code: 
  
  
  
  
  
  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)
  
style_context: 
  
  
  
  
  
  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()
  