




"""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


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

transformers_logging.set_verbosity_info()







def   _snake_case    (       lowercase__	):
				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   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return max(metric_fn(lowercase__							,					lowercase__	) for gt in ground_truths	)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Dict					=					[line.strip() for line in open(lowercase__							,					'r'	).readlines()]
				_lowerCamelCase  :    Any					=					[]

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

				_lowerCamelCase  :    Optional[int]					=					0
				for prediction, ground_truths in zip(lowercase__							,					lowercase__	):
								total += 1
								em += metric_max_over_ground_truths(lowercase__							,					lowercase__							,					lowercase__	)
								fa += metric_max_over_ground_truths(lowercase__							,					lowercase__							,					lowercase__	)

				_lowerCamelCase  :    Optional[Any]					=					1_0_0.0 * em / total
				_lowerCamelCase  :    Optional[int]					=					1_0_0.0 * fa / total

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







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Optional[Any]					=					args.k
				_lowerCamelCase  :    Optional[int]					=					[line.strip() for line in open(lowercase__							,					'r'	).readlines()]
				_lowerCamelCase  :    Optional[int]					=					[line.strip() for line in open(lowercase__							,					'r'	).readlines()]

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

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







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				def strip_title(lowercase__	):
								if title.startswith('"'	):
												_lowerCamelCase  :    Union[str, Any]					=					title[1:]
								if title.endswith('"'	):
												_lowerCamelCase  :    List[str]					=					title[:-1]
								return title

				_lowerCamelCase  :    Tuple					=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowercase__							,					return_tensors='pt'							,					padding=lowercase__							,					truncation=lowercase__							,					)['input_ids'].to(args.device	)

				_lowerCamelCase  :    str					=					rag_model.rag.question_encoder(lowercase__	)
				_lowerCamelCase  :    Optional[Any]					=					question_enc_outputs[0]

				_lowerCamelCase  :    Dict					=					rag_model.retriever(
				    lowercase__							,					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  :    Optional[int]					=					rag_model.retriever.index.get_doc_dicts(result.doc_ids	)
				_lowerCamelCase  :    List[Any]					=					[]
				for docs in all_docs:
								_lowerCamelCase  :    Tuple					=					[strip_title(lowercase__	) for title in docs['title']]
								provenance_strings.append('\t'.join(lowercase__	)	)
				return provenance_strings







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				with torch.no_grad():
								_lowerCamelCase  :    List[Any]					=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    lowercase__							,					return_tensors='pt'							,					padding=lowercase__							,					truncation=lowercase__	)

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

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

								return answers







def   _snake_case    (       ):
				_lowerCamelCase  :    Union[str, Any]					=					argparse.ArgumentParser()
				parser.add_argument(
				    '--model_type'							,					choices=['rag_sequence', 'rag_token', 'bart']							,					type=lowercase__							,					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=lowercase__							,					choices=['exact', 'compressed', 'legacy']							,					type=lowercase__							,					help='RAG model retriever type'							,					)
				parser.add_argument(
				    '--index_path'							,					default=lowercase__							,					type=lowercase__							,					help='Path to the retrieval index'							,					)
				parser.add_argument('--n_docs'							,					default=5							,					type=lowercase__							,					help='Number of retrieved docs'	)
				parser.add_argument(
				    '--model_name_or_path'							,					default=lowercase__							,					type=lowercase__							,					required=lowercase__							,					help='Path to pretrained checkpoints or model identifier from huggingface.co/models'							,					)
				parser.add_argument(
				    '--eval_mode'							,					choices=['e2e', 'retrieval']							,					default='e2e'							,					type=lowercase__							,					help=(
				        'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
				        ' precision@k.'
				    )							,					)
				parser.add_argument('--k'							,					default=1							,					type=lowercase__							,					help='k for the precision@k calculation'	)
				parser.add_argument(
				    '--evaluation_set'							,					default=lowercase__							,					type=lowercase__							,					required=lowercase__							,					help='Path to a file containing evaluation samples'							,					)
				parser.add_argument(
				    '--gold_data_path'							,					default=lowercase__							,					type=lowercase__							,					required=lowercase__							,					help='Path to a tab-separated file with gold samples'							,					)
				parser.add_argument(
				    '--gold_data_mode'							,					default='qa'							,					type=lowercase__							,					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=lowercase__							,					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=lowercase__							,					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=lowercase__							,					help='Number of beams to be used when generating answers'							,					)
				parser.add_argument('--min_length'							,					default=1							,					type=lowercase__							,					help='Min length of the generated answers'	)
				parser.add_argument('--max_length'							,					default=50							,					type=lowercase__							,					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  :    Optional[Any]					=					parser.parse_args()
				_lowerCamelCase  :    Optional[Any]					=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)
				return args







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					{}
				if args.model_type is None:
								_lowerCamelCase  :    Dict					=					infer_model_type(args.model_name_or_path	)
								assert args.model_type is not None
				if args.model_type.startswith('rag'	):
								_lowerCamelCase  :    Any					=					RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
								_lowerCamelCase  :    List[str]					=					args.n_docs
								if args.index_name is not None:
												_lowerCamelCase  :    Tuple					=					args.index_name
								if args.index_path is not None:
												_lowerCamelCase  :    int					=					args.index_path
				else:
								_lowerCamelCase  :    str					=					BartForConditionalGeneration

