

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


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

transformers_logging.set_verbosity_info()






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Any:
	return max(metric_fn(lowerCamelCase__     ,			lowerCamelCase__      ) for gt in ground_truths      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    str 	=			[line.strip() for line in open(lowerCamelCase__     ,			'r'      ).readlines()]
	__lowerCamelCase    :    Optional[Any] 	=			[]

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

	__lowerCamelCase    :    List[str] 	=			0
	for prediction, ground_truths in zip(lowerCamelCase__     ,			lowerCamelCase__      ):
		total += 1
		em += metric_max_over_ground_truths(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
		fa += metric_max_over_ground_truths(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	__lowerCamelCase    :    Dict 	=			100.0 * em / total
	__lowerCamelCase    :    Any 	=			100.0 * fa / total

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Dict 	=			args.k
	__lowerCamelCase    :    Any 	=			[line.strip() for line in open(lowerCamelCase__     ,			'r'      ).readlines()]
	__lowerCamelCase    :    Any 	=			[line.strip() for line in open(lowerCamelCase__     ,			'r'      ).readlines()]

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

	__lowerCamelCase    :    List[str] 	=			100.0 * em / total
	logger.info(F"Precision@{k}: {em: .2f}"      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Union[str, Any]:
	def strip_title(lowerCamelCase__      ):
		if title.startswith('"'      ):
			__lowerCamelCase    :    List[str] 	=			title[1:]
		if title.endswith('"'      ):
			__lowerCamelCase    :    int 	=			title[:-1]
		return title

	__lowerCamelCase    :    Optional[Any] 	=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    lowerCamelCase__     ,			return_tensors='pt'     ,			padding=lowerCamelCase__     ,			truncation=lowerCamelCase__     ,			)['input_ids'].to(args.device      )

	__lowerCamelCase    :    int 	=			rag_model.rag.question_encoder(lowerCamelCase__      )
	__lowerCamelCase    :    List[Any] 	=			question_enc_outputs[0]

	__lowerCamelCase    :    int 	=			rag_model.retriever(
	    lowerCamelCase__     ,			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'     ,			)
	__lowerCamelCase    :    int 	=			rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
	__lowerCamelCase    :    Any 	=			[]
	for docs in all_docs:
		__lowerCamelCase    :    List[str] 	=			[strip_title(lowerCamelCase__      ) for title in docs['title']]
		provenance_strings.append('\t'.join(lowerCamelCase__      )      )
	return provenance_strings






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					int:
	with torch.no_grad():
		__lowerCamelCase    :    Dict 	=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    lowerCamelCase__     ,			return_tensors='pt'     ,			padding=lowerCamelCase__     ,			truncation=lowerCamelCase__      )

		__lowerCamelCase    :    List[str] 	=			inputs_dict.input_ids.to(args.device      )
		__lowerCamelCase    :    List[str] 	=			inputs_dict.attention_mask.to(args.device      )
		__lowerCamelCase    :    str 	=			rag_model.generate(  # rag_model overwrites generate
		    lowerCamelCase__     ,			attention_mask=lowerCamelCase__     ,			num_beams=args.num_beams     ,			min_length=args.min_length     ,			max_length=args.max_length     ,			early_stopping=lowerCamelCase__     ,			num_return_sequences=1     ,			bad_words_ids=[[0, 0]]     ,			)
		__lowerCamelCase    :    str 	=			rag_model.retriever.generator_tokenizer.batch_decode(lowerCamelCase__     ,			skip_special_tokens=lowerCamelCase__      )

