

"""simple docstring"""





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


__UpperCamelCase  :      str							      =		logging.getLogger(__name__)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "sequence-classification"







					def __init__(       self			:				List[Any]   ,lowercase_			:				List[str]		):
						if type(lowercase_		) == dict:
							lowerCAmelCase__	:						int  				=	Namespace(**lowercase_		)
						lowerCAmelCase__	:						int  				=	glue_output_modes[hparams.task]
						lowerCAmelCase__	:						List[Any]  				=	glue_tasks_num_labels[hparams.task]

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







					def        __lowerCAmelCase (       self			:				Dict   ,**lowercase_			:				Tuple		):
						return self.model(**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				Tuple   ,lowercase_			:				Dict		):
						lowerCAmelCase__	:						List[Any]  				=	{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

						lowerCAmelCase__	:						Optional[int]  				=	self(**lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	outputs[0]

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







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Tuple  				=	self.hparams
						lowerCAmelCase__	:						int  				=	processors[args.task]()
						lowerCAmelCase__	:						int  				=	processor.get_labels()

						for mode in ["train", "dev"]:
							lowerCAmelCase__	:						int  				=	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		)
								lowerCAmelCase__	:						int  				=	(
								    processor.get_dev_examples(args.data_dir		)
								    if mode == '''dev'''
								    else processor.get_train_examples(args.data_dir		)
								)
								lowerCAmelCase__	:						Optional[int]  				=	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        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				str   ,lowercase_			:				int   ,lowercase_			:				bool = False		):
						lowerCAmelCase__	:						Tuple  				=	'''dev''' if mode == '''test''' else mode

						lowerCAmelCase__	:						Tuple  				=	self._feature_file(lowercase_		)
						logger.info('''Loading features from cached file %s'''   ,lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.load(lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	torch.tensor([f.input_ids for f in features]   ,dtype=torch.long		)
						lowerCAmelCase__	:						Dict  				=	torch.tensor([f.attention_mask for f in features]   ,dtype=torch.long		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([f.token_type_ids for f in features]   ,dtype=torch.long		)
						if self.hparams.glue_output_mode == "classification":
							lowerCAmelCase__	:						Optional[Any]  				=	torch.tensor([f.label for f in features]   ,dtype=torch.long		)
						elif self.hparams.glue_output_mode == "regression":
							lowerCAmelCase__	:						List[Any]  				=	torch.tensor([f.label for f in features]   ,dtype=torch.float		)

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







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				str   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						Optional[int]  				=	{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

						lowerCAmelCase__	:						int  				=	self(**lowercase_		)
						lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	outputs[:2]
						lowerCAmelCase__	:						Optional[Any]  				=	logits.detach().cpu().numpy()
						lowerCAmelCase__	:						Union[str, Any]  				=	inputs['''labels'''].detach().cpu().numpy()

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







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				str		):
						lowerCAmelCase__	:						int  				=	torch.stack([x['''val_loss'''] for x in outputs]		).mean().detach().cpu().item()
						lowerCAmelCase__	:						Any  				=	np.concatenate([x['''pred'''] for x in outputs]   ,axis=0		)

						if self.hparams.glue_output_mode == "classification":
							lowerCAmelCase__	:						Any  				=	np.argmax(lowercase_   ,axis=1		)
						elif self.hparams.glue_output_mode == "regression":
							lowerCAmelCase__	:						Optional[Any]  				=	np.squeeze(lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	np.concatenate([x['''target'''] for x in outputs]   ,axis=0		)
						lowerCAmelCase__	:						Tuple  				=	[[] for _ in range(out_label_ids.shape[0]		)]
						lowerCAmelCase__	:						str  				=	[[] for _ in range(out_label_ids.shape[0]		)]

						lowerCAmelCase__	:						Any  				=	{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task   ,lowercase_   ,lowercase_		)}

						lowerCAmelCase__	:						Optional[int]  				=	dict(results.items()		)
						lowerCAmelCase__	:						Any  				=	results
						return ret, preds_list, out_label_list







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				list		):
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	self._eval_end(lowercase_		)
						lowerCAmelCase__	:						str  				=	ret['''log''']
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				Tuple		):
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	self._eval_end(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	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        __lowerCAmelCase (       lowercase_			:				Dict   ,lowercase_			:				int		):
						BaseTransformer.add_model_specific_args(lowercase_   ,lowercase_		)
						parser.add_argument(
						    '''--max_seq_length'''   ,default=1_2_8   ,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   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						str  				=	argparse.ArgumentParser()
	add_generic_args(A_						,     os.getcwd() )
	lowerCAmelCase__	:						int  				=	GLUETransformer.add_model_specific_args(A_						,     os.getcwd() )
	lowerCAmelCase__	:						Tuple  				=	parser.parse_args()

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

	lowerCAmelCase__	:						Tuple  				=	GLUETransformer(A_ )
	lowerCAmelCase__	:						Tuple  				=	generic_train(A_						,     A_ )

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


if __name__ == "__main__":
							main()







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''RagModel''',
							    '''RagPreTrainedModel''',
							    '''RagSequenceForGeneration''',
							    '''RagTokenForGeneration''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Union[str, Any]							      =		[
							    '''TFRagModel''',
							    '''TFRagPreTrainedModel''',
							    '''TFRagSequenceForGeneration''',
							    '''TFRagTokenForGeneration''',
							]


if TYPE_CHECKING:
							from .configuration_rag import RagConfig
							from .retrieval_rag import RagRetriever
							from .tokenization_rag import RagTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rag import (
														    TFRagModel,
														    TFRagPreTrainedModel,
														    TFRagSequenceForGeneration,
														    TFRagTokenForGeneration,
														)

else:
							import sys

							__UpperCamelCase  :      Dict							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