				_lowerCamelCase  :    Dict					=					(
				    [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'							,					lowercase__	)

				_lowerCamelCase  :    List[Any]					=					get_scores if args.eval_mode == 'e2e' else get_precision_at_k
				_lowerCamelCase  :    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(lowercase__							,					args.predictions_path							,					args.gold_data_path	)
												continue

								logger.info('***** Running evaluation for {} *****'.format(lowercase__	)	)
								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  :    Optional[Any]					=					RagRetriever.from_pretrained(lowercase__							,					**lowercase__	)
												_lowerCamelCase  :    Union[str, Any]					=					model_class.from_pretrained(lowercase__							,					retriever=lowercase__							,					**lowercase__	)
												model.retriever.init_retrieval()
								else:
												_lowerCamelCase  :    str					=					model_class.from_pretrained(lowercase__							,					**lowercase__	)
								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(lowercase__	):
																questions.append(line.strip()	)
																if len(lowercase__	) == args.eval_batch_size:
																				_lowerCamelCase  :    Union[str, Any]					=					evaluate_batch_fn(lowercase__							,					lowercase__							,					lowercase__	)
																				preds_file.write('\n'.join(lowercase__	) + '\n'	)
																				preds_file.flush()
																				_lowerCamelCase  :    int					=					[]
												if len(lowercase__	) > 0:
																_lowerCamelCase  :    Dict					=					evaluate_batch_fn(lowercase__							,					lowercase__							,					lowercase__	)
																preds_file.write('\n'.join(lowercase__	)	)
																preds_file.flush()

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


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





"""simple docstring"""






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowercase__  		=		logging.get_logger(__name__)


if is_torch_available():
			import torch
			from torch import nn
			from torch.utils.data import Dataset

			from transformers import Trainer






			class lowerCAmelCase__			(  lowercase		):






						'''simple docstring'''



						def __init__(						self						,   lowercase = 101						):
										_lowerCamelCase  :    Optional[int]					=					length
						def __len__(						self						):
										return self.length






						def __getitem__(						self						,   lowercase						):
										return i






			class lowerCAmelCase__			:






						'''simple docstring'''



						def __call__(						self						,   lowercase						):
										return {"input_ids": torch.tensor(lowercase						), "labels": torch.tensor(lowercase						)}






			class lowerCAmelCase__			(  nn.Module		):






						'''simple docstring'''



						def __init__(						self						):
										super().__init__()
										# Add some (unused) params otherwise DDP will complain.
										_lowerCamelCase  :    int					=					nn.Linear(120						,   80						)






						def       A_					(						self						,   lowercase						,   lowercase=None						):
										if labels is not None:
														return torch.tensor(0.0						,   device=input_ids.device						), input_ids
										else:
														return input_ids






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@require_torch_neuroncore
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							_lowerCamelCase  :    int					=					self.get_auto_remove_tmp_dir()
							_lowerCamelCase  :    str					=					F'''--output_dir {output_dir}'''.split()
							_lowerCamelCase  :    Tuple					=					['torchrun'] + distributed_args + args
							execute_subprocess_async(lowercase						,   env=self.get_env()						)
							# successful return here == success - any errors would have caused an error in the sub-call






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@require_torch_multi_gpu
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							_lowerCamelCase  :    List[str]					=					self.get_auto_remove_tmp_dir()
							_lowerCamelCase  :    Any					=					F'''--output_dir {output_dir}'''.split()
							_lowerCamelCase  :    Any					=					['torchrun'] + distributed_args + args
							execute_subprocess_async(lowercase						,   env=self.get_env()						)
							# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
			# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
			#
			# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

			lowercase__  		=		HfArgumentParser((TrainingArguments,))
			lowercase__  		=		parser.parse_args_into_dataclasses()[0]

			logger.warning(
			    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
			    F"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
			)

			# Essentially, what we want to verify in the distributed case is that we get all samples back,
			# in the right order. (this is crucial for prediction for instance)
			for dataset_length in [101, 40, 7]:
						lowercase__  		=		DummyDataset(dataset_length)







						def   _snake_case    (       lowercase__	):
										_lowerCamelCase  :    int					=					list(range(len(lowercase__	)	)	)
										_lowerCamelCase  :    Optional[int]					=					p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
										if not success and training_args.local_rank == 0:
														logger.warning(
														    'Predictions and/or labels do not match expected results:\n  - predictions: '
														    f'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''	)
										return {"success": success}

						lowercase__  		=		Trainer(
						    model=DummyModel(),
						    args=training_args,
						    data_collator=DummyDataCollator(),
						    eval_dataset=dataset,
						    compute_metrics=compute_metrics,
						)
						lowercase__  		=		trainer.evaluate()
						logger.info(metrics)
						if metrics["eval_success"] is not True:
									logger.error(metrics)
									exit(1)

						lowercase__  		=		trainer.predict(dataset)
						logger.info(p.metrics)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics)
									exit(1)

						lowercase__  		=		2

						lowercase__  		=		trainer.evaluate()
						logger.info(metrics)
						if metrics["eval_success"] is not True:
									logger.error(metrics)
									exit(1)

						lowercase__  		=		trainer.predict(dataset)
						logger.info(p.metrics)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics)
									exit(1)
						lowercase__  		=		None