




"""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


_lowerCamelCase  :			Any        =       logging.get_logger(__name__)

_lowerCamelCase  :			Optional[int]        =       {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase  :			str        =       {
    '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'
        ),
    },
}
_lowerCamelCase  :			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'
        ),
    },
}
_lowerCamelCase  :			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'
        ),
    },
}

_lowerCamelCase  :			Union[str, Any]        =       {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
_lowerCamelCase  :			Optional[int]        =       {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
_lowerCamelCase  :			Dict        =       {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


_lowerCamelCase  :			Tuple        =       {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
_lowerCamelCase  :			Dict        =       {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
_lowerCamelCase  :			Dict        =       {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    int         =   VOCAB_FILES_NAMES
				__lowerCAmelCase    :    Dict         =   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase    :    Optional[Any]         =   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase    :    str         =   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[int]         =   VOCAB_FILES_NAMES
				__lowerCAmelCase    :    Optional[int]         =   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase    :    Any         =   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase    :    Optional[int]         =   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase  :			Any        =       collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

_lowerCamelCase  :			Tuple        =       collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


_lowerCamelCase  :			Optional[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    ```\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        lowercase      :







				def __call__(							self   :					str      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Optional[str] = None      ,     _lowerCamelCase   :					Optional[str] = None      ,     _lowerCamelCase   :					Union[bool, str] = False      ,     _lowerCamelCase   :					Union[bool, str] = False      ,     _lowerCamelCase   :					Optional[int] = None      ,     _lowerCamelCase   :					Optional[Union[str, TensorType]] = None      ,     _lowerCamelCase   :					Optional[bool] = None      ,     **_lowerCamelCase   :					Any      ,     ):



					"""simple docstring"""

					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:
						A_  :				str	   =		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      ,     )
					A_  :				List[Any]	   =		titles if not isinstance(_lowerCamelCase      ,     _lowerCamelCase       ) else [titles]
					A_  :				List[Any]	   =		texts if not isinstance(_lowerCamelCase      ,     _lowerCamelCase       ) else [texts]
					A_  :				List[Any]	   =		len(_lowerCamelCase       )
					A_  :				int	   =		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."""       )
					A_  :				Tuple	   =		super().__call__(_lowerCamelCase      ,     _lowerCamelCase      ,     padding=_lowerCamelCase      ,     truncation=_lowerCamelCase       )['''input_ids''']
					A_  :				int	   =		super().__call__(_lowerCamelCase      ,     add_special_tokens=_lowerCamelCase      ,     padding=_lowerCamelCase      ,     truncation=_lowerCamelCase       )['''input_ids''']
					A_  :				Dict	   =		{
					    '''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:
						A_  :				Union[str, Any]	   =		[]
						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_  :				Optional[int]	   =		attention_mask
					return self.pad(_lowerCamelCase      ,     padding=_lowerCamelCase      ,     max_length=_lowerCamelCase      ,     return_tensors=_lowerCamelCase       )







				def 					a_  (							self   :					Any      ,     _lowerCamelCase   :					BatchEncoding      ,     _lowerCamelCase   :					DPRReaderOutput      ,     _lowerCamelCase   :					int = 16      ,     _lowerCamelCase   :					int = 64      ,     _lowerCamelCase   :					int = 4      ,     ):



					"""simple docstring"""

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

						A_  :				List[str]	   =		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 					a_  (							self   :					Any      ,     _lowerCamelCase   :					List[int]      ,     _lowerCamelCase   :					List[int]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int      ,     ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		[]
					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)       )
					A_  :				Optional[Any]	   =		sorted(_lowerCamelCase      ,     key=lambda _lowerCamelCase       : x[1]      ,     reverse=_lowerCamelCase       )
					A_  :				str	   =		[]
					for (start_index, end_index), score in scores:
						if start_index > end_index:
							raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""       )
						A_  :				List[str]	   =		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        lowercase      (    __UpperCAmelCase     ,	__UpperCAmelCase):
				__lowerCAmelCase    :    Any         =   VOCAB_FILES_NAMES
				__lowerCAmelCase    :    Optional[int]         =   READER_PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase    :    Tuple         =   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase    :    List[Any]         =   READER_PRETRAINED_INIT_CONFIGURATION
				__lowerCAmelCase    :    Tuple         =   ["""input_ids""", """attention_mask"""]











"""simple docstring"""

from typing import Any

import numpy as np

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	return np.array_equal(_UpperCAmelCase  ,      matrix.conjugate().T	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Tuple	   =		v.conjugate().T
	A_  :				Tuple	   =		v_star.dot(_UpperCAmelCase	)
	assert isinstance(_UpperCAmelCase  ,      np.ndarray	)
	return (v_star_dot.dot(_UpperCAmelCase	)) / (v_star.dot(_UpperCAmelCase	))

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]	)
	A_  :				str	   =		np.array([[1], [2], [3]]	)
	assert is_hermitian(_UpperCAmelCase	), f"""{a} is not hermitian."""
	print(rayleigh_quotient(_UpperCAmelCase  ,      _UpperCAmelCase	)	)

	A_  :				int	   =		np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]	)
	assert is_hermitian(_UpperCAmelCase	), f"""{a} is not hermitian."""
	assert rayleigh_quotient(_UpperCAmelCase  ,      _UpperCAmelCase	) == float(3	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		tests()





