



"""simple docstring"""







import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCAmelCase_  		=  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def 	__UpperCAmelCase (       __lowerCamelCase      )				-> List[Any]:
		if "token" in model_name_or_path:
				return "rag_token"
		if "sequence" in model_name_or_path:
				return "rag_sequence"
		if "bart" in model_name_or_path:
				return "bart"
		return None



def 	__UpperCAmelCase (       __lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase      )				-> Any:
		return max(metric_fn(_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      ) for gt in ground_truths      )



def 	__UpperCAmelCase (       __lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase      )				-> Dict:
		lowercase__  :       Tuple												=     [line.strip() for line in open(_SCREAMING_SNAKE_CASE		,	'''r'''      ).readlines()]
		lowercase__  :       Tuple												=     []

		if args.gold_data_mode == "qa":
				lowercase__  :       List[str]												=     pd.read_csv(_SCREAMING_SNAKE_CASE		,	sep='''\t'''		,	header=_SCREAMING_SNAKE_CASE      )
				for answer_list in data[1]:
						lowercase__  :       int												=     ast.literal_eval(_SCREAMING_SNAKE_CASE      )
						answers.append(_SCREAMING_SNAKE_CASE      )
		else:
				lowercase__  :       List[Any]												=     [line.strip() for line in open(_SCREAMING_SNAKE_CASE		,	'''r'''      ).readlines()]
				lowercase__  :       int												=     [[reference] for reference in references]

		lowercase__  :       int												=     0
		for prediction, ground_truths in zip(_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      ):
				total += 1
				em += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      )
				fa += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      )

		lowercase__  :       int												=     1_0_0.0 * em / total
		lowercase__  :       int												=     1_0_0.0 * fa / total

		logger.info(f"""F1: {fa:.2f}"""      )
		logger.info(f"""EM: {em:.2f}"""      )



def 	__UpperCAmelCase (       __lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase      )				-> Any:
		lowercase__  :       List[Any]												=     args.k
		lowercase__  :       str												=     [line.strip() for line in open(_SCREAMING_SNAKE_CASE		,	'''r'''      ).readlines()]
		lowercase__  :       List[str]												=     [line.strip() for line in open(_SCREAMING_SNAKE_CASE		,	'''r'''      ).readlines()]

		lowercase__  :       List[Any]												=     0
		for hypo, reference in zip(_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      ):
				lowercase__  :       Optional[Any]												=     set(hypo.split('''\t'''      )[:k]      )
				lowercase__  :       List[str]												=     set(reference.split('''\t'''      )      )
				total += 1
				em += len(hypo_provenance & ref_provenance      ) / k

		lowercase__  :       List[Any]												=     1_0_0.0 * em / total
		logger.info(f"""Precision@{k}: {em: .2f}"""      )



def 	__UpperCAmelCase (       __lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase      )				-> Union[str, Any]:
		def strip_title(__lowerCamelCase      ):
				if title.startswith('''\"'''      ):
						lowercase__  :       str												=     title[1:]
				if title.endswith('''\"'''      ):
						lowercase__  :       str												=     title[:-1]
				return title

		lowercase__  :       Optional[int]												=     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    _SCREAMING_SNAKE_CASE		,	return_tensors='''pt'''		,	padding=_SCREAMING_SNAKE_CASE		,	truncation=_SCREAMING_SNAKE_CASE		,	)["""input_ids"""].to(args.device      )

		lowercase__  :       int												=     rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE      )
		lowercase__  :       Optional[Any]												=     question_enc_outputs[0]

		lowercase__  :       Any												=     rag_model.retriever(
		    _SCREAMING_SNAKE_CASE		,	question_enc_pool_output.cpu().detach().to(torch.floataa      ).numpy()		,	prefix=rag_model.rag.generator.config.prefix		,	n_docs=rag_model.config.n_docs		,	return_tensors='''pt'''		,	)
		lowercase__  :       Optional[Any]												=     rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
		lowercase__  :       List[str]												=     []
		for docs in all_docs:
				lowercase__  :       str												=     [strip_title(_SCREAMING_SNAKE_CASE      ) for title in docs["""title"""]]
				provenance_strings.append('''\t'''.join(_SCREAMING_SNAKE_CASE      )      )
		return provenance_strings



def 	__UpperCAmelCase (       __lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase      )				-> List[str]:
		with torch.no_grad():
				lowercase__  :       int												=     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    _SCREAMING_SNAKE_CASE		,	return_tensors='''pt'''		,	padding=_SCREAMING_SNAKE_CASE		,	truncation=_SCREAMING_SNAKE_CASE      )

