





'''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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case_     =	logging.get_logger(__name__)


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


   def __init__( self    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = PILImageResampling.BICUBIC    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = 1 / 255    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       super().__init__(**_UpperCAmelCase					)
       A       :       Optional[int]							=						size if size is not None else {'''height''': 224, '''width''': 224}
       A       :       List[Any]							=						get_size_dict(_UpperCAmelCase					)
       A       :       Tuple							=						crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       A       :       Dict							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase    ,   param_name='''crop_size'''					)

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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Optional[int]							=						get_size_dict(_UpperCAmelCase					)

       if "shortest_edge" in size:
           A       :       Any							=						get_resize_output_image_size(_UpperCAmelCase    ,   size=size['''shortest_edge''']    ,   default_to_square=_UpperCAmelCase					)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
       elif "height" in size and "width" in size:
           A       :       Optional[int]							=						(size['''height'''], size['''width'''])
       else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''					)
       return resize(_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Any							=						get_size_dict(_UpperCAmelCase					)
       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(_UpperCAmelCase    ,   size=(size['''height'''], size['''width'''])    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


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


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


   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       :       Any							=						do_resize if do_resize is not None else self.do_resize
       A       :       int							=						do_rescale if do_rescale is not None else self.do_rescale
       A       :       Union[str, Any]							=						do_normalize if do_normalize is not None else self.do_normalize
       A       :       Union[str, Any]							=						do_center_crop if do_center_crop is not None else self.do_center_crop
       A       :       Any							=						crop_size if crop_size is not None else self.crop_size
       A       :       Optional[Any]							=						get_size_dict(_UpperCAmelCase    ,   param_name='''crop_size'''    ,   default_to_square=_UpperCAmelCase					)
       A       :       Optional[Any]							=						resample if resample is not None else self.resample
       A       :       Optional[Any]							=						rescale_factor if rescale_factor is not None else self.rescale_factor
       A       :       str							=						image_mean if image_mean is not None else self.image_mean
       A       :       Dict							=						image_std if image_std is not None else self.image_std

       A       :       int							=						size if size is not None else self.size
       A       :       Union[str, Any]							=						get_size_dict(_UpperCAmelCase					)

       if not is_batched(_UpperCAmelCase					):
           A       :       Tuple							=						[images]

       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_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.'''					)

       # All transformations expect numpy arrays.
       A       :       Optional[Any]							=						[to_numpy_array(_UpperCAmelCase					) for image in images]

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

       if do_center_crop:
           A       :       Union[str, Any]							=						[self.center_crop(image=_UpperCAmelCase    ,   size=_UpperCAmelCase					) for image in images]

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

       if do_normalize:
           A       :       int							=						[self.normalize(image=_UpperCAmelCase    ,   mean=_UpperCAmelCase    ,   std=_UpperCAmelCase					) for image in images]

       A       :       List[Any]							=						[to_channel_dimension_format(_UpperCAmelCase    ,   _UpperCAmelCase					) for image in images]

       A       :       int							=						{'''pixel_values''': images}
       return BatchFeature(data=_UpperCAmelCase    ,   tensor_type=_UpperCAmelCase					)







'''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>""")
