

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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						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					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						return max(metric_fn(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ) for gt in ground_truths       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[line.strip() for line in open(SCREAMING_SNAKE_CASE__  ,		'''r'''       ).readlines()]
						snake_case_   =	[]

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

						snake_case_   =	snake_case_   =	snake_case_   =	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__       )

						snake_case_   =	100.0 * em / total
						snake_case_   =	100.0 * fa / total

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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	args.k
						snake_case_   =	[line.strip() for line in open(SCREAMING_SNAKE_CASE__  ,		'''r'''       ).readlines()]
						snake_case_   =	[line.strip() for line in open(SCREAMING_SNAKE_CASE__  ,		'''r'''       ).readlines()]

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

						snake_case_   =	100.0 * em / total
						logger.info(F'''Precision@{k}: {em: .2f}'''       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						def strip_title(SCREAMING_SNAKE_CASE__       ):
												if title.startswith('''"'''       ):
																		snake_case_   =	title[1:]
												if title.endswith('''"'''       ):
																		snake_case_   =	title[:-1]
												return title

						snake_case_   =	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       )

						snake_case_   =	rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	question_enc_outputs[0]

						snake_case_   =	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'''  ,		)
						snake_case_   =	rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
						snake_case_   =	[]
						for docs in all_docs:
												snake_case_   =	[strip_title(SCREAMING_SNAKE_CASE__       ) for title in docs['''title''']]
												provenance_strings.append('''\t'''.join(SCREAMING_SNAKE_CASE__       )       )
						return provenance_strings


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						with torch.no_grad():
												snake_case_   =	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    SCREAMING_SNAKE_CASE__  ,		return_tensors='''pt'''  ,		padding=SCREAMING_SNAKE_CASE__  ,		truncation=SCREAMING_SNAKE_CASE__       )

												snake_case_   =	inputs_dict.input_ids.to(args.device       )
												snake_case_   =	inputs_dict.attention_mask.to(args.device       )
												snake_case_   =	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]]  ,		)
												snake_case_   =	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   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	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.'''  ,		)
						snake_case_   =	parser.parse_args()
						snake_case_   =	torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )
						return args


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	{}
						if args.model_type is None:
												snake_case_   =	infer_model_type(args.model_name_or_path       )
												assert args.model_type is not None
						if args.model_type.startswith('''rag'''       ):
												snake_case_   =	RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
												snake_case_   =	args.n_docs
												if args.index_name is not None:
																		snake_case_   =	args.index_name
												if args.index_path is not None:
																		snake_case_   =	args.index_path
						else:
												snake_case_   =	BartForConditionalGeneration

						snake_case_   =	(
						    [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__       )

						snake_case_   =	get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
						snake_case_   =	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'''       ):
																		snake_case_   =	RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )
																		snake_case_   =	model_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,		retriever=SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )
																		model.retriever.init_retrieval()
												else:
																		snake_case_   =	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:
																		snake_case_   =	[]
																		for line in tqdm(SCREAMING_SNAKE_CASE__       ):
																								questions.append(line.strip()       )
																								if len(SCREAMING_SNAKE_CASE__       ) == args.eval_batch_size:
																														snake_case_   =	evaluate_batch_fn(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																														preds_file.write('''\n'''.join(SCREAMING_SNAKE_CASE__       ) + '''\n'''       )
																														preds_file.flush()
																														snake_case_   =	[]
																		if len(SCREAMING_SNAKE_CASE__       ) > 0:
																								snake_case_   =	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)


import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return x + 2





class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	'''x = 3'''
									snake_case_   =	{}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									assert result == 3
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3}							)

									snake_case_   =	'''x = y'''
									snake_case_   =	{'''y''': 5}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 5, '''y''': 5}							)







			def 				snake_case__(     self :				Dict							)      ->Optional[int]:
									snake_case_   =	'''y = add_two(x)'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 5}							)

									# Won't work without the tool
									with CaptureStdout() as out:
															snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									assert result is None
									assert "tried to execute add_two" in out.out







			def 				snake_case__(     self :				Union[str, Any]							)      ->Dict:
									snake_case_   =	'''x = 3'''
									snake_case_   =	{}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									assert result == 3
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3}							)







			def 				snake_case__(     self :				Optional[int]							)      ->Optional[int]:
									snake_case_   =	'''test_dict = {\'x\': x, \'y\': add_two(x)}'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 5}							)
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}							)







			def 				snake_case__(     self :				Dict							)      ->str:
									snake_case_   =	'''x = 3\ny = 5'''
									snake_case_   =	{}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 5}							)







			def 				snake_case__(     self :				str							)      ->Tuple:
									snake_case_   =	'''text = f\'This is x: {x}.\''''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == "This is x: 3."
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''text''': '''This is x: 3.'''}							)







			def 				snake_case__(     self :				Optional[Any]							)      ->List[str]:
									snake_case_   =	'''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == 2
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 2}							)

									snake_case_   =	{'''x''': 8}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 8, '''y''': 5}							)







			def 				snake_case__(     self :				str							)      ->str:
									snake_case_   =	'''test_list = [x, add_two(x)]'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									self.assertListEqual(_UpperCamelCase							,   [3, 5]							)
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''test_list''': [3, 5]}							)







			def 				snake_case__(     self :				Any							)      ->List[Any]:
									snake_case_   =	'''y = x'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									assert result == 3
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 3}							)







			def 				snake_case__(     self :				Optional[int]							)      ->Dict:
									snake_case_   =	'''test_list = [x, add_two(x)]\ntest_list[1]'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''test_list''': [3, 5]}							)

									snake_case_   =	'''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}							)







			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	'''x = 0\nfor i in range(3):\n    x = i'''
									snake_case_   =	{}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''range''': range}							,   state=_UpperCamelCase							)
									assert result == 2
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 2, '''i''': 2}							)