





"""simple docstring"""






import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowerCamelCase__ :       Dict						     =			logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :Any		, *lowerCamelCase_    :Any		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Union[str, Any]=None		, lowerCamelCase_    :Optional[int]=None		, **lowerCamelCase_    :Dict					)							->			str:







			'''simple docstring'''




			super().__init__(*lowerCamelCase_		, **lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= eval_examples
			SCREAMING_SNAKE_CASE       :     Any				= post_process_function
			SCREAMING_SNAKE_CASE       :     Dict				= quant_trainer_args
			SCREAMING_SNAKE_CASE       :     List[Any]				= 1_28  # default number of calibration samples





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Dict=None					)							->			Optional[Any]:







			'''simple docstring'''




			if calib_dataset is None and self.calib_dataset is None:
					raise ValueError('''Trainer: calibration requires an calib_dataset.'''					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= calib_dataset if calib_dataset is not None else self.calib_dataset

			SCREAMING_SNAKE_CASE       :     Tuple				= self._remove_unused_columns(lowerCamelCase_		, description='''Calibration'''					)

			return DataLoader(
			    lowerCamelCase_		, batch_size=self.args.eval_batch_size		, collate_fn=self.data_collator		, drop_last=self.args.dataloader_drop_last		, num_workers=self.args.dataloader_num_workers		, pin_memory=self.args.dataloader_pin_memory		, shuffle=lowerCamelCase_		, )





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :int=None					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= self.train_dataset if calib_dataset is None else calib_dataset
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_calib_dataloader(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[str]				= self.model
			quant_trainer.configure_model(lowerCamelCase_		, self.quant_trainer_args		, calib=lowerCamelCase_					)
			model.eval()
			quant_trainer.enable_calibration(lowerCamelCase_					)

			logger.info('''***** Running calibration *****'''					)
			logger.info(f"  Num examples = {self.calib_num}"					)
			logger.info(f"  Batch size = {calib_dataloader.batch_size}"					)

			for step, inputs in enumerate(lowerCamelCase_					):
					# Prediction step
					SCREAMING_SNAKE_CASE       :     Dict				= self.prediction_step(lowerCamelCase_		, lowerCamelCase_		, prediction_loss_only=lowerCamelCase_					)
					if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
							break

			quant_trainer.finish_calibration(lowerCamelCase_		, self.quant_trainer_args					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :Tuple=None		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Any=None		, lowerCamelCase_    :str = "eval"					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.eval_dataset if eval_dataset is None else eval_dataset
			SCREAMING_SNAKE_CASE       :     Tuple				= self.get_eval_dataloader(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= self.eval_examples if eval_examples is None else eval_examples

			# Temporarily disable metric computation, we will do it in the loop here.
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.compute_metrics
			SCREAMING_SNAKE_CASE       :     List[str]				= None
			SCREAMING_SNAKE_CASE       :     Any				= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
					SCREAMING_SNAKE_CASE       :     Dict				= eval_loop(
					    lowerCamelCase_		, description='''Evaluation'''		, prediction_loss_only=True if compute_metrics is None else None		, ignore_keys=lowerCamelCase_		, )
			finally:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= compute_metrics

			if self.post_process_function is not None and self.compute_metrics is not None:
					SCREAMING_SNAKE_CASE       :     Tuple				= self.post_process_function(lowerCamelCase_		, lowerCamelCase_		, output.predictions					)
					SCREAMING_SNAKE_CASE       :     List[str]				= self.compute_metrics(lowerCamelCase_					)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()					):
							if not key.startswith(f"{metric_key_prefix}_"					):
									SCREAMING_SNAKE_CASE       :     List[Any]				= metrics.pop(lowerCamelCase_					)

					self.log(lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= {}

			if self.args.tpu_metrics_debug or self.args.debug:
					# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
					xm.master_print(met.metrics_report()					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= self.callback_handler.on_evaluate(self.args		, self.state		, self.control		, lowerCamelCase_					)
			return metrics





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Any=None		, lowerCamelCase_    :str = "test"					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_test_dataloader(lowerCamelCase_					)

			# Temporarily disable metric computation, we will do it in the loop here.
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.compute_metrics
			SCREAMING_SNAKE_CASE       :     int				= None
			SCREAMING_SNAKE_CASE       :     str				= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
					SCREAMING_SNAKE_CASE       :     int				= eval_loop(
					    lowerCamelCase_		, description='''Prediction'''		, prediction_loss_only=True if compute_metrics is None else None		, ignore_keys=lowerCamelCase_		, )
			finally:
					SCREAMING_SNAKE_CASE       :     int				= compute_metrics

			if self.post_process_function is None or self.compute_metrics is None:
					return output

			SCREAMING_SNAKE_CASE       :     int				= self.post_process_function(lowerCamelCase_		, lowerCamelCase_		, output.predictions		, '''predict'''					)
			SCREAMING_SNAKE_CASE       :     int				= self.compute_metrics(lowerCamelCase_					)

