
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	:    int          =      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def 	a_		(	__lowercase       :     List[str]			)			->  Tuple:
			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_		(	__lowercase       :     List[Any]	, __lowercase       :     Dict	, __lowercase       :     Tuple			)			->  str:
			return max(metric_fn(__lowercase	, __lowercase			) for gt in ground_truths			)






def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Optional[Any]	, __lowercase       :     Tuple			)			->  Dict:
			_snake_case          =      [line.strip() for line in open(__lowercase	, 'r'			).readlines()]
			_snake_case          =      []

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

			_snake_case          =      _snake_case          =      _snake_case          =      0
			for prediction, ground_truths in zip(__lowercase	, __lowercase			):
						total += 1
						em += metric_max_over_ground_truths(__lowercase	, __lowercase	, __lowercase			)
						fa += metric_max_over_ground_truths(__lowercase	, __lowercase	, __lowercase			)

			_snake_case          =      1_0_0.0 * em / total
			_snake_case          =      1_0_0.0 * fa / total

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






def 	a_		(	__lowercase       :     Union[str, Any]	, __lowercase       :     List[Any]	, __lowercase       :     List[Any]			)			->  List[str]:
			_snake_case          =      args.k
			_snake_case          =      [line.strip() for line in open(__lowercase	, 'r'			).readlines()]
			_snake_case          =      [line.strip() for line in open(__lowercase	, 'r'			).readlines()]

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

			_snake_case          =      1_0_0.0 * em / total
			logger.info(f'''Precision@{k}: {em: .2f}'''			)






def 	a_		(	__lowercase       :     List[str]	, __lowercase       :     Union[str, Any]	, __lowercase       :     Dict			)			->  str:
			def strip_title(__lowercase       :     Tuple			):
						if title.startswith('"'			):
									_snake_case          =      title[1:]
						if title.endswith('"'			):
									_snake_case          =      title[:-1]
						return title

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

			_snake_case          =      rag_model.rag.question_encoder(__lowercase			)
			_snake_case          =      question_enc_outputs[0]

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






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     int	, __lowercase       :     Union[str, Any]			)			->  Optional[int]:
			with torch.no_grad():
						_snake_case          =      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    __lowercase	, return_tensors='pt'	, padding=__lowercase	, truncation=__lowercase			)

						_snake_case          =      inputs_dict.input_ids.to(args.device			)
						_snake_case          =      inputs_dict.attention_mask.to(args.device			)
						_snake_case          =      rag_model.generate(  # rag_model overwrites generate
						    __lowercase	, attention_mask=__lowercase	, num_beams=args.num_beams	, min_length=args.min_length	, max_length=args.max_length	, early_stopping=__lowercase	, num_return_sequences=1	, bad_words_ids=[[0, 0]]	, )
						_snake_case          =      rag_model.retriever.generator_tokenizer.batch_decode(__lowercase	, skip_special_tokens=__lowercase			)

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

						return answers






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






def 	a_		(	__lowercase       :     List[Any]			)			->  str:
			_snake_case          =      {}
			if args.model_type is None:
						_snake_case          =      infer_model_type(args.model_name_or_path			)
						assert args.model_type is not None
			if args.model_type.startswith('rag'			):
						_snake_case          =      RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
						_snake_case          =      args.n_docs
						if args.index_name is not None:
									_snake_case          =      args.index_name
						if args.index_path is not None:
									_snake_case          =      args.index_path
			else:
						_snake_case          =      BartForConditionalGeneration

			_snake_case          =      (
			    [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'	, __lowercase			)