				lowercase__  :       Tuple												=     inputs_dict.input_ids.to(args.device      )
				lowercase__  :       Dict												=     inputs_dict.attention_mask.to(args.device      )
				lowercase__  :       int												=     rag_model.generate(  # rag_model overwrites generate
				    _SCREAMING_SNAKE_CASE		,	attention_mask=_SCREAMING_SNAKE_CASE		,	num_beams=args.num_beams		,	min_length=args.min_length		,	max_length=args.max_length		,	early_stopping=_SCREAMING_SNAKE_CASE		,	num_return_sequences=1		,	bad_words_ids=[[0, 0]]		,	)
				lowercase__  :       Optional[Any]												=     rag_model.retriever.generator_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		,	skip_special_tokens=_SCREAMING_SNAKE_CASE      )

				if args.print_predictions:
						for q, a in zip(_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      ):
								logger.info('''Q: {} - A: {}'''.format(_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      )      )

				return answers



def 	__UpperCAmelCase (       )				-> int:
		lowercase__  :       Optional[int]												=     argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type'''		,	choices=['''rag_sequence''', '''rag_token''', '''bart''']		,	type=_SCREAMING_SNAKE_CASE		,	help=(
		        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
		        ''' model_name_or_path'''
		    )		,	)
		parser.add_argument(
		    '''--index_name'''		,	default=_SCREAMING_SNAKE_CASE		,	choices=['''exact''', '''compressed''', '''legacy''']		,	type=_SCREAMING_SNAKE_CASE		,	help='''RAG model retriever type'''		,	)
		parser.add_argument(
		    '''--index_path'''		,	default=_SCREAMING_SNAKE_CASE		,	type=_SCREAMING_SNAKE_CASE		,	help='''Path to the retrieval index'''		,	)
		parser.add_argument('''--n_docs'''		,	default=5		,	type=_SCREAMING_SNAKE_CASE		,	help='''Number of retrieved docs'''      )
		parser.add_argument(
		    '''--model_name_or_path'''		,	default=_SCREAMING_SNAKE_CASE		,	type=_SCREAMING_SNAKE_CASE		,	required=_SCREAMING_SNAKE_CASE		,	help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''		,	)
		parser.add_argument(
		    '''--eval_mode'''		,	choices=['''e2e''', '''retrieval''']		,	default='''e2e'''		,	type=_SCREAMING_SNAKE_CASE		,	help=(
		        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
		        ''' precision@k.'''
		    )		,	)
		parser.add_argument('''--k'''		,	default=1		,	type=_SCREAMING_SNAKE_CASE		,	help='''k for the precision@k calculation'''      )
		parser.add_argument(
		    '''--evaluation_set'''		,	default=_SCREAMING_SNAKE_CASE		,	type=_SCREAMING_SNAKE_CASE		,	required=_SCREAMING_SNAKE_CASE		,	help='''Path to a file containing evaluation samples'''		,	)
		parser.add_argument(
		    '''--gold_data_path'''		,	default=_SCREAMING_SNAKE_CASE		,	type=_SCREAMING_SNAKE_CASE		,	required=_SCREAMING_SNAKE_CASE		,	help='''Path to a tab-separated file with gold samples'''		,	)
		parser.add_argument(
		    '''--gold_data_mode'''		,	default='''qa'''		,	type=_SCREAMING_SNAKE_CASE		,	choices=['''qa''', '''ans''']		,	help=(
		        '''Format of the gold data file'''
		        '''qa - a single line in the following format: question [tab] answer_list'''
		        '''ans - a single line of the gold file contains the expected answer string'''
		    )		,	)
		parser.add_argument(
		    '''--predictions_path'''		,	type=_SCREAMING_SNAKE_CASE		,	default='''predictions.txt'''		,	help='''Name of the predictions file, to be stored in the checkpoints directory'''		,	)
		parser.add_argument(
		    '''--eval_all_checkpoints'''		,	action='''store_true'''		,	help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''		,	)
		parser.add_argument(
		    '''--eval_batch_size'''		,	default=8		,	type=_SCREAMING_SNAKE_CASE		,	help='''Batch size per GPU/CPU for evaluation.'''		,	)
		parser.add_argument(
		    '''--recalculate'''		,	help='''Recalculate predictions even if the prediction file exists'''		,	action='''store_true'''		,	)
		parser.add_argument(
		    '''--num_beams'''		,	default=4		,	type=_SCREAMING_SNAKE_CASE		,	help='''Number of beams to be used when generating answers'''		,	)
		parser.add_argument('''--min_length'''		,	default=1		,	type=_SCREAMING_SNAKE_CASE		,	help='''Min length of the generated answers'''      )
		parser.add_argument('''--max_length'''		,	default=50		,	type=_SCREAMING_SNAKE_CASE		,	help='''Max length of the generated answers'''      )

