


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			:			Optional[int]												=					logging.get_logger(__name__)

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

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


a			:			Optional[Any]												=					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
a			:			List[Any]												=					{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
a			:			Dict												=					{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__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_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__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			:			int												=					collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

a			:			str												=					collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


a			:			Tuple												=					R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''
@add_start_docstrings(lowerCAmelCase__					)
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __call__(					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      BatchEncoding:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = 1_6			,							snake_case_ = 6_4			,							snake_case_ = 4			,							) ->      List[DPRSpanPrediction]:







								'''simple docstring'''

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

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							) ->      List[DPRSpanPrediction]:







								'''simple docstring'''

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




@add_end_docstrings(lowerCAmelCase__					)
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  lowerCAmelCase__					):



					'''simple docstring'''







					__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






# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

a			:			Dict												=					'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
	raise ImportWarning(
	    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
	)

if version.parse(pyarrow.__version__).major < 8:
	raise ImportWarning(
	    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
	    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
	)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

a			:			Optional[int]												=					concatenate_datasets
a			:			List[Any]												=					DownloadConfig
a			:			List[Any]												=					DownloadManager
a			:			str												=					DownloadMode
a			:			int												=					DownloadConfig
a			:			List[str]												=					DownloadMode
a			:			Optional[int]												=					DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


