from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


SCREAMING_SNAKE_CASE		:     Optional[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"

SCREAMING_SNAKE_CASE		:     Dict         =	"\\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"

SCREAMING_SNAKE_CASE		:     Tuple         =	"\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_			(   _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				Tuple			):
						return float((preds == labels).mean()			)






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






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





						def        A_							(self						)				->				Optional[int]:
												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        A_							(self						)				->				Any:
												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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				int:
												if self.config_name == "axb":
																		return {"matthews_correlation": matthews_corrcoef(__UpperCamelCase			,     __UpperCamelCase						)}
												elif self.config_name == "cb":
																		return acc_and_fa(__UpperCamelCase			,     __UpperCamelCase			,     fa_avg="""macro"""						)
												elif self.config_name == "record":
																		UpperCamelCase_		:							Any  =		[
																		    {
																		        """qas""": [
																		            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
																		            for ref in references
																		        ]
																		    }
																		]
																		UpperCamelCase_		:							Tuple  =		{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
																		return evaluate_record(__UpperCamelCase			,     __UpperCamelCase						)[0]
												elif self.config_name == "multirc":
																		return evaluate_multirc(__UpperCamelCase			,     __UpperCamelCase						)
												elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																		return {"accuracy": simple_accuracy(__UpperCamelCase			,     __UpperCamelCase						)}
												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

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{"UserAgent": UserAgent().random}






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict			):
						UpperCamelCase_		:							Tuple  =		script.contents[0]
						UpperCamelCase_		:							List[Any]  =		json.loads(data[data.find("""{\"config\""""			) : -1]			)
						return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]

class        UpperCamelCase						:





						def __init__(self			,     __UpperCamelCase						)				->				List[str]:
												UpperCamelCase_		:							Union[str, Any]  =		f'''https://www.instagram.com/{username}/'''
												UpperCamelCase_		:							Optional[int]  =		self.get_json()





						def        A_							(self						)				->				dict:
												UpperCamelCase_		:							Optional[Any]  =		requests.get(self.url			,     headers=__UpperCamelCase						).text
												UpperCamelCase_		:							Any  =		BeautifulSoup(__UpperCamelCase			,     """html.parser"""						).find_all("""script"""						)
												try:
																		return extract_user_profile(scripts[4]						)
												except (json.decoder.JSONDecodeError, KeyError):
																		return extract_user_profile(scripts[3]						)





						def __repr__(self						)				->				str:
												return f'''{self.__class__.__name__}(\'{self.username}\')'''





						def __str__(self						)				->				str:
												return f'''{self.fullname} ({self.username}) is {self.biography}'''





						@property
						def        A_							(self						)				->				str:
												return self.user_data["username"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["full_name"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["biography"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["business_email"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["external_url"]





						@property
						def        A_							(self						)				->				int:
												return self.user_data["edge_followed_by"]["count"]





						@property
						def        A_							(self						)				->				int:
												return self.user_data["edge_follow"]["count"]





						@property
						def        A_							(self						)				->				int:
												return self.user_data["edge_owner_to_timeline_media"]["count"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["profile_pic_url_hd"]





						@property
						def        A_							(self						)				->				bool:
												return self.user_data["is_verified"]





						@property
						def        A_							(self						)				->				bool:
												return self.user_data["is_private"]






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str = "github"			):
						import os

						if os.environ.get("""CI"""			):
												return  # test failing on GitHub Actions
						UpperCamelCase_		:							Any  =		InstagramUser(_SCREAMING_SNAKE_CASE			)
						assert instagram_user.user_data
						assert isinstance(instagram_user.user_data		, _SCREAMING_SNAKE_CASE			)
						assert instagram_user.username == username
						if username != "github":
												return
						assert instagram_user.fullname == "GitHub"
						assert instagram_user.biography == "Built for developers."
						assert instagram_user.number_of_posts > 150
						assert instagram_user.number_of_followers > 12_0000
						assert instagram_user.number_of_followings > 15
						assert instagram_user.email == "support@github.com"
						assert instagram_user.website == "https://github.com/readme"
						assert instagram_user.profile_picture_url.startswith("""https://instagram."""			)
						assert instagram_user.is_verified is True
						assert instagram_user.is_private is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	SCREAMING_SNAKE_CASE		:     Dict         =	InstagramUser("github")
	print(instagram_user)
	print(F'''{instagram_user.number_of_posts = }''')
	print(F'''{instagram_user.number_of_followers = }''')
	print(F'''{instagram_user.number_of_followings = }''')
	print(F'''{instagram_user.email = }''')
	print(F'''{instagram_user.website = }''')
	print(F'''{instagram_user.profile_picture_url = }''')
	print(F'''{instagram_user.is_verified = }''')
	print(F'''{instagram_user.is_private = }''')






