


import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
			from transformers.models.rag.configuration_rag import RagConfig
			from transformers.models.rag.tokenization_rag import RagTokenizer

@require_faiss
@require_torch
class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Dict       )			->					List[str]:
														__snake_case	:    Union[str, Any]											=     tempfile.mkdtemp()
														__snake_case	:    Dict											=     8

														# DPR tok
														__snake_case	:    Any											=     [
														    "[UNK]",
														    "[CLS]",
														    "[SEP]",
														    "[PAD]",
														    "[MASK]",
														    "want",
														    "##want",
														    "##ed",
														    "wa",
														    "un",
														    "runn",
														    "##ing",
														    ",",
														    "low",
														    "lowest",
														]
														__snake_case	:    int											=     os.path.join(self.tmpdirname					,			"dpr_tokenizer"       )
														os.makedirs(lowerCamelCase					,			exist_ok=lowerCamelCase       )
														__snake_case	:    Union[str, Any]											=     os.path.join(lowerCamelCase					,			DPR_VOCAB_FILES_NAMES["vocab_file"]       )
														with open(self.vocab_file					,			"w"					,			encoding="utf-8"       ) as vocab_writer:
																					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]       )       )

														# BART tok
														__snake_case	:    List[Any]											=     [
														    "l",
														    "o",
														    "w",
														    "e",
														    "r",
														    "s",
														    "t",
														    "i",
														    "d",
														    "n",
														    "\u0120",
														    "\u0120l",
														    "\u0120n",
														    "\u0120lo",
														    "\u0120low",
														    "er",
														    "\u0120lowest",
														    "\u0120newer",
														    "\u0120wider",
														    "<unk>",
														]
														__snake_case	:    int											=     dict(zip(lowerCamelCase					,			range(len(lowerCamelCase       )       )       )       )
														__snake_case	:    Optional[int]											=     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
														__snake_case	:    Optional[int]											=     {"unk_token": "<unk>"}

														__snake_case	:    str											=     os.path.join(self.tmpdirname					,			"bart_tokenizer"       )
														os.makedirs(lowerCamelCase					,			exist_ok=lowerCamelCase       )
														__snake_case	:    Union[str, Any]											=     os.path.join(lowerCamelCase					,			BART_VOCAB_FILES_NAMES["vocab_file"]       )
														__snake_case	:    List[Any]											=     os.path.join(lowerCamelCase					,			BART_VOCAB_FILES_NAMES["merges_file"]       )
														with open(self.vocab_file					,			"w"					,			encoding="utf-8"       ) as fp:
																					fp.write(json.dumps(lowerCamelCase       ) + "\n"       )
														with open(self.merges_file					,			"w"					,			encoding="utf-8"       ) as fp:
																					fp.write("\n".join(lowerCamelCase       )       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					DPRQuestionEncoderTokenizer:
														return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,			"dpr_tokenizer"       )       )

							def 					__snake_case      (							self :						Any       )			->					BartTokenizer:
														return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname					,			"bart_tokenizer"       )       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					str:
														shutil.rmtree(self.tmpdirname       )

							@require_tokenizers
							def 					__snake_case      (							self :						List[Any]       )			->					Optional[Any]:
														__snake_case	:    Union[str, Any]											=     os.path.join(self.tmpdirname					,			"rag_tokenizer"       )
														__snake_case	:    Optional[Any]											=     RagConfig(question_encoder=DPRConfig().to_dict()					,			generator=BartConfig().to_dict()       )
														__snake_case	:    Tuple											=     RagTokenizer(question_encoder=self.get_dpr_tokenizer()					,			generator=self.get_bart_tokenizer()       )
														rag_config.save_pretrained(lowerCamelCase       )
														rag_tokenizer.save_pretrained(lowerCamelCase       )
														__snake_case	:    List[str]											=     RagTokenizer.from_pretrained(lowerCamelCase					,			config=lowerCamelCase       )
														self.assertIsInstance(new_rag_tokenizer.question_encoder					,			lowerCamelCase       )
														self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()					,			rag_tokenizer.question_encoder.get_vocab()       )
														self.assertIsInstance(new_rag_tokenizer.generator					,			lowerCamelCase       )
														self.assertEqual(new_rag_tokenizer.generator.get_vocab()					,			rag_tokenizer.generator.get_vocab()       )

