


"""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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


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": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
a_       							=					{
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
a_       							=					{
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


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 snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			VOCAB_FILES_NAMES
		__UpperCamelCase           =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase           =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase           =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
		__UpperCamelCase           =			DPRContextEncoderTokenizer





class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			VOCAB_FILES_NAMES
		__UpperCamelCase           =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase           =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase           =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
		__UpperCamelCase           =			DPRQuestionEncoderTokenizer





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    [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 snake_case      :







		def __call__(					self      :       Union[str, Any]    ,   a__      :       Optional[Any]    ,   a__      :       Optional[int] = None    ,   a__      :       Any = None    ,   a__      :       Optional[Any] = False    ,   a__      :       Dict = False    ,   a__      :       str = None    ,   a__      :       List[Any] = None    ,   a__      :       Optional[Any] = None    ,   **a__      :       Any    ,   )       ->						BatchEncoding:





						'''simple docstring'''







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







		def  a_ (					self      :       Dict    ,   a__      :       Optional[Any]    ,   a__      :       Union[str, Any]    ,   a__      :       List[Any] = 16    ,   a__      :       List[str] = 64    ,   a__      :       Optional[int] = 4    ,   )       ->						List[DPRSpanPrediction]:





						'''simple docstring'''







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

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







		def  a_ (					self      :       Dict    ,   a__      :       List[str]    ,   a__      :       Dict    ,   a__      :       Dict    ,   a__      :       Optional[Any]    ,   )       ->						List[DPRSpanPrediction]:





						'''simple docstring'''







						_A					  =			[]
						for start_index, start_score in enumerate(__a		):
										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)		)
						_A					  =			sorted(__a    ,   key=lambda a__		: x[1]    ,   reverse=__a		)
						_A					  =			[]
						for (start_index, end_index), score in scores:
										assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
										_A					  =			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(__a		) == top_spans:
														break
						return chosen_span_intervals





@add_end_docstrings(_UpperCamelCase)
class snake_case      (  _UpperCamelCase     ,				_UpperCamelCase):
		__UpperCamelCase           =			VOCAB_FILES_NAMES
		__UpperCamelCase           =			READER_PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase           =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase           =			READER_PRETRAINED_INIT_CONFIGURATION
		__UpperCamelCase           =			['input_ids', 'attention_mask']
		__UpperCamelCase           =			DPRReaderTokenizer



"""simple docstring"""


def      a__ (				__lowercase     ,			__lowercase   )	->	int:
				while a != 0:
								_A  ,		_A					  =			b % a, a
				return b






def      a__ (				__lowercase     ,			__lowercase   )	->	int:
				if gcd(__lowercase     ,			__lowercase   ) != 1:
								_A					  =			f"""mod inverse of {a!r} and {m!r} does not exist"""
								raise ValueError(__lowercase   )
				_A  ,		_A  ,		_A					  =			1, 0, a
				_A  ,		_A  ,		_A					  =			0, 1, m
				while va != 0:
								_A					  =			ua // va
								_A  ,		_A  ,		_A  ,		_A  ,		_A  ,		_A					  =			(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
				return ua % m