



"""simple docstring"""

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowercase__			   =  "\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

lowercase__			   =  "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

lowercase__			   =  "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"





def       __magic_name__    (   _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Optional[Any]					):
							return float((preds == labels).mean()					)





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Optional[Any]="binary"					):
							__a		:		Optional[Any]									=       simple_accuracy(_lowerCamelCase  ,       _lowerCamelCase					)
							__a		:		Union[str, Any]									=       float(fa_score(y_true=_lowerCamelCase  ,       y_pred=_lowerCamelCase  ,       average=_lowerCamelCase					)					)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}





def       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Union[str, Any]					):
							__a		:		Dict									=       {}
							for id_pred, label in zip(_lowerCamelCase  ,       _lowerCamelCase					):
														__a		:		Dict									=       F'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
														__a		:		List[Any]									=       id_pred["""prediction"""]
														if question_id in question_map:
																					question_map[question_id].append((pred, label)					)
														else:
																					__a		:		Optional[int]									=       [(pred, label)]
							__a							,     __a		:		Optional[int]									=       [], []
							for question, preds_labels in question_map.items():
														__a							,     __a		:		Dict									=       zip(*_lowerCamelCase					)
														__a		:		str									=       fa_score(y_true=_lowerCamelCase  ,       y_pred=_lowerCamelCase  ,       average="""macro"""					)
														fas.append(_lowerCamelCase					)
														__a		:		str									=       int(sum(pred == label for pred, label in preds_labels					) == len(_lowerCamelCase					)					)
														ems.append(_lowerCamelCase					)
							__a		:		Dict									=       float(sum(_lowerCamelCase					) / len(_lowerCamelCase					)					)
							__a		:		Union[str, Any]									=       sum(_lowerCamelCase					) / len(_lowerCamelCase					)
							__a		:		List[Any]									=       float(fa_score(y_true=_lowerCamelCase  ,       y_pred=[id_pred["""prediction"""] for id_pred in ids_preds]					)					)
							return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,					_KWARGS_DESCRIPTION      )
class SCREAMING_SNAKE_CASE__					(		datasets.Metric      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													if self.config_name not in [
													    "boolq",
													    "cb",
													    "copa",
													    "multirc",
													    "record",
													    "rte",
													    "wic",
													    "wsc",
													    "wsc.fixed",
													    "axb",
													    "axg",
													]:
																				raise KeyError(
																				    """You should supply a configuration name selected in """
																				    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""							)
													return datasets.MetricInfo(
													    description=_DESCRIPTION						,     citation=_CITATION						,     inputs_description=_KWARGS_DESCRIPTION						,     features=datasets.Features(self._get_feature_types()							)						,     codebase_urls=[]						,     reference_urls=[]						,     format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None						,     )



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													if self.config_name == "record":
																				return {
																				    "predictions": {
																				        "idx": {
																				            "passage": datasets.Value("""int64"""							),
																				            "query": datasets.Value("""int64"""							),
																				        },
																				        "prediction_text": datasets.Value("""string"""							),
																				    },
																				    "references": {
																				        "idx": {
																				            "passage": datasets.Value("""int64"""							),
																				            "query": datasets.Value("""int64"""							),
																				        },
																				        "answers": datasets.Sequence(datasets.Value("""string"""							)							),
																				    },
																				}
													elif self.config_name == "multirc":
																				return {
																				    "predictions": {
																				        "idx": {
																				            "answer": datasets.Value("""int64"""							),
																				            "paragraph": datasets.Value("""int64"""							),
																				            "question": datasets.Value("""int64"""							),
																				        },
																				        "prediction": datasets.Value("""int64"""							),
																				    },
																				    "references": datasets.Value("""int64"""							),
																				}
													else:
																				return {
																				    "predictions": datasets.Value("""int64"""							),
																				    "references": datasets.Value("""int64"""							),
																				}





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													if self.config_name == "axb":
																				return {"matthews_correlation": matthews_corrcoef(_lowercase						,     _lowercase							)}
													elif self.config_name == "cb":
																				return acc_and_fa(_lowercase						,     _lowercase						,     fa_avg="""macro"""							)
													elif self.config_name == "record":
																				__a		:		List[str]									=       [
																				    {
																				        """qas""": [
																				            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
																				            for ref in references
																				        ]
																				    }
																				]
																				__a		:		List[Any]									=       {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
																				return evaluate_record(_lowercase						,     _lowercase							)[0]
													elif self.config_name == "multirc":
																				return evaluate_multirc(_lowercase						,     _lowercase							)
													elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																				return {"accuracy": simple_accuracy(_lowercase						,     _lowercase							)}
													else:
																				raise KeyError(
																				    """You should supply a configuration name selected in """
																				    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""							)






