





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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
     import torch

if is_vision_available():
     import PIL


snake_case_     =	logging.get_logger(__name__)
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : str		)		->  Tuple[int, int]:

    def constraint_to_multiple_of(UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : Dict=0    ,       UpperCamelCase__    : Optional[int]=None		):
        A       :       Any							=						round(val / multiple		) * multiple

        if max_val is not None and x > max_val:
            A       :       Optional[int]							=						math.floor(val / multiple		) * multiple

        if x < min_val:
            A       :       List[str]							=						math.ceil(val / multiple		) * multiple

        return x

    A       :       Optional[int]							=						(output_size, output_size) if isinstance(a__    ,       a__		) else output_size

    A,	A       :       Any							=						get_image_size(a__		)
    A,	A       :       Dict							=						output_size

    # determine new height and width
    A       :       Union[str, Any]							=						output_height / input_height
    A       :       Tuple							=						output_width / input_width

    if keep_aspect_ratio:
        # scale as little as possible
        if abs(1 - scale_width		) < abs(1 - scale_height		):
            # fit width
            A       :       Any							=						scale_width
        else:
            # fit height
            A       :       str							=						scale_height

    A       :       List[str]							=						constraint_to_multiple_of(scale_height * input_height    ,       multiple=a__		)
    A       :       str							=						constraint_to_multiple_of(scale_width * input_width    ,       multiple=a__		)

    return (new_height, new_width)





class   _lowercase						(							a				):
   _UpperCamelCase       		=   ["""pixel_values"""]


   def __init__( self    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = 1    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = 1 / 255    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       super().__init__(**lowercase_					)
       A       :       Dict							=						size if size is not None else {'''height''': 384, '''width''': 384}
       A       :       str							=						get_size_dict(lowercase_					)
       A       :       Optional[Any]							=						do_resize
       A       :       Dict							=						size
       A       :       Tuple							=						keep_aspect_ratio
       A       :       Optional[int]							=						ensure_multiple_of
       A       :       Dict							=						resample
       A       :       str							=						do_rescale
       A       :       Any							=						rescale_factor
       A       :       Union[str, Any]							=						do_normalize
       A       :       Union[str, Any]							=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A       :       Optional[int]							=						image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = 1    ,   _UpperCAmelCase = PILImageResampling.BICUBIC    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Any							=						get_size_dict(lowercase_					)
       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()}'''					)
       A       :       Any							=						get_resize_output_image_size(
           lowercase_    ,   output_size=(size['''height'''], size['''width'''])    ,   keep_aspect_ratio=lowercase_    ,   multiple=lowercase_    ,   )
       return resize(lowercase_    ,   size=lowercase_    ,   resample=lowercase_    ,   data_format=lowercase_    ,   **lowercase_					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       return rescale(lowercase_    ,   scale=lowercase_    ,   data_format=lowercase_    ,   **lowercase_					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       return normalize(lowercase_    ,   mean=lowercase_    ,   std=lowercase_    ,   data_format=lowercase_    ,   **lowercase_					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = ChannelDimension.FIRST    ,   **_UpperCAmelCase    ,   ):
       A       :       Optional[int]							=						do_resize if do_resize is not None else self.do_resize
       A       :       Tuple							=						size if size is not None else self.size
       A       :       Union[str, Any]							=						get_size_dict(lowercase_					)
       A       :       Optional[Any]							=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       A       :       Optional[Any]							=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       A       :       Union[str, Any]							=						resample if resample is not None else self.resample
       A       :       str							=						do_rescale if do_rescale is not None else self.do_rescale
       A       :       List[Any]							=						rescale_factor if rescale_factor is not None else self.rescale_factor
       A       :       int							=						do_normalize if do_normalize is not None else self.do_normalize
       A       :       List[Any]							=						image_mean if image_mean is not None else self.image_mean
       A       :       Optional[Any]							=						image_std if image_std is not None else self.image_std

       A       :       str							=						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 or resample is None:
           raise ValueError('''Size and resample 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_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.
       A       :       List[str]							=						[to_numpy_array(lowercase_					) for image in images]

       if do_resize:
           A       :       Union[str, Any]							=						[self.resize(image=lowercase_    ,   size=lowercase_    ,   resample=lowercase_					) for image in images]

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

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

       A       :       Dict							=						[to_channel_dimension_format(lowercase_    ,   lowercase_					) for image in images]

       A       :       List[Any]							=						{'''pixel_values''': images}
       return BatchFeature(data=lowercase_    ,   tensor_type=lowercase_					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       Optional[int]							=						outputs.logits

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

           if is_torch_tensor(lowercase_					):
               A       :       List[Any]							=						target_sizes.numpy()

           A       :       Any							=						[]

           for idx in range(len(lowercase_					)					):
               A       :       Any							=						torch.nn.functional.interpolate(
                   logits[idx].unsqueeze(dim=0					)    ,   size=target_sizes[idx]    ,   mode='''bilinear'''    ,   align_corners=lowercase_					)
               A       :       str							=						resized_logits[0].argmax(dim=0					)
               semantic_segmentation.append(lowercase_					)
       else:
           A       :       List[Any]							=						logits.argmax(dim=1					)
           A       :       Optional[int]							=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

       return semantic_segmentation







'''simple docstring'''






import requests

snake_case_     =	"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def 							_lowerCamelCase(  UpperCamelCase__    : str		)		->  None:
    # fetching a list of articles in json format
    A       :       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>""")