			# Prefix all keys with metric_key_prefix + '_'
			for key in list(metrics.keys()					):
					if not key.startswith(f"{metric_key_prefix}_"					):
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= metrics.pop(lowerCamelCase_					)

			return PredictionOutput(predictions=predictions.predictions		, label_ids=predictions.label_ids		, metrics=lowerCamelCase_					)







	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :Any="./"					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.eval_dataset
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_eval_dataloader(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= next(iter(lowerCamelCase_					)					)

			# saving device - to make it consistent
			SCREAMING_SNAKE_CASE       :     str				= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''					)

			# convert to tuple
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= tuple(v.to(lowerCamelCase_					) for k, v in batch.items()					)

			logger.info('''Converting model to be onnx compatible'''					)
			from pytorch_quantization.nn import TensorQuantizer

			SCREAMING_SNAKE_CASE       :     Dict				= True

			SCREAMING_SNAKE_CASE       :     Any				= self.model.to(lowerCamelCase_					)

			model.eval()
			model.float()

			SCREAMING_SNAKE_CASE       :     Tuple				= model.module if hasattr(lowerCamelCase_		, '''module'''					) else model
			quant_trainer.configure_model(lowerCamelCase_		, self.quant_trainer_args					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= os.path.join(lowerCamelCase_		, '''model.onnx'''					)
			logger.info(f"exporting model to {output_model_file}"					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= {0: '''batch_size''', 1: '''seq_len'''}

			torch.onnx.export(
			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, export_params=lowerCamelCase_		, opset_version=13		, do_constant_folding=lowerCamelCase_		, input_names=['''input_ids''', '''attention_mask''', '''token_type_ids''']		, output_names=['''output_start_logits''', '''output_end_logits''']		, dynamic_axes={
			        '''input_ids''': axes,
			        '''attention_mask''': axes,
			        '''token_type_ids''': axes,
			        '''output_start_logits''': axes,
			        '''output_end_logits''': axes,
			    }		, verbose=lowerCamelCase_		, )
			logger.info('''onnx export finished'''					)







"""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__ :       Optional[Any]						     =			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def __A						(   a_ :							Optional[int]     )->							Dict:




		'''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 __A						(   a_ :							List[Any]		,		a_ :							Optional[int]		,		a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






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





def __A						(   a_ :							List[Any]		,		a_ :							Union[str, Any]		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []

		if args.gold_data_mode == "qa":
				SCREAMING_SNAKE_CASE       :     List[Any]				= pd.read_csv(a_		,		sep='''\t'''		,		header=a_     )
				for answer_list in data[1]:
						SCREAMING_SNAKE_CASE       :     str				= ast.literal_eval(a_     )
						answers.append(a_     )
		else:
				SCREAMING_SNAKE_CASE       :     Any				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
				SCREAMING_SNAKE_CASE       :     Dict				= [[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       :     Any				= 100.0 * em / total
		SCREAMING_SNAKE_CASE       :     Optional[int]				= 100.0 * fa / total

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





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							List[Any]     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= args.k
		SCREAMING_SNAKE_CASE       :     Tuple				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]

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

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





def __A						(   a_ :							Any		,		a_ :							List[str]		,		a_ :							str     )->							int:




		'''simple docstring'''






		def strip_title(a_ :							Optional[Any]     ):
				if title.startswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Tuple				= title[1:]
				if title.endswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= title[:-1]
				return title

		SCREAMING_SNAKE_CASE       :     Tuple				= 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       :     Union[str, Any]				= question_enc_outputs[0]

		SCREAMING_SNAKE_CASE       :     Dict				= 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       :     Any				= rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
		SCREAMING_SNAKE_CASE       :     Dict				= []
		for docs in all_docs:
				SCREAMING_SNAKE_CASE       :     List[Any]				= [strip_title(a_     ) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(a_     )     )
		return provenance_strings





def __A						(   a_ :							List[Any]		,		a_ :							int		,		a_ :							str     )->							Tuple:




		'''simple docstring'''






		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     Optional[int]				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_     )

				SCREAMING_SNAKE_CASE       :     Dict				= inputs_dict.input_ids.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Any				= inputs_dict.attention_mask.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Tuple				= 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       :     Dict				= 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 __A						(   )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= 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       :     List[str]				= parser.parse_args()
		SCREAMING_SNAKE_CASE       :     Dict				= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''     )
		return args





def __A						(   a_ :							Optional[Any]     )->							int:




		'''simple docstring'''






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

		SCREAMING_SNAKE_CASE       :     Optional[int]				= (
		    [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       :     int				= get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
		SCREAMING_SNAKE_CASE       :     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(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       :     Dict				= 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       :     str				= 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       :     Dict				= []
						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       :     Union[str, Any]				= []
						if len(a_     ) > 0:
								SCREAMING_SNAKE_CASE       :     str				= 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__":
					lowerCamelCase__ :       List[str]						     =			get_args()
					main(args)





