



'''simple docstring'''







import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


SCREAMING_SNAKE_CASE__     : Any				=   """\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

SCREAMING_SNAKE_CASE__     : Tuple				=   """
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
"""

SCREAMING_SNAKE_CASE__     : str				=   """
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer 'id' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - 'id': id of the question-answer pair (see above),
            - 'answers': a list of Dict {'text': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    'exact': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'total': Number of score considered
    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'HasAns_f1': The F-score of predicted tokens versus the gold answer
    'HasAns_total': Number of score considered
    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'NoAns_f1': The F-score of predicted tokens versus the gold answer
    'NoAns_total': Number of score considered
    'best_exact': Best exact match (with varying threshold)
    'best_exact_thresh': No-answer probability threshold associated to the best exact match
    'best_f1': Best F1 (with varying threshold)
    'best_f1_thresh': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}
"""




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






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					return datasets.MetricInfo(
					    description=_DESCRIPTION      ,       citation=_CITATION      ,       inputs_description=_KWARGS_DESCRIPTION      ,       features=datasets.Features(
					        {
					            'predictions': {
					                'id': datasets.Value('string'	),
					                'prediction_text': datasets.Value('string'	),
					                'no_answer_probability': datasets.Value('float32'	),
					            },
					            'references': {
					                'id': datasets.Value('string'	),
					                'answers': datasets.features.Sequence(
					                    {'text': datasets.Value('string'	), 'answer_start': datasets.Value('int32'	)}	),
					            },
					        }	)      ,       codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']      ,       reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']      ,       )






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=1.0	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      {p['id']: p['no_answer_probability'] for p in predictions}
					SCREAMING_SNAKE_CASE_						:int										=      [{'paragraphs': [{'qas': references}]}]
					SCREAMING_SNAKE_CASE_						:List[str]										=      {p['id']: p['prediction_text'] for p in predictions}

					SCREAMING_SNAKE_CASE_						:Any										=      make_qid_to_has_ans(SCREAMING_SNAKE_CASE	)  # maps qid to True/False
					SCREAMING_SNAKE_CASE_						:str										=      [k for k, v in qid_to_has_ans.items() if v]
					SCREAMING_SNAKE_CASE_						:Any										=      [k for k, v in qid_to_has_ans.items() if not v]

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Tuple										=      get_raw_scores(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      apply_no_ans_threshold(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      apply_no_ans_threshold(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      make_eval_dict(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					if has_ans_qids:
							SCREAMING_SNAKE_CASE_						:str										=      make_eval_dict(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       qid_list=SCREAMING_SNAKE_CASE	)
							merge_eval(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       'HasAns'	)
					if no_ans_qids:
							SCREAMING_SNAKE_CASE_						:Tuple										=      make_eval_dict(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       qid_list=SCREAMING_SNAKE_CASE	)
							merge_eval(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       'NoAns'	)
					find_all_best_thresh(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					return dict(SCREAMING_SNAKE_CASE	)







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_  :							Any            =	{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							List[str]            =	["FlavaFeatureExtractor"]
					snake_case_  :							int            =	["FlavaImageProcessor"]
					snake_case_  :							Tuple            =	["FlavaProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Optional[int]            =	[
					    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "FlavaForPreTraining",
					    "FlavaImageCodebook",
					    "FlavaImageModel",
					    "FlavaModel",
					    "FlavaMultimodalModel",
					    "FlavaPreTrainedModel",
					    "FlavaTextModel",
					]

if TYPE_CHECKING:
					from .configuration_flava import (
					    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    FlavaConfig,
					    FlavaImageCodebookConfig,
					    FlavaImageConfig,
					    FlavaMultimodalConfig,
					    FlavaTextConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_flava import FlavaFeatureExtractor
										from .image_processing_flava import FlavaImageProcessor
										from .processing_flava import FlavaProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flava import (
										    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlavaForPreTraining,
										    FlavaImageCodebook,
										    FlavaImageModel,
										    FlavaModel,
										    FlavaMultimodalModel,
										    FlavaPreTrainedModel,
										    FlavaTextModel,
										)

else:
					import sys

					snake_case_  :							Any            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





