

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


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

_UpperCAmelCase	:     Optional[Any]		       = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase	:     Optional[Any]		       = {
    """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"""
        ),
    },
}
_UpperCAmelCase	:     Optional[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"""
        ),
    },
}
_UpperCAmelCase	:     Any		       = {
    """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"""
        ),
    },
}

_UpperCAmelCase	:     str		       = {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_UpperCAmelCase	:     int		       = {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_UpperCAmelCase	:     Any		       = {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_UpperCAmelCase	:     List[str]		       = {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_UpperCAmelCase	:     Tuple		       = {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_UpperCAmelCase	:     str		       = {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				List[str]     =    VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE      :				Any     =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE      :				Dict     =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE      :				Dict     =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE      :				Dict     =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE      :				str     =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



_UpperCAmelCase	:     Optional[Any]		       = collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_UpperCAmelCase	:     str		       = collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_UpperCAmelCase	:     Optional[int]		       = 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)

    Returns:
        `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(lowercase_    )
class 				lowercase       :




				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   ,     ):
											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:
																		snake_case_           =					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   ,     )
											snake_case_           =					titles if not isinstance(snake_case   ,     snake_case ) else [titles]
											snake_case_           =					texts if not isinstance(snake_case   ,     snake_case ) else [texts]
											snake_case_           =					len(snake_case )
											snake_case_           =					questions if not isinstance(snake_case   ,     snake_case ) else [questions] * n_passages
											if len(snake_case ) != len(snake_case ):
																		raise ValueError(
																		    F'''There should be as many titles than texts but got {len(snake_case )} titles and {len(snake_case )} texts.''' )
											snake_case_           =					super().__call__(snake_case   ,     snake_case   ,     padding=snake_case   ,     truncation=snake_case )['input_ids']
											snake_case_           =					super().__call__(snake_case   ,     add_special_tokens=snake_case   ,     padding=snake_case   ,     truncation=snake_case )['input_ids']
											snake_case_           =					{
											    '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:
																		snake_case_           =					[]
																		for input_ids in encoded_inputs["input_ids"]:
																									attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
																		snake_case_           =					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 = 16   ,     snake_case = 64   ,     snake_case = 4   ,     ):
											snake_case_           =					reader_input['input_ids']
											snake_case_       ,					snake_case_       ,					snake_case_           =					reader_output[:3]
											snake_case_           =					len(snake_case )
											snake_case_           =					sorted(range(snake_case )   ,     reverse=snake_case   ,     key=relevance_logits.__getitem__ )
											snake_case_           =					[]
											for doc_id in sorted_docs:
																		snake_case_           =					list(input_ids[doc_id] )
																		# assuming question & title information is at the beginning of the sequence
																		snake_case_           =					sequence_ids.index(self.sep_token_id   ,     2 ) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																									snake_case_           =					sequence_ids.index(self.pad_token_id )
																		else:
																									snake_case_           =					len(snake_case )

																		snake_case_           =					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   ,     ):
											snake_case_           =					[]
											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) )
											snake_case_           =					sorted(snake_case   ,     key=lambda snake_case : x[1]   ,     reverse=snake_case )
											snake_case_           =					[]
											for (start_index, end_index), score in scores:
																		if start_index > end_index:
																									raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]''' )
																		snake_case_           =					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(snake_case ) == top_spans:
																									break
											return chosen_span_intervals







@add_end_docstrings(lowercase_    )
class 				lowercase       ( lowercase_			,						lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE      :				List[str]     =    READER_PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE      :				str     =    READER_PRETRAINED_INIT_CONFIGURATION
				__SCREAMING_SNAKE_CASE      :				str     =    ['''input_ids''', '''attention_mask''']







import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					np.inf

							def set_batch_size(UpperCamelCase__   ) -> None:
														nonlocal batch_size
														if isinstance(UpperCamelCase__     ,							UpperCamelCase__   ):
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS   )
														elif isinstance(UpperCamelCase__     ,							UpperCamelCase__   ):
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS   )
														elif isinstance(UpperCamelCase__     ,							UpperCamelCase__   ) and feature.dtype == "binary":
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS   )

							_visit(UpperCamelCase__     ,							UpperCamelCase__   )

							return None if batch_size is np.inf else batch_size







class 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = False   ,     snake_case = False   ,     snake_case = None   ,     **snake_case   ,     ):
											super().__init__(
											    snake_case   ,     split=snake_case   ,     features=snake_case   ,     cache_dir=snake_case   ,     keep_in_memory=snake_case   ,     streaming=snake_case   ,     num_proc=snake_case   ,     **snake_case   ,     )
											snake_case_           =					path_or_paths if isinstance(snake_case   ,     snake_case ) else {self.split: path_or_paths}
											snake_case_           =					_PACKAGED_DATASETS_MODULES['parquet'][1]
											snake_case_           =					Parquet(
											    cache_dir=snake_case   ,     data_files=snake_case   ,     features=snake_case   ,     hash=snake_case   ,     **snake_case   ,     )




				def 					a							(      self ):
											# Build iterable dataset
											if self.streaming:
																		snake_case_           =					self.builder.as_streaming_dataset(split=self.split )
											# Build regular (map-style) dataset
											else:
																		snake_case_           =					None
																		snake_case_           =					None
																		snake_case_           =					None
																		snake_case_           =					None

																		self.builder.download_and_prepare(
																		    download_config=snake_case   ,     download_mode=snake_case   ,     verification_mode=snake_case   ,     base_path=snake_case   ,     num_proc=self.num_proc   ,     )
																		snake_case_           =					self.builder.as_dataset(
																		    split=self.split   ,     verification_mode=snake_case   ,     in_memory=self.keep_in_memory )
											return dataset







class 				lowercase       :




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											snake_case_           =					dataset
											snake_case_           =					path_or_buf
											snake_case_           =					batch_size or get_writer_batch_size(dataset.features )
											snake_case_           =					parquet_writer_kwargs




				def 					a							(      self ):
											snake_case_           =					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

											if isinstance(self.path_or_buf   ,     (str, bytes, os.PathLike) ):
																		with open(self.path_or_buf   ,     'wb+' ) as buffer:
																									snake_case_           =					self._write(file_obj=snake_case   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											else:
																		snake_case_           =					self._write(file_obj=self.path_or_buf   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											return written




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     **snake_case ):
											snake_case_           =					0
											snake_case_           =					parquet_writer_kwargs.pop('path_or_buf'   ,     snake_case )
											snake_case_           =					self.dataset.features.arrow_schema

											snake_case_           =					pq.ParquetWriter(snake_case   ,     schema=snake_case   ,     **snake_case )

											for offset in logging.tqdm(
											    range(0   ,     len(self.dataset )   ,     snake_case )   ,     unit='ba'   ,     disable=not logging.is_progress_bar_enabled()   ,     desc='Creating parquet from Arrow format'   ,     ):
																		snake_case_           =					query_table(
																		    table=self.dataset._data   ,     key=slice(snake_case   ,     offset + batch_size )   ,     indices=self.dataset._indices if self.dataset._indices is not None else None   ,     )
																		writer.write_table(snake_case )
																		written += batch.nbytes
											writer.close()
											return written




