




'''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


a													=			logging.get_logger(__name__)

a													=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a													=			{
    "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													=			{
    "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													=			{
    "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													=			{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
a													=			{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
a													=			{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


a													=			{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
a													=			{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
a													=			{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}




class       _A				(		__UpperCAmelCase     ):
		__a					    =       VOCAB_FILES_NAMES
		__a					    =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		__a					    =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a					    =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class       _A				(		__UpperCAmelCase     ):
		__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													=			collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

a													=			collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


a													=			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(__UpperCAmelCase     )
class       _A				:






		def __call__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = False						,		_SCREAMING_SNAKE_CASE = False						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE						,		):







								if titles is None and texts is None:
														return super().__call__(
														    lowerCAmelCase_						,		padding=lowerCAmelCase_						,		truncation=lowerCAmelCase_						,		max_length=lowerCAmelCase_						,		return_tensors=lowerCAmelCase_						,		return_attention_mask=lowerCAmelCase_						,		**lowerCAmelCase_						,		)
								elif titles is None or texts is None:
														_UpperCAmelCase      =			titles if texts is None else texts
														return super().__call__(
														    lowerCAmelCase_						,		lowerCAmelCase_						,		padding=lowerCAmelCase_						,		truncation=lowerCAmelCase_						,		max_length=lowerCAmelCase_						,		return_tensors=lowerCAmelCase_						,		return_attention_mask=lowerCAmelCase_						,		**lowerCAmelCase_						,		)
								_UpperCAmelCase      =			titles if not isinstance(lowerCAmelCase_						,		lowerCAmelCase_			) else [titles]
								_UpperCAmelCase      =			texts if not isinstance(lowerCAmelCase_						,		lowerCAmelCase_			) else [texts]
								_UpperCAmelCase      =			len(lowerCAmelCase_			)
								_UpperCAmelCase      =			questions if not isinstance(lowerCAmelCase_						,		lowerCAmelCase_			) else [questions] * n_passages
								if len(lowerCAmelCase_			) != len(lowerCAmelCase_			):
														raise ValueError(
														    F"There should be as many titles than texts but got {len(lowerCAmelCase_			)} titles and {len(lowerCAmelCase_			)} texts."			)
								_UpperCAmelCase      =			super().__call__(lowerCAmelCase_						,		lowerCAmelCase_						,		padding=lowerCAmelCase_						,		truncation=lowerCAmelCase_			)["""input_ids"""]
								_UpperCAmelCase      =			super().__call__(lowerCAmelCase_						,		add_special_tokens=lowerCAmelCase_						,		padding=lowerCAmelCase_						,		truncation=lowerCAmelCase_			)["""input_ids"""]
								_UpperCAmelCase      =			{
								    """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(lowerCAmelCase_						,		lowerCAmelCase_			)
								    ]
								}
								if return_attention_mask is not False:
														_UpperCAmelCase      =			[]
														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      =			attention_mask
								return self.pad(lowerCAmelCase_						,		padding=lowerCAmelCase_						,		max_length=lowerCAmelCase_						,		return_tensors=lowerCAmelCase_			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = 16						,		_SCREAMING_SNAKE_CASE = 64						,		_SCREAMING_SNAKE_CASE = 4						,		):
								_UpperCAmelCase      =			reader_input["""input_ids"""]
								_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			reader_output[:3]
								_UpperCAmelCase      =			len(lowerCAmelCase_			)
								_UpperCAmelCase      =			sorted(range(lowerCAmelCase_			)						,		reverse=lowerCAmelCase_						,		key=relevance_logits.__getitem__			)
								_UpperCAmelCase      =			[]
								for doc_id in sorted_docs:
														_UpperCAmelCase      =			list(input_ids[doc_id]			)
														# assuming question & title information is at the beginning of the sequence
														_UpperCAmelCase      =			sequence_ids.index(self.sep_token_id						,		2			) + 1  # second sep id
														if sequence_ids[-1] == self.pad_token_id:
																				_UpperCAmelCase      =			sequence_ids.index(self.pad_token_id			)
														else:
																				_UpperCAmelCase      =			len(lowerCAmelCase_			)

														_UpperCAmelCase      =			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=lowerCAmelCase_						,		top_spans=lowerCAmelCase_						,		)
														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=lowerCAmelCase_						,		start_index=lowerCAmelCase_						,		end_index=lowerCAmelCase_						,		text=self.decode(sequence_ids[start_index : end_index + 1]			)						,		)			)
														if len(lowerCAmelCase_			) >= num_spans:
																				break
								return nbest_spans_predictions[:num_spans]






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			[]
								for start_index, start_score in enumerate(lowerCAmelCase_			):
														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      =			sorted(lowerCAmelCase_						,		key=lambda _SCREAMING_SNAKE_CASE			: x[1]						,		reverse=lowerCAmelCase_			)
								_UpperCAmelCase      =			[]
								for (start_index, end_index), score in scores:
														if start_index > end_index:
																				raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"			)
														_UpperCAmelCase      =			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(lowerCAmelCase_			) == top_spans:
																				break
								return chosen_span_intervals




@add_end_docstrings(__UpperCAmelCase     )
class       _A				(		__UpperCAmelCase       ,     __UpperCAmelCase     ):
		__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"""]





# flake8: noqa
# Lint as: python3
a													=			[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental