							=	get_size_dict(__lowerCamelCase    )
     __A       :		Dict							=	np.array(__lowerCamelCase    ) if clusters is not None else None
     __A       :		Union[str, Any]							=	do_resize
     __A       :		Union[str, Any]							=	size
     __A       :		Optional[int]							=	resample
     __A       :		List[str]							=	do_normalize
     __A       :		Optional[int]							=	do_color_quantize

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = PILImageResampling.BILINEAR   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		str							=	get_size_dict(__lowerCamelCase    )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""Size dictionary must contain both height and width keys. Got {size.keys()}"""    )
     return resize(
         __lowerCamelCase   ,		size=(size['''height'''], size['''width'''])   ,		resample=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		):



     '''simple docstring'''




     __A       :		int							=	rescale(image=__lowerCamelCase   ,		scale=1 / 1_2_7.5   ,		data_format=__lowerCamelCase    )
     __A       :		Tuple							=	image - 1
     return image







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = ChannelDimension.FIRST   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Dict							=	do_resize if do_resize is not None else self.do_resize
     __A       :		Any							=	size if size is not None else self.size
     __A       :		Dict							=	get_size_dict(__lowerCamelCase    )
     __A       :		List[str]							=	resample if resample is not None else self.resample
     __A       :		Optional[int]							=	do_normalize if do_normalize is not None else self.do_normalize
     __A       :		Tuple							=	do_color_quantize if do_color_quantize is not None else self.do_color_quantize
     __A       :		Dict							=	clusters if clusters is not None else self.clusters
     __A       :		Optional[Any]							=	np.array(__lowerCamelCase    )

     __A       :		List[str]							=	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 or resample is None:
         raise ValueError('''Size and resample must be specified if do_resize is True.'''    )

     if do_color_quantize and clusters is None:
         raise ValueError('''Clusters must be specified if do_color_quantize is True.'''    )

     # All transformations expect numpy arrays.
     __A       :		Optional[int]							=	[to_numpy_array(__lowerCamelCase    ) for image in images]

     if do_resize:
         __A       :		Any							=	[self.resize(image=__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase    ) for image in images]

     if do_normalize:
         __A       :		Union[str, Any]							=	[self.normalize(image=__lowerCamelCase    ) for image in images]

     if do_color_quantize:
         __A       :		Optional[int]							=	[to_channel_dimension_format(__lowerCamelCase   ,		ChannelDimension.LAST    ) for image in images]
         # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
         __A       :		int							=	np.array(__lowerCamelCase    )
         __A       :		Union[str, Any]							=	color_quantize(__lowerCamelCase   ,		__lowerCamelCase    ).reshape(images.shape[:-1]    )

         # flatten to (batch_size, height*width)
         __A       :		Any							=	images.shape[0]
         __A       :		List[Any]							=	images.reshape(__lowerCamelCase   ,		-1    )

         # We need to convert back to a list of images to keep consistent behaviour across processors.
         __A       :		int							=	list(__lowerCamelCase    )
     else:
         __A       :		str							=	[to_channel_dimension_format(__lowerCamelCase   ,		__lowerCamelCase    ) for image in images]

     __A       :		List[Any]							=	{'''input_ids''': images}
     return BatchFeature(data=__lowerCamelCase   ,		tensor_type=__lowerCamelCase    )











"""simple docstring"""



import requests

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







def 						__lowercase	(  snake_case_    :    str		)  ->None:



    '''simple docstring'''







    __A       :		str							=	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>""")




