





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


_lowercase		:     List[str] 					=logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		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__	):
		return max(metric_fn(lowerCAmelCase__       ,lowerCAmelCase__	) for gt in ground_truths	)


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    int   	=			[line.strip() for line in open(lowerCAmelCase__       ,'r'	).readlines()]
		lowerCamelCase_  :    Optional[int]   	=			[]

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

		lowerCamelCase_  :    Tuple   	=			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_  :    Optional[Any]   	=			100.0 * em / total
		lowerCamelCase_  :    Optional[Any]   	=			100.0 * fa / total

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


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    List[str]   	=			args.k
		lowerCamelCase_  :    List[str]   	=			[line.strip() for line in open(lowerCAmelCase__       ,'r'	).readlines()]
		lowerCamelCase_  :    Union[str, Any]   	=			[line.strip() for line in open(lowerCAmelCase__       ,'r'	).readlines()]

		lowerCamelCase_  :    int   	=			0
		for hypo, reference in zip(lowerCAmelCase__       ,lowerCAmelCase__	):
				lowerCamelCase_  :    Optional[int]   	=			set(hypo.split('\t'	)[:k]	)
				lowerCamelCase_  :    Tuple   	=			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__	):
		def strip_title(lowerCAmelCase__	):
				if title.startswith('"'	):
						lowerCamelCase_  :    Any   	=			title[1:]
				if title.endswith('"'	):
						lowerCamelCase_  :    Dict   	=			title[:-1]
				return title

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

		lowerCamelCase_  :    List[str]   	=			rag_model.rag.question_encoder(lowerCAmelCase__	)
		lowerCamelCase_  :    str   	=			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_  :    List[Any]   	=			rag_model.retriever.index.get_doc_dicts(result.doc_ids	)
		lowerCamelCase_  :    Dict   	=			[]
		for docs in all_docs:
				lowerCamelCase_  :    Union[str, Any]   	=			[strip_title(lowerCAmelCase__	) for title in docs['title']]
				provenance_strings.append('\t'.join(lowerCAmelCase__	)	)
		return provenance_strings


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
		with torch.no_grad():
				lowerCamelCase_  :    Union[str, Any]   	=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowerCAmelCase__       ,return_tensors='pt'       ,padding=lowerCAmelCase__       ,truncation=lowerCAmelCase__	)

				lowerCamelCase_  :    int   	=			inputs_dict.input_ids.to(args.device	)
				lowerCamelCase_  :    Any   	=			inputs_dict.attention_mask.to(args.device	)
				lowerCamelCase_  :    int   	=			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_  :    Optional[Any]   	=			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    (   ):
		lowerCamelCase_  :    Tuple   	=			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=50       ,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_  :    Any   	=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)
		return args


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		lowerCamelCase_  :    Tuple   	=			{}
		if args.model_type is None:
				lowerCamelCase_  :    Optional[Any]   	=			infer_model_type(args.model_name_or_path	)
				assert args.model_type is not None
		if args.model_type.startswith('rag'	):
				lowerCamelCase_  :    Dict   	=			RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
				lowerCamelCase_  :    int   	=			args.n_docs
				if args.index_name is not None:
						lowerCamelCase_  :    Optional[int]   	=			args.index_name
				if args.index_path is not None:
						lowerCamelCase_  :    Dict   	=			args.index_path
		else:
				lowerCamelCase_  :    Union[str, Any]   	=			BartForConditionalGeneration

		lowerCamelCase_  :    List[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[Any]   	=			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_  :    str   	=			RagRetriever.from_pretrained(lowerCAmelCase__       ,**lowerCAmelCase__	)
						lowerCamelCase_  :    Dict   	=			model_class.from_pretrained(lowerCAmelCase__       ,retriever=lowerCAmelCase__       ,**lowerCAmelCase__	)
						model.retriever.init_retrieval()
				else:
						lowerCamelCase_  :    Dict   	=			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_  :    Any   	=			[]
						for line in tqdm(lowerCAmelCase__	):
								questions.append(line.strip()	)
								if len(lowerCAmelCase__	) == args.eval_batch_size:
										lowerCamelCase_  :    Union[str, Any]   	=			evaluate_batch_fn(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	)
										preds_file.write('\n'.join(lowerCAmelCase__	) + '\n'	)
										preds_file.flush()
										lowerCamelCase_  :    Tuple   	=			[]
						if len(lowerCAmelCase__	) > 0:
								lowerCamelCase_  :    Optional[Any]   	=			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__":
		_lowercase		:     Any 					=get_args()
		main(args)







import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:     Optional[Any] 					=[(1,)]


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    Union[str, Any]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow:
				assert True
		else:
				assert False, "should have been skipped"


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    List[str]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow and torch_device == "cuda":
				assert True
		else:
				assert False, "should have been skipped"






@require_torch
class 	UpperCamelCase_     (     unittest.TestCase       ):



					@slow
					@require_torch_gpu
					def      __a    (		self    :    Tuple	):
							check_slow_torch_cuda()



					@require_torch_gpu
					@slow
					def      __a    (		self    :    Optional[int]	):
							check_slow_torch_cuda()





					@parameterized.expand(lowerCamelCase	)
					@slow
					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Optional[Any]=None	):
							check_slow()


@slow
@require_torch_gpu
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@require_torch_gpu
@slow
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@slow
@pytest.mark.parametrize('param'       ,[1]	)
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()





@pytest.mark.parametrize('param'       ,[1]	)
@slow
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()
