






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


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

transformers_logging.set_verbosity_info()




def   lowercase__   (  __snake_case					:							str						):


					'''simple docstring'''


					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   lowercase__   (  __snake_case					:							List[str]  , __snake_case					:							str  , __snake_case					:							Dict						):


					'''simple docstring'''


					return max(metric_fn(__snake_case  , __snake_case						) for gt in ground_truths						)




def   lowercase__   (  __snake_case					:							Dict  , __snake_case					:							int  , __snake_case					:							str						):


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		[line.strip() for line in open(__snake_case  , 'r'						).readlines()]
					UpperCAmelCase_					:   str   =		[]

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

					UpperCAmelCase_					:   List[Any]   =		0
					for prediction, ground_truths in zip(__snake_case  , __snake_case						):
										total += 1
										em += metric_max_over_ground_truths(__snake_case  , __snake_case  , __snake_case						)
										fa += metric_max_over_ground_truths(__snake_case  , __snake_case  , __snake_case						)

					UpperCAmelCase_					:   Any   =		100.0 * em / total
					UpperCAmelCase_					:   Any   =		100.0 * fa / total

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




def   lowercase__   (  __snake_case					:							str  , __snake_case					:							int  , __snake_case					:							Optional[Any]						):


					'''simple docstring'''


					UpperCAmelCase_					:   List[Any]   =		args.k
					UpperCAmelCase_					:   int   =		[line.strip() for line in open(__snake_case  , 'r'						).readlines()]
					UpperCAmelCase_					:   Optional[Any]   =		[line.strip() for line in open(__snake_case  , 'r'						).readlines()]

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

					UpperCAmelCase_					:   Union[str, Any]   =		100.0 * em / total
					logger.info(F"Precision@{k}: {em: .2f}"						)




def   lowercase__   (  __snake_case					:							List[Any]  , __snake_case					:							int  , __snake_case					:							List[Any]						):


					'''simple docstring'''


					def strip_title(__snake_case					:							Optional[Any]						):
										if title.startswith('"'						):
															UpperCAmelCase_					:   Union[str, Any]   =		title[1:]
										if title.endswith('"'						):
															UpperCAmelCase_					:   Union[str, Any]   =		title[:-1]
										return title

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

					UpperCAmelCase_					:   List[str]   =		rag_model.rag.question_encoder(__snake_case						)
					UpperCAmelCase_					:   List[str]   =		question_enc_outputs[0]

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




def   lowercase__   (  __snake_case					:							Union[str, Any]  , __snake_case					:							Optional[int]  , __snake_case					:							List[str]						):


					'''simple docstring'''


					with torch.no_grad():
										UpperCAmelCase_					:   Dict   =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    __snake_case  , return_tensors='pt'  , padding=__snake_case  , truncation=__snake_case						)

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

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

										return answers




def   lowercase__   (  ):


					'''simple docstring'''


					UpperCAmelCase_					:   Union[str, Any]   =		argparse.ArgumentParser()
					parser.add_argument(
					    '--model_type'  , choices=['rag_sequence', 'rag_token', 'bart']  , type=__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=__snake_case  , choices=['exact', 'compressed', 'legacy']  , type=__snake_case  , help='RAG model retriever type'  , )
					parser.add_argument(
					    '--index_path'  , default=__snake_case  , type=__snake_case  , help='Path to the retrieval index'  , )
					parser.add_argument('--n_docs'  , default=5  , type=__snake_case  , help='Number of retrieved docs'						)
					parser.add_argument(
					    '--model_name_or_path'  , default=__snake_case  , type=__snake_case  , required=__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=__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=__snake_case  , help='k for the precision@k calculation'						)
					parser.add_argument(
					    '--evaluation_set'  , default=__snake_case  , type=__snake_case  , required=__snake_case  , help='Path to a file containing evaluation samples'  , )
					parser.add_argument(
					    '--gold_data_path'  , default=__snake_case  , type=__snake_case  , required=__snake_case  , help='Path to a tab-separated file with gold samples'  , )
					parser.add_argument(
					    '--gold_data_mode'  , default='qa'  , type=__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=__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=__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=__snake_case  , help='Number of beams to be used when generating answers'  , )
					parser.add_argument('--min_length'  , default=1  , type=__snake_case  , help='Min length of the generated answers'						)
					parser.add_argument('--max_length'  , default=50  , type=__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.'  , )
					UpperCAmelCase_					:   Optional[Any]   =		parser.parse_args()
					UpperCAmelCase_					:   Any   =		torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)
					return args




def   lowercase__   (  __snake_case					:							List[Any]						):


