
'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase            = {
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase            = [
						    "DPRContextEncoderTokenizerFast",
						    "DPRQuestionEncoderTokenizerFast",
						    "DPRReaderTokenizerFast",
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase            = [
						    "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "DPRContextEncoder",
						    "DPRPretrainedContextEncoder",
						    "DPRPreTrainedModel",
						    "DPRPretrainedQuestionEncoder",
						    "DPRPretrainedReader",
						    "DPRQuestionEncoder",
						    "DPRReader",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase            = [
						    "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFDPRContextEncoder",
						    "TFDPRPretrainedContextEncoder",
						    "TFDPRPretrainedQuestionEncoder",
						    "TFDPRPretrainedReader",
						    "TFDPRQuestionEncoder",
						    "TFDPRReader",
						]


if TYPE_CHECKING:
						from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
						from .tokenization_dpr import (
						    DPRContextEncoderTokenizer,
						    DPRQuestionEncoderTokenizer,
						    DPRReaderOutput,
						    DPRReaderTokenizer,
						)

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_dpr_fast import (
												    DPRContextEncoderTokenizerFast,
												    DPRQuestionEncoderTokenizerFast,
												    DPRReaderTokenizerFast,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_dpr import (
												    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPRContextEncoder,
												    DPRPretrainedContextEncoder,
												    DPRPreTrainedModel,
												    DPRPretrainedQuestionEncoder,
												    DPRPretrainedReader,
												    DPRQuestionEncoder,
												    DPRReader,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_dpr import (
												    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDPRContextEncoder,
												    TFDPRPretrainedContextEncoder,
												    TFDPRPretrainedQuestionEncoder,
												    TFDPRPretrainedReader,
												    TFDPRQuestionEncoder,
												    TFDPRReader,
												)

else:
						import sys

						UpperCAmelCase            = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






'''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,
)


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

lowerCamelCase__	  =		'\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

lowerCamelCase__	  =		'\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'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}\n'

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






		'''simple docstring'''
		def 						__lowercase      (						self							:       Optional[Any]		)      ->			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							:       Union[str, Any]      ,		UpperCamelCase_							:       Optional[int]      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       Dict=1.0		)      ->			Dict:







									'''simple docstring'''





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

									_lowercase							:		Tuple				=   make_qid_to_has_ans(UpperCamelCase_		)  # maps qid to True/False
									_lowercase							:		List[str]				=   [k for k, v in qid_to_has_ans.items() if v]
									_lowercase							:		Union[str, Any]				=   [k for k, v in qid_to_has_ans.items() if not v]

									_lowercase			, _lowercase							:		Tuple				=   get_raw_scores(UpperCamelCase_      ,		UpperCamelCase_		)
									_lowercase							:		int				=   apply_no_ans_threshold(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_		)
									_lowercase							:		int				=   apply_no_ans_threshold(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_		)
									_lowercase							:		List[str]				=   make_eval_dict(UpperCamelCase_      ,		UpperCamelCase_		)

									if has_ans_qids:
																_lowercase							:		Optional[int]				=   make_eval_dict(UpperCamelCase_      ,		UpperCamelCase_      ,		qid_list=UpperCamelCase_		)
																merge_eval(UpperCamelCase_      ,		UpperCamelCase_      ,		'''HasAns'''		)
									if no_ans_qids:
																_lowercase							:		int				=   make_eval_dict(UpperCamelCase_      ,		UpperCamelCase_      ,		qid_list=UpperCamelCase_		)
																merge_eval(UpperCamelCase_      ,		UpperCamelCase_      ,		'''NoAns'''		)
									find_all_best_thresh(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_		)
									return dict(UpperCamelCase_		)