		parser.add_argument(
		    '''--print_predictions'''		,	action='''store_true'''		,	help='''If True, prints predictions while evaluating.'''		,	)
		parser.add_argument(
		    '''--print_docs'''		,	action='''store_true'''		,	help='''If True, prints docs retried while generating.'''		,	)
		lowercase__  :       Optional[Any]												=     parser.parse_args()
		lowercase__  :       Tuple												=     torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''      )
		return args



def 	__UpperCAmelCase (       __lowerCamelCase      )				-> str:
		lowercase__  :       List[Any]												=     {}
		if args.model_type is None:
				lowercase__  :       Any												=     infer_model_type(args.model_name_or_path      )
				assert args.model_type is not None
		if args.model_type.startswith('''rag'''      ):
				lowercase__  :       List[str]												=     RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
				lowercase__  :       int												=     args.n_docs
				if args.index_name is not None:
						lowercase__  :       Optional[Any]												=     args.index_name
				if args.index_path is not None:
						lowercase__  :       int												=     args.index_path
		else:
				lowercase__  :       Optional[int]												=     BartForConditionalGeneration

		lowercase__  :       int												=     (
		    [f.path for f in os.scandir(args.model_name_or_path      ) if f.is_dir()]
		    if args.eval_all_checkpoints
		    else [args.model_name_or_path]
		)

		logger.info('''Evaluate the following checkpoints: %s'''		,	_SCREAMING_SNAKE_CASE      )

		lowercase__  :       Dict												=     get_scores if args.eval_mode == """e2e""" else get_precision_at_k
		lowercase__  :       int												=     evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

		for checkpoint in checkpoints:
				if os.path.exists(args.predictions_path      ) and (not args.recalculate):
						logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path      )      )
						score_fn(_SCREAMING_SNAKE_CASE		,	args.predictions_path		,	args.gold_data_path      )
						continue

				logger.info('''***** Running evaluation for {} *****'''.format(_SCREAMING_SNAKE_CASE      )      )
				logger.info('''  Batch size = %d'''		,	args.eval_batch_size      )
				logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path      )      )

				if args.model_type.startswith('''rag'''      ):
						lowercase__  :       Any												=     RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		,	**_SCREAMING_SNAKE_CASE      )
						lowercase__  :       List[str]												=     model_class.from_pretrained(_SCREAMING_SNAKE_CASE		,	retriever=_SCREAMING_SNAKE_CASE		,	**_SCREAMING_SNAKE_CASE      )
						model.retriever.init_retrieval()
				else:
						lowercase__  :       Union[str, Any]												=     model_class.from_pretrained(_SCREAMING_SNAKE_CASE		,	**_SCREAMING_SNAKE_CASE      )
				model.to(args.device      )

				with open(args.evaluation_set		,	'''r'''      ) as eval_file, open(args.predictions_path		,	'''w'''      ) as preds_file:
						lowercase__  :       Dict												=     []
						for line in tqdm(_SCREAMING_SNAKE_CASE      ):
								questions.append(line.strip()      )
								if len(_SCREAMING_SNAKE_CASE      ) == args.eval_batch_size:
										lowercase__  :       Union[str, Any]												=     evaluate_batch_fn(_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      )
										preds_file.write('''\n'''.join(_SCREAMING_SNAKE_CASE      ) + '''\n'''      )
										preds_file.flush()
										lowercase__  :       List[str]												=     []
						if len(_SCREAMING_SNAKE_CASE      ) > 0:
								lowercase__  :       Optional[Any]												=     evaluate_batch_fn(_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      )
								preds_file.write('''\n'''.join(_SCREAMING_SNAKE_CASE      )      )
								preds_file.flush()

						score_fn(_SCREAMING_SNAKE_CASE		,	args.predictions_path		,	args.gold_data_path      )


if __name__ == "__main__":
							lowerCAmelCase_  		=  get_args()
							main(args)

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 = }''')