							@slow
							def 					__snake_case      (							self :						Any       )			->					Optional[Any]:
														__snake_case	:    Dict											=     RagTokenizer.from_pretrained("facebook/rag-token-nq"       )
														__snake_case	:    List[str]											=     [
														    "who got the first nobel prize in physics",
														    "when is the next deadpool movie being released",
														    "which mode is used for short wave broadcast service",
														    "who is the owner of reading football club",
														    "when is the next scandal episode coming out",
														    "when is the last time the philadelphia won the superbowl",
														    "what is the most current adobe flash player version",
														    "how many episodes are there in dragon ball z",
														    "what is the first step in the evolution of the eye",
														    "where is gall bladder situated in human body",
														    "what is the main mineral in lithium batteries",
														    "who is the president of usa right now",
														    "where do the greasers live in the outsiders",
														    "panda is a national animal of which country",
														    "what is the name of manchester united stadium",
														]
														__snake_case	:    str											=     tokenizer(lowerCamelCase       )
														self.assertIsNotNone(lowerCamelCase       )






							@slow
							def 					__snake_case      (							self :						List[Any]       )			->					Dict:
														__snake_case	:    Optional[int]											=     RagTokenizer.from_pretrained("facebook/rag-sequence-nq"       )
														__snake_case	:    List[Any]											=     [
														    "who got the first nobel prize in physics",
														    "when is the next deadpool movie being released",
														    "which mode is used for short wave broadcast service",
														    "who is the owner of reading football club",
														    "when is the next scandal episode coming out",
														    "when is the last time the philadelphia won the superbowl",
														    "what is the most current adobe flash player version",
														    "how many episodes are there in dragon ball z",
														    "what is the first step in the evolution of the eye",
														    "where is gall bladder situated in human body",
														    "what is the main mineral in lithium batteries",
														    "who is the president of usa right now",
														    "where do the greasers live in the outsiders",
														    "panda is a national animal of which country",
														    "what is the name of manchester united stadium",
														]
														__snake_case	:    Tuple											=     tokenizer(lowerCamelCase       )
														self.assertIsNotNone(lowerCamelCase       )





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_snake_case  :	List[Any]			      =       "\\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"

_snake_case  :	Any			      =       "\\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"

_snake_case  :	str			      =       "\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        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							return float((preds == labels).mean()			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase="binary"			):
							__snake_case	:    Union[str, Any]											=     simple_accuracy(__lowerCamelCase							,			__lowerCamelCase			)
							__snake_case	:    List[str]											=     float(fa_score(y_true=__lowerCamelCase							,			y_pred=__lowerCamelCase							,			average=__lowerCamelCase			)			)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    List[Any]											=     {}
							for id_pred, label in zip(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    int											=     F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
														__snake_case	:    str											=     id_pred["prediction"]
														if question_id in question_map:
																					question_map[question_id].append((pred, label)			)
														else:
																					__snake_case	:    Optional[Any]											=     [(pred, label)]
							__snake_case				,    __snake_case	:    Dict											=     [], []
							for question, preds_labels in question_map.items():
														__snake_case				,    __snake_case	:    int											=     zip(*__lowerCamelCase			)
														__snake_case	:    Optional[Any]											=     fa_score(y_true=__lowerCamelCase							,			y_pred=__lowerCamelCase							,			average="macro"			)
														fas.append(__lowerCamelCase			)
														__snake_case	:    Union[str, Any]											=     int(sum(pred == label for pred, label in preds_labels			) == len(__lowerCamelCase			)			)
														ems.append(__lowerCamelCase			)
							__snake_case	:    Tuple											=     float(sum(__lowerCamelCase			) / len(__lowerCamelCase			)			)
							__snake_case	:    Any											=     sum(__lowerCamelCase			) / len(__lowerCamelCase			)
							__snake_case	:    int											=     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        a	(datasets.Metric   ):




							"""simple docstring"""


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

