


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


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

transformers_logging.set_verbosity_info()


def     lowercase	(    a ):



		'''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	(    a     , a     , a ):



		'''simple docstring'''




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


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Dict				     =						[line.strip() for line in open(a     , "r" ).readlines()]
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]

		if args.gold_data_mode == "qa":
				SCREAMING_SNAKE_CASE_				:int				     =						pd.read_csv(a     , sep="\t"     , header=a )
				for answer_list in data[1]:
						SCREAMING_SNAKE_CASE_				:Any				     =						ast.literal_eval(a )
						answers.append(a )
		else:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[line.strip() for line in open(a     , "r" ).readlines()]
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[[reference] for reference in references]

		SCREAMING_SNAKE_CASE_				:Dict				     =						0
		for prediction, ground_truths in zip(a     , a ):
				total += 1
				em += metric_max_over_ground_truths(a     , a     , a )
				fa += metric_max_over_ground_truths(a     , a     , a )

		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						100.0 * em / total
		SCREAMING_SNAKE_CASE_				:Any				     =						100.0 * fa / total

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


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						args.k
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[line.strip() for line in open(a     , "r" ).readlines()]
		SCREAMING_SNAKE_CASE_				:List[str]				     =						[line.strip() for line in open(a     , "r" ).readlines()]

		SCREAMING_SNAKE_CASE_				:List[str]				     =						0
		for hypo, reference in zip(a     , a ):
				SCREAMING_SNAKE_CASE_				:str				     =						set(hypo.split("\t" )[:k] )
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						set(reference.split("\t" ) )
				total += 1
				em += len(hypo_provenance & ref_provenance ) / k

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


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		def strip_title(a ):
				if title.startswith("\"" ):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						title[1:]
				if title.endswith("\"" ):
						SCREAMING_SNAKE_CASE_				:int				     =						title[:-1]
				return title

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    a     , return_tensors="pt"     , padding=a     , truncation=a     , )["input_ids"].to(args.device )

		SCREAMING_SNAKE_CASE_				:Any				     =						rag_model.rag.question_encoder(a )
		SCREAMING_SNAKE_CASE_				:int				     =						question_enc_outputs[0]

		SCREAMING_SNAKE_CASE_				:List[str]				     =						rag_model.retriever(
		    a     , 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"     , )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						rag_model.retriever.index.get_doc_dicts(result.doc_ids )
		SCREAMING_SNAKE_CASE_				:Dict				     =						[]
		for docs in all_docs:
				SCREAMING_SNAKE_CASE_				:Dict				     =						[strip_title(a ) for title in docs["title"]]
				provenance_strings.append("\t".join(a ) )
		return provenance_strings


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		with torch.no_grad():
				SCREAMING_SNAKE_CASE_				:Tuple				     =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    a     , return_tensors="pt"     , padding=a     , truncation=a )

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

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

				return answers


def     lowercase	(    ):



		'''simple docstring'''




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


def     lowercase	(    a ):



		'''simple docstring'''




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

		SCREAMING_SNAKE_CASE_				: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"     , a )

		SCREAMING_SNAKE_CASE_				:Tuple				     =						get_scores if args.eval_mode == "e2e" else get_precision_at_k
		SCREAMING_SNAKE_CASE_				: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(a     , args.predictions_path     , args.gold_data_path )
						continue

				logger.info("***** Running evaluation for {} *****".format(a ) )
				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" ):
						SCREAMING_SNAKE_CASE_				:int				     =						RagRetriever.from_pretrained(a     , **a )
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model_class.from_pretrained(a     , retriever=a     , **a )
						model.retriever.init_retrieval()
				else:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model_class.from_pretrained(a     , **a )
				model.to(args.device )

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

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


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










from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__     					=			Lock()


def     lowercase	(    a     , a     , a     , a     , a     , a     , a ):



		'''simple docstring'''




		global process_lock

		# we perform n swaps since after n swaps we know we are sorted
		# we *could* stop early if we are sorted already, but it takes as long to
		# find out we are sorted as it does to sort the list with this algorithm
		for i in range(0     , 10 ):
				if (i + position) % 2 == 0 and r_send is not None:
						# send your value to your right neighbor
						process_lock.acquire()
						r_send[1].send(a )
						process_lock.release()

						# receive your right neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:str				     =						rr_cv[0].recv()
						process_lock.release()

						# take the lower value since you are on the left
						SCREAMING_SNAKE_CASE_				:int				     =						min(a     , a )
				elif (i + position) % 2 != 0 and l_send is not None:
						# send your value to your left neighbor
						process_lock.acquire()
						l_send[1].send(a )
						process_lock.release()

						# receive your left neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:int				     =						lr_cv[0].recv()
						process_lock.release()

						# take the higher value since you are on the right
						SCREAMING_SNAKE_CASE_				:Dict				     =						max(a     , a )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(a )


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]
		# initialize the list of pipes where the values will be retrieved
		for _ in arr:
				result_pipe.append(Pipe() )
		# creates the processes
		# the first and last process only have one neighbor so they are made outside
		# of the loop
		SCREAMING_SNAKE_CASE_				:str				     =						Pipe()
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						Pipe()
		process_array_.append(
		    Process(
		        target=a     , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])     , ) )
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						temp_rs
		SCREAMING_SNAKE_CASE_				:Any				     =						temp_rr

		for i in range(1     , len(a ) - 1 ):
				SCREAMING_SNAKE_CASE_				:int				     =						Pipe()
				SCREAMING_SNAKE_CASE_				:Dict				     =						Pipe()
				process_array_.append(
				    Process(
				        target=a     , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])     , ) )
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp_rs
				SCREAMING_SNAKE_CASE_				:int				     =						temp_rr

		process_array_.append(
		    Process(
		        target=a     , args=(
		            len(a ) - 1,
		            arr[len(a ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(a ) - 1],
		        )     , ) )

		# start the processes
		for p in process_array_:
				p.start()

		# wait for the processes to end and write their values to the list
		for p in range(0     , len(a ) ):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						result_pipe[p][0].recv()
				process_array_[p].join()
		return arr


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[str]				     =						list(range(10     , 0     , -1 ) )
		print("Initial List" )
		print(*a )
		SCREAMING_SNAKE_CASE_				:int				     =						odd_even_transposition(a )
		print("Sorted List\n" )
		print(*a )


if __name__ == "__main__":
		main()






