
'''simple docstring'''






import requests

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





def  UpperCAmelCase				(	UpperCAmelCase__				:				str):
				lowerCamelCase       :      int          =			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>')




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


A			:						Optional[Any]        =  logging.get_logger(__name__)

A			:						str        =  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A			:						Any        =  {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
A			:						Dict        =  {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
A			:						Optional[int]        =  {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

A			:						int        =  {
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
A			:						Optional[Any]        =  {
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
A			:						Any        =  {
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


A			:						Union[str, Any]        =  {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A			:						Tuple        =  {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A			:						Any        =  {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					A__     =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
					A__     =					DPRContextEncoderTokenizer






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					A__     =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
					A__     =					DPRQuestionEncoderTokenizer






A			:						Any        =  collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

A			:						Optional[Any]        =  collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A			:						int        =  r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '



@add_start_docstrings(UpperCAmelCase__				)
class 				A       :
					'''simple docstring'''






					def __call__(self       :		str ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None ,   _UpperCAmelCase       :		Optional[str] = None ,   _UpperCAmelCase       :		Union[bool, str] = False ,   _UpperCAmelCase       :		Union[bool, str] = False ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		Optional[Union[str, TensorType]] = None ,   _UpperCAmelCase       :		Optional[bool] = None ,   **_UpperCAmelCase       :		Any ,   )		->  BatchEncoding:




							"""simple docstring"""


							if titles is None and texts is None:
									return super().__call__(
									    _UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							elif titles is None or texts is None:
									lowercase__	     =						titles if texts is None else texts
									return super().__call__(
									    _UpperCAmelCase ,   _UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						titles if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else [titles]
							lowercase__	     =						texts if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else [texts]
							lowercase__	     =						len(_UpperCAmelCase						)
							lowercase__	     =						questions if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else [questions] * n_passages
							assert len(_UpperCAmelCase						) == len(
							    _UpperCAmelCase						), f'''There should be as many titles than texts but got {len(_UpperCAmelCase						)} titles and {len(_UpperCAmelCase						)} texts.'''
							lowercase__	     =						super().__call__(_UpperCAmelCase ,   _UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase						)["""input_ids"""]
							lowercase__	     =						super().__call__(_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase						)["""input_ids"""]
							lowercase__	     =						{
							    """input_ids""": [
							        (encoded_question_and_title + encoded_text)[:max_length]
							        if max_length is not None and truncation
							        else encoded_question_and_title + encoded_text
							        for encoded_question_and_title, encoded_text in zip(_UpperCAmelCase ,   _UpperCAmelCase						)
							    ]
							}
							if return_attention_mask is not False:
									lowercase__	     =						[]
									for input_ids in encoded_inputs["input_ids"]:
											attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
									lowercase__	     =						attention_mask
							return self.pad(_UpperCAmelCase ,   padding=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		BatchEncoding ,   _UpperCAmelCase       :		DPRReaderOutput ,   _UpperCAmelCase       :		int = 16 ,   _UpperCAmelCase       :		int = 64 ,   _UpperCAmelCase       :		int = 4 ,   )		->  List[DPRSpanPrediction]:




							"""simple docstring"""


							lowercase__	     =						reader_input["""input_ids"""]
							lowercase__     ,      lowercase__     ,      lowercase__	     =						reader_output[:3]
							lowercase__	     =						len(_UpperCAmelCase						)
							lowercase__	     =						sorted(range(_UpperCAmelCase						) ,   reverse=_UpperCAmelCase ,   key=relevance_logits.__getitem__						)
							lowercase__	     =						[]
							for doc_id in sorted_docs:
									lowercase__	     =						list(input_ids[doc_id]						)
									# assuming question & title information is at the beginning of the sequence
									lowercase__	     =						sequence_ids.index(self.sep_token_id ,   2						) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
											lowercase__	     =						sequence_ids.index(self.pad_token_id						)
									else:
											lowercase__	     =						len(_UpperCAmelCase						)

									lowercase__	     =						self._get_best_spans(
									    start_logits=start_logits[doc_id][passage_offset:sequence_len] ,   end_logits=end_logits[doc_id][passage_offset:sequence_len] ,   max_answer_length=_UpperCAmelCase ,   top_spans=_UpperCAmelCase ,   )
									for start_index, end_index in best_spans:
											start_index += passage_offset
											end_index += passage_offset
											nbest_spans_predictions.append(
											    DPRSpanPrediction(
											        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] ,   relevance_score=relevance_logits[doc_id] ,   doc_id=_UpperCAmelCase ,   start_index=_UpperCAmelCase ,   end_index=_UpperCAmelCase ,   text=self.decode(sequence_ids[start_index : end_index + 1]						) ,   )						)
									if len(_UpperCAmelCase						) >= num_spans:
											break
							return nbest_spans_predictions[:num_spans]


					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		int ,   )		->  List[DPRSpanPrediction]:




							"""simple docstring"""


							lowercase__	     =						[]
							for start_index, start_score in enumerate(_UpperCAmelCase						):
									for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]						):
											scores.append(((start_index, start_index + answer_length), start_score + end_score)						)
							lowercase__	     =						sorted(_UpperCAmelCase ,   key=lambda _UpperCAmelCase						: x[1] ,   reverse=_UpperCAmelCase						)
							lowercase__	     =						[]
							for (start_index, end_index), score in scores:
									assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
									lowercase__	     =						end_index - start_index + 1
									assert length <= max_answer_length, f'''Span is too long: {length} > {max_answer_length}'''
									if any(
									    start_index <= prev_start_index <= prev_end_index <= end_index
									    or prev_start_index <= start_index <= end_index <= prev_end_index
									    for (prev_start_index, prev_end_index) in chosen_span_intervals						):
											continue
									chosen_span_intervals.append((start_index, end_index)						)

									if len(_UpperCAmelCase						) == top_spans:
											break
							return chosen_span_intervals






@add_end_docstrings(UpperCAmelCase__				)
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					READER_PRETRAINED_VOCAB_FILES_MAP
					A__     =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					READER_PRETRAINED_INIT_CONFIGURATION
					A__     =					['''input_ids''', '''attention_mask''']
					A__     =					DPRReaderTokenizer