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

		return answers






def        SCREAMING_SNAKE_CASE__	(  )  ->					Dict:
	__lowerCamelCase    :    Any 	=			argparse.ArgumentParser()
	parser.add_argument(
	    '--model_type'     ,			choices=['rag_sequence', 'rag_token', 'bart']     ,			type=lowerCamelCase__     ,			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=lowerCamelCase__     ,			choices=['exact', 'compressed', 'legacy']     ,			type=lowerCamelCase__     ,			help='RAG model retriever type'     ,			)
	parser.add_argument(
	    '--index_path'     ,			default=lowerCamelCase__     ,			type=lowerCamelCase__     ,			help='Path to the retrieval index'     ,			)
	parser.add_argument('--n_docs'     ,			default=5     ,			type=lowerCamelCase__     ,			help='Number of retrieved docs'      )
	parser.add_argument(
	    '--model_name_or_path'     ,			default=lowerCamelCase__     ,			type=lowerCamelCase__     ,			required=lowerCamelCase__     ,			help='Path to pretrained checkpoints or model identifier from huggingface.co/models'     ,			)
	parser.add_argument(
	    '--eval_mode'     ,			choices=['e2e', 'retrieval']     ,			default='e2e'     ,			type=lowerCamelCase__     ,			help=(
	        'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
	        ' precision@k.'
	    )     ,			)
	parser.add_argument('--k'     ,			default=1     ,			type=lowerCamelCase__     ,			help='k for the precision@k calculation'      )
	parser.add_argument(
	    '--evaluation_set'     ,			default=lowerCamelCase__     ,			type=lowerCamelCase__     ,			required=lowerCamelCase__     ,			help='Path to a file containing evaluation samples'     ,			)
	parser.add_argument(
	    '--gold_data_path'     ,			default=lowerCamelCase__     ,			type=lowerCamelCase__     ,			required=lowerCamelCase__     ,			help='Path to a tab-separated file with gold samples'     ,			)
	parser.add_argument(
	    '--gold_data_mode'     ,			default='qa'     ,			type=lowerCamelCase__     ,			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=lowerCamelCase__     ,			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=lowerCamelCase__     ,			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=lowerCamelCase__     ,			help='Number of beams to be used when generating answers'     ,			)
	parser.add_argument('--min_length'     ,			default=1     ,			type=lowerCamelCase__     ,			help='Min length of the generated answers'      )
	parser.add_argument('--max_length'     ,			default=5_0     ,			type=lowerCamelCase__     ,			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.'     ,			)
	__lowerCamelCase    :    int 	=			parser.parse_args()
	__lowerCamelCase    :    int 	=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
	return args






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

	__lowerCamelCase    :    str 	=			(
	    [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'     ,			lowerCamelCase__      )

	__lowerCamelCase    :    Optional[int] 	=			get_scores if args.eval_mode == 'e2e' else get_precision_at_k
	__lowerCamelCase    :    Optional[Any] 	=			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(lowerCamelCase__     ,			args.predictions_path     ,			args.gold_data_path      )
			continue

		logger.info('***** Running evaluation for {} *****'.format(lowerCamelCase__      )      )
		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'      ):
			__lowerCamelCase    :    Tuple 	=			RagRetriever.from_pretrained(lowerCamelCase__     ,			**lowerCamelCase__      )
			__lowerCamelCase    :    Dict 	=			model_class.from_pretrained(lowerCamelCase__     ,			retriever=lowerCamelCase__     ,			**lowerCamelCase__      )
			model.retriever.init_retrieval()
		else:
			__lowerCamelCase    :    Union[str, Any] 	=			model_class.from_pretrained(lowerCamelCase__     ,			**lowerCamelCase__      )
		model.to(args.device      )

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

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


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







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[Any]:

	# A local function to see if a dot lands in the circle.
	def is_in_circle(lowerCamelCase__     ,			lowerCamelCase__      ) -> bool:
		__lowerCamelCase    :    Dict 	=			sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	__lowerCamelCase    :    Optional[int] 	=			mean(
	    int(is_in_circle(uniform(-1.0     ,			1.0      )     ,			uniform(-1.0     ,			1.0      )      )      )
	    for _ in range(lowerCamelCase__      )      )
	# The ratio of the area for circle to square is pi/4.
	__lowerCamelCase    :    Any 	=			proportion * 4
	print(F"The estimated value of pi is {pi_estimate}"      )
	print(F"The numpy value of pi is {pi}"      )
	print(F"The total error is {abs(pi - pi_estimate      )}"      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0     ,			)  ->					float:
	return mean(
	    function_to_integrate(uniform(lowerCamelCase__     ,			lowerCamelCase__      )      ) for _ in range(lowerCamelCase__      )      ) * (max_value - min_value)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0      )  ->					None:

	def identity_function(lowerCamelCase__      ) -> float:
		return x

	__lowerCamelCase    :    str 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    int 	=			(max_value * max_value - min_value * min_value) / 2

	print('******************'      )
	print(F"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(F"Estimated value is {estimated_value}"      )
	print(F"Expected value is {expected_value}"      )
	print(F"Total error is {abs(estimated_value - expected_value      )}"      )
	print('******************'      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					None:

	def function_to_integrate(lowerCamelCase__      ) -> float:
		return sqrt(4.0 - x * x      )

	__lowerCamelCase    :    Any 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			0.0     ,			2.0      )

	print('******************'      )
	print('Estimating pi using area_under_curve_estimator'      )
	print(F"Estimated value is {estimated_value}"      )
	print(F"Expected value is {pi}"      )
	print(F"Total error is {abs(estimated_value - pi      )}"      )
	print('******************'      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




