from typing import Dict, Iterable, Optional, 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, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

SCREAMING_SNAKE_CASE__		:					List[Any]						=					logging.get_logger(__name__)







def     _a				(      lowercase__  :			str     ,  lowercase__  :			int     ,  lowercase__  :			Any ):







     '''simple docstring'''


     return [
         int(10_00 * (box[0] / width) ),
         int(10_00 * (box[1] / height) ),
         int(10_00 * (box[2] / width) ),
         int(10_00 * (box[3] / height) ),
     ]







def     _a				(      lowercase__  :			np.ndarray     ,  lowercase__  :			Optional[str]     ,  lowercase__  :			Optional[str] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		to_pil_image(lowercase__ )
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		pil_image.size
     SCREAMING_SNAKE_CASE__				:				Dict			=		pytesseract.image_to_data(lowercase__     ,  lang=lowercase__     ,  output_type='dict'     ,  config=lowercase__ )
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		data['text'], data['left'], data['top'], data['width'], data['height']

     # filter empty words and corresponding coordinates
     SCREAMING_SNAKE_CASE__				:				List[str]			=		[idx for idx, word in enumerate(lowercase__ ) if not word.strip()]
     SCREAMING_SNAKE_CASE__				:				Any			=		[word for idx, word in enumerate(lowercase__ ) if idx not in irrelevant_indices]
     SCREAMING_SNAKE_CASE__				:				str			=		[coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
     SCREAMING_SNAKE_CASE__				:				List[str]			=		[coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
     SCREAMING_SNAKE_CASE__				:				List[str]			=		[coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     for x, y, w, h in zip(lowercase__     ,  lowercase__     ,  lowercase__     ,  lowercase__ ):
          SCREAMING_SNAKE_CASE__				:				List[str]			=		[x, y, x + w, y + h]
          actual_boxes.append(lowercase__ )

     # finally, normalize the bounding boxes
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(lowercase__     ,  lowercase__     ,  lowercase__ ) )

     assert len(lowercase__ ) == len(lowercase__ ), "Not as many words as there are bounding boxes"

     return words, normalized_boxes







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




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




       """simple docstring"""
       super().__init__(**a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		size if size is not None else {'height': 224, 'width': 224}
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		get_size_dict(a_				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		do_resize
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		resample
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		do_rescale
       SCREAMING_SNAKE_CASE__				:				str			=		rescale_value
       SCREAMING_SNAKE_CASE__				:				str			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				List[str]			=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_std if image_std is not None else IMAGENET_STANDARD_STD
       SCREAMING_SNAKE_CASE__				:				Dict			=		apply_ocr
       SCREAMING_SNAKE_CASE__				:				Any			=		ocr_lang
       SCREAMING_SNAKE_CASE__				:				Dict			=		tesseract_config




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		get_size_dict(a_				)
       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()}'''				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		(size['height'], size['width'])
       return resize(a_     , size=a_     , resample=a_     , data_format=a_     , **a_				)




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




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




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




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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__				:				Dict			=		size if size is not None else self.size
       SCREAMING_SNAKE_CASE__				:				str			=		get_size_dict(a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__				:				int			=		image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_std if image_std is not None else self.image_std
       SCREAMING_SNAKE_CASE__				:				Any			=		apply_ocr if apply_ocr is not None else self.apply_ocr
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ocr_lang if ocr_lang is not None else self.ocr_lang
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tesseract_config if tesseract_config is not None else self.tesseract_config

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		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:
            raise ValueError('Size 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('If do_normalize is True, image_mean and image_std must be specified.'				)

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__				:				Any			=		[to_numpy_array(a_				) for image in images]

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
            requires_backends(self     , 'pytesseract'				)
            SCREAMING_SNAKE_CASE__				:				str			=		[]
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[]
            for image in images:
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Tuple			=		apply_tesseract(a_     , a_     , a_				)
                 words_batch.append(a_				)
                 boxes_batch.append(a_				)

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

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

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

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

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

       if apply_ocr:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		words_batch
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		boxes_batch
       return data




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>")



