



'''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


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

transformers_logging.set_verbosity_info()



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   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   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Any:
		return max(metric_fn(lowerCAmelCase_		,    lowerCAmelCase_						) for gt in ground_truths						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Dict:
		_a  :   int				 =							[line.strip() for line in open(lowerCAmelCase_		,    'r'						).readlines()]
		_a  :   Union[str, Any]				 =							[]

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

		_a  :   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_						)

		_a  :   int				 =							100.0 * em / total
		_a  :   Dict				 =							100.0 * fa / total

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



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Dict:
		_a  :   Union[str, Any]				 =							args.k
		_a  :   Union[str, Any]				 =							[line.strip() for line in open(lowerCAmelCase_		,    'r'						).readlines()]
		_a  :   List[str]				 =							[line.strip() for line in open(lowerCAmelCase_		,    'r'						).readlines()]

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

		_a  :   Tuple				 =							100.0 * em / total
		logger.info(f"""Precision@{k}: {em: .2f}"""						)



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

		_a  :   Union[str, Any]				 =							rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    lowerCAmelCase_		,    return_tensors='pt'		,    padding=lowerCAmelCase_		,    truncation=lowerCAmelCase_		,    )['input_ids'].to(args.device						)

		_a  :   str				 =							rag_model.rag.question_encoder(lowerCAmelCase_						)
		_a  :   List[Any]				 =							question_enc_outputs[0]

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



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Dict:
		with torch.no_grad():
				_a  :   Optional[int]				 =							rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowerCAmelCase_		,    return_tensors='pt'		,    padding=lowerCAmelCase_		,    truncation=lowerCAmelCase_						)

				_a  :   Union[str, Any]				 =							inputs_dict.input_ids.to(args.device						)
				_a  :   int				 =							inputs_dict.attention_mask.to(args.device						)
				_a  :   List[Any]				 =							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]]		,    )
				_a  :   List[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   __lowerCamelCase  (   )    ->   str:
		_a  :   List[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=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.'		,    )
		_a  :   int				 =							parser.parse_args()
		_a  :   Optional[Any]				 =							torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)
		return args



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Any:
		_a  :   Tuple				 =							{}
		if args.model_type is None:
				_a  :   Dict				 =							infer_model_type(args.model_name_or_path						)
				assert args.model_type is not None
		if args.model_type.startswith('rag'						):
				_a  :   Tuple				 =							RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
				_a  :   Any				 =							args.n_docs
				if args.index_name is not None:
						_a  :   Tuple				 =							args.index_name
				if args.index_path is not None:
						_a  :   str				 =							args.index_path
		else:
				_a  :   Any				 =							BartForConditionalGeneration

		_a  :   Any				 =							(
		    [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_						)

		_a  :   Optional[int]				 =							get_scores if args.eval_mode == 'e2e' else get_precision_at_k
		_a  :   List[str]				 =							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'						):
						_a  :   Any				 =							RagRetriever.from_pretrained(lowerCAmelCase_		,    **lowerCAmelCase_						)
						_a  :   Optional[Any]				 =							model_class.from_pretrained(lowerCAmelCase_		,    retriever=lowerCAmelCase_		,    **lowerCAmelCase_						)
						model.retriever.init_retrieval()
				else:
						_a  :   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:
						_a  :   Any				 =							[]
						for line in tqdm(lowerCAmelCase_						):
								questions.append(line.strip()						)
								if len(lowerCAmelCase_						) == args.eval_batch_size:
										_a  :   Union[str, Any]				 =							evaluate_batch_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)
										preds_file.write('\n'.join(lowerCAmelCase_						) + '\n'						)
										preds_file.flush()
										_a  :   Dict				 =							[]
						if len(lowerCAmelCase_						) > 0:
								_a  :   Dict				 =							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__":
							__lowerCAmelCase		    =     get_args()
							main(args)









'''simple docstring'''

from __future__ import annotations

class 			__magic_name__		:







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : int	):
									_a  :   Tuple				 =							value
									_a  :   Node | None				 =							None
									_a  :   Node | None				 =							None

class 			__magic_name__		:
							lowerCAmelCase :					int







							def __init__(							self      : Union[str, Any]	):
									_a  :   Tuple				 =							0







							def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int	):
									if node is None:
											return

									if path_sum == self.target:
											self.paths += 1

									if node.left:
											self.depth_first_search(node.left			,path_sum + node.left.value	)
									if node.right:
											self.depth_first_search(node.right			,path_sum + node.right.value	)







							def 							__lowercase    (							self      : Union[str, Any]			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int | None = None	):
									if node is None:
											return 0
									if target is not None:
											_a  :   Any				 =							target

									self.depth_first_search(_UpperCAmelCase			,node.value	)
									self.path_sum(node.left	)
									self.path_sum(node.right	)

									return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()




