

'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class     _SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_      ):
 __SCREAMING_SNAKE_CASE	:int     			=	['image_processor', 'tokenizer']
 __SCREAMING_SNAKE_CASE	:str     			=	'LayoutLMv3ImageProcessor'
 __SCREAMING_SNAKE_CASE	:Optional[int]     			=	('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')



 def __init__(       self						:       str					, a__						:       int=None					, a__						:       List[Any]=None					, **a__						:       Any     ):
      __magic_name__		  =							None
      if "feature_extractor" in kwargs:
           warnings.warn(
               '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
               ''' instead.'''					, a__					, )
           __magic_name__		  =							kwargs.pop('''feature_extractor'''     )

      __magic_name__		  =							image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError('''You need to specify an `image_processor`.'''     )
      if tokenizer is None:
           raise ValueError('''You need to specify a `tokenizer`.'''     )

      super().__init__(a__					, a__     )



 def __call__(       self						:       int					, a__						:       Union[str, Any]					, a__						:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					, a__						:       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					, a__						:       Union[List[List[int]], List[List[List[int]]]] = None					, a__						:       Optional[Union[List[int], List[List[int]]]] = None					, a__						:       bool = True					, a__						:       Union[bool, str, PaddingStrategy] = False					, a__						:       Union[bool, str, TruncationStrategy] = None					, a__						:       Optional[int] = None					, a__						:       int = 0					, a__						:       Optional[int] = None					, a__						:       Optional[bool] = None					, a__						:       Optional[bool] = None					, a__						:       bool = False					, a__						:       bool = False					, a__						:       bool = False					, a__						:       bool = False					, a__						:       bool = True					, a__						:       Optional[Union[str, TensorType]] = None					, **a__						:       Any					, ):


      if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''     )

      if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''     )

      # first, apply the image processor
      __magic_name__		  =							self.image_processor(images=a__					, return_tensors=a__     )

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(a__					, a__     ):
                __magic_name__		  =							[text]  # add batch dimension (as the image processor always adds a batch dimension)
           __magic_name__		  =							features['words']

      __magic_name__		  =							self.tokenizer(
          text=text if text is not None else features['''words''']					, text_pair=text_pair if text_pair is not None else None					, boxes=boxes if boxes is not None else features['''boxes''']					, word_labels=a__					, add_special_tokens=a__					, padding=a__					, truncation=a__					, max_length=a__					, stride=a__					, pad_to_multiple_of=a__					, return_token_type_ids=a__					, return_attention_mask=a__					, return_overflowing_tokens=a__					, return_special_tokens_mask=a__					, return_offsets_mapping=a__					, return_length=a__					, verbose=a__					, return_tensors=a__					, **a__					, )

      # add pixel values
      __magic_name__		  =							features.pop('''pixel_values'''     )
      if return_overflowing_tokens is True:
           __magic_name__		  =							self.get_overflowing_images(a__					, encoded_inputs['''overflow_to_sample_mapping''']     )
      __magic_name__		  =							images

      return encoded_inputs



 def 					snake_case__    (       self						:       Tuple					, a__						:       List[str]					, a__						:       List[str]     ):
      __magic_name__		  =							[]
      for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]     )

      if len(a__     ) != len(a__     ):
           raise ValueError(
               '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
               F''' {len(a__     )} and {len(a__     )}'''     )

      return images_with_overflow



 def 					snake_case__    (       self						:       str					, *a__						:       List[Any]					, **a__						:       Optional[Any]     ):
      return self.tokenizer.batch_decode(*a__					, **a__     )



 def 					snake_case__    (       self						:       Union[str, Any]					, *a__						:       List[Any]					, **a__						:       Tuple     ):
      return self.tokenizer.decode(*a__					, **a__     )



 @property
 def 					snake_case__    (       self						:       int     ):
      return ["input_ids", "bbox", "attention_mask", "pixel_values"]



 @property
 def 					snake_case__    (       self						:       Dict     ):
      warnings.warn(
          '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					, a__					, )
      return self.image_processor_class



 @property
 def 					snake_case__    (       self						:       Tuple     ):
      warnings.warn(
          '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					, a__					, )
      return self.image_processor











import requests

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

def        __a			(   __lowerCAmelCase   )     ->     None:
					# fetching a list of articles in json format
					SCREAMING_SNAKE_CASE							:     List[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>""")