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


_lowerCAmelCase       =		logging.get_logger(__name__)

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

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


_lowerCAmelCase       =		{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCAmelCase       =		{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCAmelCase       =		{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class     lowerCAmelCase_     (       __lowercase ):
							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     lowerCAmelCase_     (       __lowercase ):
							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




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

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


_lowerCAmelCase       =		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(__lowercase )
class     lowerCAmelCase_     :







							def __call__(   self      :     Union[str, Any] ,			_A      :     List[Any] ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			_A      :     Union[bool, str] = False ,			_A      :     Union[bool, str] = False ,			_A      :     Optional[int] = None ,			_A      :     Optional[Union[str, TensorType]] = None ,			_A      :     Optional[bool] = None ,			**_A      :     str ,			):
												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:
																	_UpperCamelCase			     =     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 ,			)
												_UpperCamelCase			     =     titles if not isinstance(_A ,			_A		) else [titles]
												_UpperCamelCase			     =     texts if not isinstance(_A ,			_A		) else [texts]
												_UpperCamelCase			     =     len(_A		)
												_UpperCamelCase			     =     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."""
												_UpperCamelCase			     =     super().__call__(_A ,			_A ,			padding=_A ,			truncation=_A		)['''input_ids''']
												_UpperCamelCase			     =     super().__call__(_A ,			add_special_tokens=_A ,			padding=_A ,			truncation=_A		)['''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(_A ,			_A		)
												    ]
												}
												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(_A ,			padding=_A ,			max_length=_A ,			return_tensors=_A		)







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     BatchEncoding ,			_A      :     DPRReaderOutput ,			_A      :     int = 16 ,			_A      :     int = 64 ,			_A      :     int = 4 ,			):
												_UpperCamelCase			     =     reader_input['''input_ids''']
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     reader_output[:3]
												_UpperCamelCase			     =     len(_A		)
												_UpperCamelCase			     =     sorted(range(_A		) ,			reverse=_A ,			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(_A		)

																	_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=_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  UpperCamelCase_						(   self      :     Any ,			_A      :     List[int] ,			_A      :     List[int] ,			_A      :     int ,			_A      :     int ,			):
												_UpperCamelCase			     =     []
												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)		)
												_UpperCamelCase			     =     sorted(_A ,			key=lambda _A		: x[1] ,			reverse=_A		)
												_UpperCamelCase			     =     []
												for (start_index, end_index), score in scores:
																	assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
																	_UpperCamelCase			     =     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(__lowercase )
class     lowerCAmelCase_     (       __lowercase,  __lowercase ):
							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

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return math.sqrt(sum(pow(a - b  ,     2						) for a, b in zip(__snake_case  ,     __snake_case						)						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):

					if dataset.ndim != value_array.ndim:
										_UpperCamelCase			     =     (
										    '''Wrong input data\'s dimensions... '''
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(__snake_case						)

					try:
										if dataset.shape[1] != value_array.shape[1]:
															_UpperCamelCase			     =     (
															    '''Wrong input data\'s shape... '''
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(__snake_case						)
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError('''Wrong shape'''						)

					if dataset.dtype != value_array.dtype:
										_UpperCamelCase			     =     (
										    '''Input data have different datatype... '''
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(__snake_case						)

					_UpperCamelCase			     =     []

					for value in value_array:
										_UpperCamelCase			     =     euclidean(__snake_case  ,     dataset[0]						)
										_UpperCamelCase			     =     dataset[0].tolist()

										for dataset_value in dataset[1:]:
															_UpperCamelCase			     =     euclidean(__snake_case  ,     __snake_case						)

															if dist > temp_dist:
																				_UpperCamelCase			     =     temp_dist
																				_UpperCamelCase			     =     dataset_value.tolist()

										answer.append([vector, dist]						)

					return answer
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return np.dot(__snake_case  ,     __snake_case						) / (norm(__snake_case						) * norm(__snake_case						))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
