
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_UpperCAmelCase :			Union[str, 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"

_UpperCAmelCase :			List[str]              =	"\\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"

_UpperCAmelCase :			List[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 				A	( lowercase   ,	lowercase			)       ->  Union[str, Any]:
		'''simple docstring'''

		return float((preds == labels).mean()			)


def 				A	( lowercase   ,	lowercase   ,	lowercase="binary"			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					simple_accuracy(lowercase   ,	lowercase			)
		UpperCamelCase          =					float(fa_score(y_true=lowercase   ,	y_pred=lowercase   ,	average=lowercase			)			)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}


def 				A	( lowercase   ,	lowercase			)       ->  List[str]:
		'''simple docstring'''

		UpperCamelCase          =					{}
		for id_pred, label in zip(lowercase   ,	lowercase			):
				UpperCamelCase          =					f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
				UpperCamelCase          =					id_pred['prediction']
				if question_id in question_map:
						question_map[question_id].append((pred, label)			)
				else:
						UpperCamelCase          =					[(pred, label)]
		UpperCamelCase     ,  UpperCamelCase          =					[], []
		for question, preds_labels in question_map.items():
				UpperCamelCase     ,  UpperCamelCase          =					zip(*lowercase			)
				UpperCamelCase          =					fa_score(y_true=lowercase   ,	y_pred=lowercase   ,	average='macro'			)
				fas.append(lowercase			)
				UpperCamelCase          =					int(sum(pred == label for pred, label in preds_labels			) == len(lowercase			)			)
				ems.append(lowercase			)
		UpperCamelCase          =					float(sum(lowercase			) / len(lowercase			)			)
		UpperCamelCase          =					sum(lowercase			) / len(lowercase			)
		UpperCamelCase          =					float(fa_score(y_true=lowercase   ,	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     lowercase (		datasets.Metric       ):



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


from __future__ import annotations


class     lowercase :



		def __init__(			self    ,				A_    ,				A_	)  -> Any:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					text, pattern
				UpperCamelCase     ,  UpperCamelCase          =					len(A_	), len(A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if char == self.pattern[i]:
								return i
				return -1



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if self.pattern[i] != self.text[current_pos + i]:
								return current_pos + i
				return -1



		def __UpperCamelCase			(			self	)  -> list[int]:
				"""simple docstring"""
				# searches pattern in text and returns index positions
				UpperCamelCase          =					[]
				for i in range(self.textLen - self.patLen + 1	):
						UpperCamelCase          =					self.mismatch_in_text(A_	)
						if mismatch_index == -1:
								positions.append(A_	)
						else:
								UpperCamelCase          =					self.match_in_pattern(self.text[mismatch_index]	)
								UpperCamelCase          =					(
								    mismatch_index - match_index
								)  # shifting index lgtm [py/multiple-definition]
				return positions






_UpperCAmelCase :			Union[str, Any]              =	"ABAABA"
_UpperCAmelCase :			Any              =	"AB"
_UpperCAmelCase :			Dict              =	BoyerMooreSearch(text, pattern)
_UpperCAmelCase :			Optional[int]              =	bms.bad_character_heuristic()

if len(positions) == 0:
					print("No match found")
else:
					print("Pattern found in following positions: ")
					print(positions)