"""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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowercase__			   =  logging.get_logger(__name__)

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

lowercase__			   =  {
    "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"
        ),
    },
}
lowercase__			   =  {
    "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"
        ),
    },
}
lowercase__			   =  {
    "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"
        ),
    },
}

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


lowercase__			   =  {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowercase__			   =  {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowercase__			   =  {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					VOCAB_FILES_NAMES
						_lowerCAmelCase							=					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase							=					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase							=					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						_lowerCAmelCase							=					DPRContextEncoderTokenizer







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					VOCAB_FILES_NAMES
						_lowerCAmelCase							=					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase							=					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase							=					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						_lowerCAmelCase							=					DPRQuestionEncoderTokenizer







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

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


lowercase__			   =  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(__snake_case      )
class SCREAMING_SNAKE_CASE__					:



						def __call__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = None						,     _lowercase = False						,     _lowercase = False						,     _lowercase = None						,     _lowercase = None						,     _lowercase = None						,     **_lowercase						,     ):





													'''simple docstring'''

													if titles is None and texts is None:
																				return super().__call__(
																				    _lowercase						,     padding=_lowercase						,     truncation=_lowercase						,     max_length=_lowercase						,     return_tensors=_lowercase						,     return_attention_mask=_lowercase						,     **_lowercase						,     )
													elif titles is None or texts is None:
																				__a		:		str									=       titles if texts is None else texts
																				return super().__call__(
																				    _lowercase						,     _lowercase						,     padding=_lowercase						,     truncation=_lowercase						,     max_length=_lowercase						,     return_tensors=_lowercase						,     return_attention_mask=_lowercase						,     **_lowercase						,     )
													__a		:		str									=       titles if not isinstance(_lowercase						,     _lowercase							) else [titles]
													__a		:		Optional[Any]									=       texts if not isinstance(_lowercase						,     _lowercase							) else [texts]
													__a		:		Tuple									=       len(_lowercase							)
													__a		:		Dict									=       questions if not isinstance(_lowercase						,     _lowercase							) else [questions] * n_passages
													assert len(_lowercase							) == len(
													    _lowercase							), F'''There should be as many titles than texts but got {len(_lowercase							)} titles and {len(_lowercase							)} texts.'''
													__a		:		Optional[Any]									=       super().__call__(_lowercase						,     _lowercase						,     padding=_lowercase						,     truncation=_lowercase							)["""input_ids"""]
													__a		:		str									=       super().__call__(_lowercase						,     add_special_tokens=_lowercase						,     padding=_lowercase						,     truncation=_lowercase							)["""input_ids"""]
													__a		:		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(_lowercase						,     _lowercase							)
													    ]
													}
													if return_attention_mask is not False:
																				__a		:		Optional[int]									=       []
																				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		:		str									=       attention_mask
													return self.pad(_lowercase						,     padding=_lowercase						,     max_length=_lowercase						,     return_tensors=_lowercase							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase = 16						,     _lowercase = 64						,     _lowercase = 4						,     ):





													'''simple docstring'''

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

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





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     ):





													'''simple docstring'''

													__a		:		Tuple									=       []
													for start_index, start_score in enumerate(_lowercase							):
																				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		:		str									=       sorted(_lowercase						,     key=lambda _lowercase							: x[1]						,     reverse=_lowercase							)
													__a		:		Union[str, Any]									=       []
													for (start_index, end_index), score in scores:
																				assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
																				__a		:		List[str]									=       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(_lowercase							) == top_spans:
																											break
													return chosen_span_intervals







@add_end_docstrings(__snake_case      )
class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					__snake_case      ):
						_lowerCAmelCase							=					VOCAB_FILES_NAMES
						_lowerCAmelCase							=					READER_PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase							=					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase							=					READER_PRETRAINED_INIT_CONFIGURATION
						_lowerCAmelCase							=					["input_ids", "attention_mask"]
						_lowerCAmelCase							=					DPRReaderTokenizer