			_snake_case          =      get_scores if args.eval_mode == 'e2e' else get_precision_at_k
			_snake_case          =      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(__lowercase	, args.predictions_path	, args.gold_data_path			)
									continue

						logger.info('***** Running evaluation for {} *****'.format(__lowercase			)			)
						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'			):
									_snake_case          =      RagRetriever.from_pretrained(__lowercase	, **__lowercase			)
									_snake_case          =      model_class.from_pretrained(__lowercase	, retriever=__lowercase	, **__lowercase			)
									model.retriever.init_retrieval()
						else:
									_snake_case          =      model_class.from_pretrained(__lowercase	, **__lowercase			)
						model.to(args.device			)

						with open(args.evaluation_set	, 'r'			) as eval_file, open(args.predictions_path	, 'w'			) as preds_file:
									_snake_case          =      []
									for line in tqdm(__lowercase			):
												questions.append(line.strip()			)
												if len(__lowercase			) == args.eval_batch_size:
															_snake_case          =      evaluate_batch_fn(__lowercase	, __lowercase	, __lowercase			)
															preds_file.write('\n'.join(__lowercase			) + '\n'			)
															preds_file.flush()
															_snake_case          =      []
									if len(__lowercase			) > 0:
												_snake_case          =      evaluate_batch_fn(__lowercase	, __lowercase	, __lowercase			)
												preds_file.write('\n'.join(__lowercase			)			)
												preds_file.flush()

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


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      get_args()
			main(args)

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


_lowerCamelCase	:    str          =      logging.getLogger(__name__)




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "sequence-classification"



	def __init__(  self     :   Optional[int]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				if type(lowercase     ) == dict:
							_snake_case          =      Namespace(**lowercase     )
				_snake_case          =      glue_output_modes[hparams.task]
				_snake_case          =      glue_tasks_num_labels[hparams.task]

				super().__init__(lowercase      ,  lowercase      ,  self.mode     )



	def 			A							(  self     :   List[str]      ,  **lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				return self.model(**lowercase     )



	def 			A							(  self     :   Tuple      ,  lowercase     :   Optional[Any]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

				if self.config.model_type not in ["distilbert", "bart"]:
							_snake_case          =      batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

				_snake_case          =      self(**lowercase     )
				_snake_case          =      outputs[0]

				_snake_case          =      self.trainer.lr_schedulers[0]['scheduler']
				_snake_case          =      {'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
				return {"loss": loss, "log": tensorboard_logs}



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.hparams
				_snake_case          =      processors[args.task]()
				_snake_case          =      processor.get_labels()

				for mode in ["train", "dev"]:
							_snake_case          =      self._feature_file(lowercase     )
							if os.path.exists(lowercase     ) and not args.overwrite_cache:
										logger.info('Loading features from cached file %s'      ,  lowercase     )
							else:
										logger.info('Creating features from dataset file at %s'      ,  args.data_dir     )
										_snake_case          =      (
										    processor.get_dev_examples(args.data_dir     )
										    if mode == 'dev'
										    else processor.get_train_examples(args.data_dir     )
										)
										_snake_case          =      convert_examples_to_features(
										    lowercase      ,  self.tokenizer      ,  max_length=args.max_seq_length      ,  label_list=self.labels      ,  output_mode=args.glue_output_mode      ,  )
										logger.info('Saving features into cached file %s'      ,  lowercase     )
										torch.save(lowercase      ,  lowercase     )



	def 			A							(  self     :   List[str]      ,  lowercase     :   str      ,  lowercase     :   int      ,  lowercase     :   bool = False     ):


				'''simple docstring'''


				_snake_case          =      'dev' if mode == 'test' else mode

				_snake_case          =      self._feature_file(lowercase     )
				logger.info('Loading features from cached file %s'      ,  lowercase     )
				_snake_case          =      torch.load(lowercase     )
				_snake_case          =      torch.tensor([f.input_ids for f in features]      ,  dtype=torch.long     )
				_snake_case          =      torch.tensor([f.attention_mask for f in features]      ,  dtype=torch.long     )
				_snake_case          =      torch.tensor([f.token_type_ids for f in features]      ,  dtype=torch.long     )
				if self.hparams.glue_output_mode == "classification":
							_snake_case          =      torch.tensor([f.label for f in features]      ,  dtype=torch.long     )
				elif self.hparams.glue_output_mode == "regression":
							_snake_case          =      torch.tensor([f.label for f in features]      ,  dtype=torch.float     )

				return DataLoader(
				    TensorDataset(lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )      ,  batch_size=lowercase      ,  shuffle=lowercase      ,  )



	def 			A							(  self     :   str      ,  lowercase     :   Dict      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

				if self.config.model_type not in ["distilbert", "bart"]:
							_snake_case          =      batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

