
"""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


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

transformers_logging.set_verbosity_info()






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):



	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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return max(metric_fn(__A			,							__A							) for gt in ground_truths							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   [line.strip() for line in open(__A			,							'r'							).readlines()]
	lowercase					  =   []

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

	lowercase					  =   lowercase					  =   lowercase					  =   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							)

	lowercase					  =   100.0 * em / total
	lowercase					  =   100.0 * fa / total

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   args.k
	lowercase					  =   [line.strip() for line in open(__A			,							'r'							).readlines()]
	lowercase					  =   [line.strip() for line in open(__A			,							'r'							).readlines()]

	lowercase					  =   lowercase					  =   0
	for hypo, reference in zip(__A			,							__A							):
		lowercase					  =   set(hypo.split('\t'							)[:k]							)
		lowercase					  =   set(reference.split('\t'							)							)
		total += 1
		em += len(hypo_provenance & ref_provenance							) / k

	lowercase					  =   100.0 * em / total
	logger.info(F'''Precision@{k}: {em: .2f}'''							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):



	def strip_title(__SCREAMING_SNAKE_CASE							):
		if title.startswith('"'							):
			lowercase					  =   title[1:]
		if title.endswith('"'							):
			lowercase					  =   title[:-1]
		return title

	lowercase					  =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    __A			,							return_tensors='pt'			,							padding=__A			,							truncation=__A			,							)['input_ids'].to(args.device							)

	lowercase					  =   rag_model.rag.question_encoder(__A							)
	lowercase					  =   question_enc_outputs[0]

	lowercase					  =   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'			,							)
	lowercase					  =   rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
	lowercase					  =   []
	for docs in all_docs:
		lowercase					  =   [strip_title(__A							) for title in docs['title']]
		provenance_strings.append('\t'.join(__A							)							)
	return provenance_strings






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):



	with torch.no_grad():
		lowercase					  =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    __A			,							return_tensors='pt'			,							padding=__A			,							truncation=__A							)

		lowercase					  =   inputs_dict.input_ids.to(args.device							)
		lowercase					  =   inputs_dict.attention_mask.to(args.device							)
		lowercase					  =   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]]			,							)
		lowercase					  =   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 							UpperCAmelCase_		(							):
	lowercase					  =   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.'			,							)
	lowercase					  =   parser.parse_args()
	lowercase					  =   torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)
	return args






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   {}
	if args.model_type is None:
		lowercase					  =   infer_model_type(args.model_name_or_path							)
		assert args.model_type is not None
	if args.model_type.startswith('rag'							):
		lowercase					  =   RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
		lowercase					  =   args.n_docs
		if args.index_name is not None:
			lowercase					  =   args.index_name
		if args.index_path is not None:
			lowercase					  =   args.index_path
	else:
		lowercase					  =   BartForConditionalGeneration

	lowercase					  =   (
	    [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							)

	lowercase					  =   get_scores if args.eval_mode == 'e2e' else get_precision_at_k
	lowercase					  =   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'							):
			lowercase					  =   RagRetriever.from_pretrained(__A			,							**__A							)
			lowercase					  =   model_class.from_pretrained(__A			,							retriever=__A			,							**__A							)
			model.retriever.init_retrieval()
		else:
			lowercase					  =   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:
			lowercase					  =   []
			for line in tqdm(__A							):
				questions.append(line.strip()							)
				if len(__A							) == args.eval_batch_size:
					lowercase					  =   evaluate_batch_fn(__A			,							__A			,							__A							)
					preds_file.write('\n'.join(__A							) + '\n'							)
					preds_file.flush()
					lowercase					  =   []
			if len(__A							) > 0:
				lowercase					  =   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__":
	UpperCAmelCase     				=			get_args()
	main(args)



# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


UpperCAmelCase     				=			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 	A_       (      __lowerCamelCase       ):




		'''simple docstring'''


		def __init__(	self		,   snake_case = 101	):
			lowercase					  =   length





		def __len__(	self	):
			return self.length





		def __getitem__(	self		,   snake_case	):
			return i







	class 	A_       :




		'''simple docstring'''


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







	class 	A_       (      nn.Module       ):




		'''simple docstring'''


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





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






def 							UpperCAmelCase_		(							):
	lowercase					  =   HfArgumentParser((TrainingArguments,)							)
	sys.argv += ["--output_dir", "./examples"]
	lowercase					  =   parser.parse_args_into_dataclasses()[0]

	logger.warning(
	    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
	    F'''tpu_num_cores: {training_args.tpu_num_cores}'''			,							)

	# 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 [1001, 256, 15]:
		lowercase					  =   DummyDataset(__SCREAMING_SNAKE_CASE							)

		def compute_metrics(__SCREAMING_SNAKE_CASE							) -> Dict:
			lowercase					  =   list(range(len(__SCREAMING_SNAKE_CASE							)							)							)
			lowercase					  =   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
			return {"success": success}

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

		lowercase					  =   trainer.predict(__SCREAMING_SNAKE_CASE							)
		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(__SCREAMING_SNAKE_CASE							)
		if metrics["eval_success"] is not True:
			logger.error(__SCREAMING_SNAKE_CASE							)
			exit(1							)

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

		lowercase					  =   None

	logger.info('🔥 All distributed tests successful'							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()

