





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							:			List[str]        =							logging.get_logger(__name__)

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

_UpperCAmelCase							:			Union[str, 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							:			Tuple        =							{
    """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							:			List[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							:			Dict        =							{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
_UpperCAmelCase							:			Optional[Any]        =							{
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
_UpperCAmelCase							:			Tuple        =							{
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


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

class   lowerCAmelCase				(   __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


class   lowerCAmelCase				(   __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							:			str        =							collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

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


_UpperCAmelCase							:			str        =							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(__UpperCamelCase     )
class   lowerCAmelCase				:
 def __call__( self    : int     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : Optional[str] = None     ,	UpperCAmelCase    : Optional[str] = None     ,	UpperCAmelCase    : Union[bool, str] = False     ,	UpperCAmelCase    : Union[bool, str] = False     ,	UpperCAmelCase    : Optional[int] = None     ,	UpperCAmelCase    : Optional[Union[str, TensorType]] = None     ,	UpperCAmelCase    : Optional[bool] = None     ,	**UpperCAmelCase    : List[Any]     ,	)     ->      BatchEncoding:
     if titles is None and texts is None:
         return super().__call__(
             UpperCAmelCase     ,	padding=UpperCAmelCase     ,	truncation=UpperCAmelCase     ,	max_length=UpperCAmelCase     ,	return_tensors=UpperCAmelCase     ,	return_attention_mask=UpperCAmelCase     ,	**UpperCAmelCase     ,	)
     elif titles is None or texts is None:
         lowerCamelCase__       : Tuple   					=						titles if texts is None else texts
         return super().__call__(
             UpperCAmelCase     ,	UpperCAmelCase     ,	padding=UpperCAmelCase     ,	truncation=UpperCAmelCase     ,	max_length=UpperCAmelCase     ,	return_tensors=UpperCAmelCase     ,	return_attention_mask=UpperCAmelCase     ,	**UpperCAmelCase     ,	)
     lowerCamelCase__       : Optional[int]   					=						titles if not isinstance(UpperCAmelCase     ,	UpperCAmelCase      ) else [titles]
     lowerCamelCase__       : Optional[int]   					=						texts if not isinstance(UpperCAmelCase     ,	UpperCAmelCase      ) else [texts]
     lowerCamelCase__       : Optional[int]   					=						len(UpperCAmelCase      )
     lowerCamelCase__       : int   					=						questions if not isinstance(UpperCAmelCase     ,	UpperCAmelCase      ) else [questions] * n_passages
     if len(UpperCAmelCase      ) != len(UpperCAmelCase      ):
         raise ValueError(
             F"""There should be as many titles than texts but got {len(UpperCAmelCase      )} titles and {len(UpperCAmelCase      )} texts."""      )
     lowerCamelCase__       : List[str]   					=						super().__call__(UpperCAmelCase     ,	UpperCAmelCase     ,	padding=UpperCAmelCase     ,	truncation=UpperCAmelCase      )['input_ids']
     lowerCamelCase__       : List[Any]   					=						super().__call__(UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase     ,	padding=UpperCAmelCase     ,	truncation=UpperCAmelCase      )['input_ids']
     lowerCamelCase__       : Union[str, Any]   					=						{
         '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(UpperCAmelCase     ,	UpperCAmelCase      )
         ]
     }
     if return_attention_mask is not False:
         lowerCamelCase__       : Dict   					=						[]
         for input_ids in encoded_inputs["input_ids"]:
             attention_mask.append([int(input_id != self.pad_token_id      ) for input_id in input_ids]      )
         lowerCamelCase__       : Optional[int]   					=						attention_mask
     return self.pad(UpperCAmelCase     ,	padding=UpperCAmelCase     ,	max_length=UpperCAmelCase     ,	return_tensors=UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : BatchEncoding     ,	UpperCAmelCase    : DPRReaderOutput     ,	UpperCAmelCase    : int = 16     ,	UpperCAmelCase    : int = 64     ,	UpperCAmelCase    : int = 4     ,	)     ->      List[DPRSpanPrediction]:
     lowerCamelCase__       : int   					=						reader_input['input_ids']
     lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						reader_output[:3]
     lowerCamelCase__       : str   					=						len(UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						sorted(range(UpperCAmelCase      )     ,	reverse=UpperCAmelCase     ,	key=relevance_logits.__getitem__      )
     lowerCamelCase__       : List[DPRReaderOutput]   					=						[]
     for doc_id in sorted_docs:
         lowerCamelCase__       : Tuple   					=						list(input_ids[doc_id]      )
         # assuming question & title information is at the beginning of the sequence
         lowerCamelCase__       : List[Any]   					=						sequence_ids.index(self.sep_token_id     ,	2      ) + 1  # second sep id
         if sequence_ids[-1] == self.pad_token_id:
             lowerCamelCase__       : Tuple   					=						sequence_ids.index(self.pad_token_id      )
         else:
             lowerCamelCase__       : str   					=						len(UpperCAmelCase      )

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







 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : List[int]     ,	UpperCAmelCase    : List[int]     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : int     ,	)     ->      List[DPRSpanPrediction]:
     lowerCamelCase__       : Any   					=						[]
     for start_index, start_score in enumerate(UpperCAmelCase      ):
         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)      )
     lowerCamelCase__       : Optional[Any]   					=						sorted(UpperCAmelCase     ,	key=lambda UpperCAmelCase      : x[1]     ,	reverse=UpperCAmelCase      )
     lowerCamelCase__       : Tuple   					=						[]
     for (start_index, end_index), score in scores:
         if start_index > end_index:
             raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""      )
         lowerCamelCase__       : List[Any]   					=						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(UpperCAmelCase      ) == top_spans:
             break
     return chosen_span_intervals


@add_end_docstrings(__UpperCamelCase     )
class   lowerCAmelCase				(   __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"""]












import pytest


_UpperCAmelCase							:			Optional[int]        =							"""__dummy_dataset1__"""

_UpperCAmelCase							:			Dict        =							"""
import json
import os

import datasets


REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),
                \"ner_tags\": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            \"O\",
                            \"B-PER\",
                            \"I-PER\",
                            \"B-ORG\",
                            \"I-ORG\",
                            \"B-LOC\",
                            \"I-LOC\",
                        ]
                    )
                ),
                \"langs\": datasets.Sequence(datasets.Value(\"string\")),
                \"spans\": datasets.Sequence(datasets.Value(\"string\")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, \"r\", encoding=\"utf-8\") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
"""


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    return DATASET_LOADING_SCRIPT_NAME


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	List[Any]:
    return DATASET_LOADING_SCRIPT_CODE


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[Any]:
    lowerCamelCase__       : int   					=						dataset_loading_script_name
    lowerCamelCase__       : Any   					=						tmp_path / 'datasets' / script_name
    script_dir.mkdir(parents=_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						script_dir / F"""{script_name}.py"""
    with open(_UpperCAmelCase		,      'w'					) as f:
        f.write(_UpperCAmelCase					)
    return str(_UpperCAmelCase					)