				_snake_case          =      self(**lowercase     )
				_snake_case     , _snake_case          =      outputs[:2]
				_snake_case          =      logits.detach().cpu().numpy()
				_snake_case          =      inputs['labels'].detach().cpu().numpy()

				return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      torch.stack([x['val_loss'] for x in outputs]     ).mean().detach().cpu().item()
				_snake_case          =      np.concatenate([x['pred'] for x in outputs]      ,  axis=0     )

				if self.hparams.glue_output_mode == "classification":
							_snake_case          =      np.argmax(lowercase      ,  axis=1     )
				elif self.hparams.glue_output_mode == "regression":
							_snake_case          =      np.squeeze(lowercase     )

				_snake_case          =      np.concatenate([x['target'] for x in outputs]      ,  axis=0     )
				_snake_case          =      [[] for _ in range(out_label_ids.shape[0]     )]
				_snake_case          =      [[] for _ in range(out_label_ids.shape[0]     )]

				_snake_case          =      {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task      ,  lowercase      ,  lowercase     )}

				_snake_case          =      dict(results.items()     )
				_snake_case          =      results
				return ret, preds_list, out_label_list



	def 			A							(  self     :   List[str]      ,  lowercase     :   list     ):


				'''simple docstring'''


				_snake_case     , _snake_case     , _snake_case          =      self._eval_end(lowercase     )
				_snake_case          =      ret['log']
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



	def 			A							(  self     :   Tuple      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				_snake_case     , _snake_case     , _snake_case          =      self._eval_end(lowercase     )
				_snake_case          =      ret['log']
				# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
				return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	@staticmethod
	def 			A							(  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				BaseTransformer.add_model_specific_args(lowercase      ,  lowercase     )
				parser.add_argument(
				    '--max_seq_length'      ,  default=128      ,  type=lowercase      ,  help=(
				        'The maximum total input sequence length after tokenization. Sequences longer '
				        'than this will be truncated, sequences shorter will be padded.'
				    )      ,  )

				parser.add_argument(
				    '--task'      ,  default=''      ,  type=lowercase      ,  required=lowercase      ,  help='The GLUE task to run'      ,  )
				parser.add_argument(
				    '--gpus'      ,  default=0      ,  type=lowercase      ,  help='The number of GPUs allocated for this, it is by default 0 meaning none'      ,  )

				parser.add_argument(
				    '--overwrite_cache'      ,  action='store_true'      ,  help='Overwrite the cached training and evaluation sets'     )

				return parser






def 	a_		(	)			->  List[str]:
			_snake_case          =      argparse.ArgumentParser()
			add_generic_args(__lowercase	, os.getcwd()			)
			_snake_case          =      GLUETransformer.add_model_specific_args(__lowercase	, os.getcwd()			)
			_snake_case          =      parser.parse_args()

			# If output_dir not provided, a folder will be generated in pwd
			if args.output_dir is None:
						_snake_case          =      os.path.join(
						    './results'	, f'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'			)}'''	, )
						os.makedirs(args.output_dir			)

			_snake_case          =      GLUETransformer(__lowercase			)
			_snake_case          =      generic_train(__lowercase	, __lowercase			)

			# Optionally, predict on dev set and write to output_dir
			if args.do_predict:
						_snake_case          =      sorted(glob.glob(os.path.join(args.output_dir	, 'checkpoint-epoch=*.ckpt'			)	, recursive=__lowercase			)			)
						_snake_case          =      model.load_from_checkpoint(checkpoints[-1]			)
						return trainer.test(__lowercase			)


if __name__ == "__main__":
			main()