					'''simple docstring'''


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

					UpperCAmelCase_					:   Optional[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'  , __snake_case						)

					UpperCAmelCase_					:   str   =		get_scores if args.eval_mode == 'e2e' else get_precision_at_k
					UpperCAmelCase_					:   Union[str, 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(__snake_case  , args.predictions_path  , args.gold_data_path						)
															continue

										logger.info('***** Running evaluation for {} *****'.format(__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'						):
															UpperCAmelCase_					:   Tuple   =		RagRetriever.from_pretrained(__snake_case  , **__snake_case						)
															UpperCAmelCase_					:   str   =		model_class.from_pretrained(__snake_case  , retriever=__snake_case  , **__snake_case						)
															model.retriever.init_retrieval()
										else:
															UpperCAmelCase_					:   Union[str, Any]   =		model_class.from_pretrained(__snake_case  , **__snake_case						)
										model.to(args.device						)

										with open(args.evaluation_set  , 'r'						) as eval_file, open(args.predictions_path  , 'w'						) as preds_file:
															UpperCAmelCase_					:   Union[str, Any]   =		[]
															for line in tqdm(__snake_case						):
																				questions.append(line.strip()						)
																				if len(__snake_case						) == args.eval_batch_size:
																									UpperCAmelCase_					:   List[Any]   =		evaluate_batch_fn(__snake_case  , __snake_case  , __snake_case						)
																									preds_file.write('\n'.join(__snake_case						) + '\n'						)
																									preds_file.flush()
																									UpperCAmelCase_					:   Optional[int]   =		[]
															if len(__snake_case						) > 0:
																				UpperCAmelCase_					:   Dict   =		evaluate_batch_fn(__snake_case  , __snake_case  , __snake_case						)
																				preds_file.write('\n'.join(__snake_case						)						)
																				preds_file.flush()

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


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














def   lowercase__   (  __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int						):


					'''simple docstring'''


					return len_board * len_board_column * row + column




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							set[int]  , ):


					'''simple docstring'''


					if board[row][column] != word[word_index]:
										return False

					if word_index == len(__snake_case						) - 1:
										return True

					UpperCAmelCase_					:   Dict   =		[(0, 1), (0, -1), (-1, 0), (1, 0)]
					UpperCAmelCase_					:   Union[str, Any]   =		len(__snake_case						)
					UpperCAmelCase_					:   List[Any]   =		len(board[0]						)
					for direction in traverts_directions:
										UpperCAmelCase_					:   Optional[int]   =		row + direction[0]
										UpperCAmelCase_					:   Optional[int]   =		column + direction[1]
										if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
															continue

										UpperCAmelCase_					:   Tuple   =		get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)
										if key in visited_points_set:
															continue

										visited_points_set.add(__snake_case						)
										if exits_word(__snake_case  , __snake_case  , __snake_case  , __snake_case  , word_index + 1  , __snake_case						):
															return True

										visited_points_set.remove(__snake_case						)

					return False




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str						):


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		(
					    'The board should be a non empty matrix of single chars strings.'
					)

					UpperCAmelCase_					:   Optional[Any]   =		len(__snake_case						)
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(__snake_case						)

					for row in board:
										if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
															raise ValueError(__snake_case						)

										for item in row:
															if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) != 1:
																				raise ValueError(__snake_case						)

    # Validate word
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(
										    'The word parameter should be a string of length greater than 0.'						)

					UpperCAmelCase_					:   Any   =		len(board[0]						)
					for i in range(__snake_case						):
										for j in range(__snake_case						):
															if exits_word(
															    __snake_case  , __snake_case  , __snake_case  , __snake_case  , 0  , {get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)}						):
																				return True

					return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()






