

"""simple docstring"""






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 import BertTokenizer


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

snake_case_   :   Tuple        =       {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case_   :   Tuple        =       {
    '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'
        ),
    },
}
snake_case_   :   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'
        ),
    },
}
snake_case_   :   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'
        ),
    },
}

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


snake_case_   :   Optional[int]        =       {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
snake_case_   :   Any        =       {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
snake_case_   :   Any        =       {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}







class       snake_case__  ( a__     ):
			SCREAMING_SNAKE_CASE__     				=						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE__     				=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE__     				=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE__     				=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class       snake_case__  ( a__     ):
			SCREAMING_SNAKE_CASE__     				=						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE__     				=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE__     				=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE__     				=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

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

snake_case_   :   Union[str, Any]        =       collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


snake_case_   :   str        =       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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(a__     )
class       snake_case__  :







			def __call__(			self			: Union[str, Any]			,  lowercase			: List[str]			,  lowercase			: Optional[Any] = None			,  lowercase			: Tuple = None			,  lowercase			: List[str] = False			,  lowercase			: List[Any] = False			,  lowercase			: List[str] = None			,  lowercase			: List[Any] = None			,  lowercase			: Optional[Any] = None			,  **lowercase			: Tuple			,  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: int			,  lowercase			: Optional[Any]			,  lowercase			: Union[str, Any] = 16			,  lowercase			: Optional[int] = 64			,  lowercase			: Optional[int] = 4			,  ):





								'''simple docstring'''



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

													UpperCAmelCase			:   Union[str, Any]      =				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=lowercase__			,  top_spans=lowercase__			,  )
													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=lowercase__			,  start_index=lowercase__			,  end_index=lowercase__			,  text=self.decode(sequence_ids[start_index : end_index + 1]  )			,  )  )
													if len(lowercase__  ) >= num_spans:
																		break
								return nbest_spans_predictions[:num_spans]







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: Union[str, Any]			,  lowercase			: int			,  lowercase			: Dict			,  lowercase			: Any			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				[]
								for start_index, start_score in enumerate(lowercase__  ):
													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)  )
								UpperCAmelCase			:   str      =				sorted(lowercase__			,  key=lambda lowercase  : x[1]			,  reverse=lowercase__  )
								UpperCAmelCase			:   List[str]      =				[]
								for (start_index, end_index), score in scores:
													if start_index > end_index:
																		raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""  )
													UpperCAmelCase			:   Union[str, Any]      =				end_index - start_index + 1
													if length > max_answer_length:
																		raise ValueError(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(lowercase__  ) == top_spans:
																		break
								return chosen_span_intervals







@add_end_docstrings(a__     )
class       snake_case__  ( a__					,  a__     ):
			SCREAMING_SNAKE_CASE__     				=						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE__     				=						READER_PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE__     				=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE__     				=						READER_PRETRAINED_INIT_CONFIGURATION
			SCREAMING_SNAKE_CASE__     				=						["""input_ids""", """attention_mask"""]





"""simple docstring"""






def   lowercase_	(    _lowercase       :		list							,      _lowercase       :		int							,      _lowercase       :		int = 0							,      _lowercase       :		int = 0	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				right or len(_lowercase	) - 1
					if left > right:
										return -1
					elif list_data[left] == key:
										return left
					elif list_data[right] == key:
										return right
					else:
										return search(_lowercase							,      _lowercase							,      left + 1							,      right - 1	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


