






"""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


__a       :	Optional[Any]             = logging.getLogger(__name__)







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='sequence-classification'




							def __init__(   self:     Any				,		__A:     List[str]    ):

												'''simple docstring'''

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

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




							def  lowercase		(   self:     str				,		**__A:     Union[str, Any]    ):

												'''simple docstring'''

												return self.model(**__A    )




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

												'''simple docstring'''

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

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

												a__          =  self(**__A    )
												a__          =  outputs[0]

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




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

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

												for mode in ["train", "dev"]:
																	a__          =  self._feature_file(__A    )
																	if os.path.exists(__A    ) and not args.overwrite_cache:
																						logger.info('''Loading features from cached file %s'''				,		__A    )
																	else:
																						logger.info('''Creating features from dataset file at %s'''				,		args.data_dir    )
																						a__          =  (
																						    processor.get_dev_examples(args.data_dir    )
																						    if mode == '''dev'''
																						    else processor.get_train_examples(args.data_dir    )
																						)
																						a__          =  convert_examples_to_features(
																						    __A				,		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'''				,		__A    )
																						torch.save(__A				,		__A    )




							def  lowercase		(   self:     Union[str, Any]				,		__A:     str				,		__A:     int				,		__A:     bool = False    ):

												'''simple docstring'''

												a__          =  '''dev''' if mode == '''test''' else mode

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

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




							def  lowercase		(   self:     Union[str, Any]				,		__A:     int				,		__A:     List[Any]    ):

												'''simple docstring'''

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

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

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

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




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

												'''simple docstring'''

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

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

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

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

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




							def  lowercase		(   self:     Union[str, Any]				,		__A:     list    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     str				,		__A:     Any    ):

												'''simple docstring'''

												a__       ,a__       ,a__          =  self._eval_end(__A    )
												a__          =  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  lowercase		(   __A:     Optional[int]				,		__A:     Any    ):

												'''simple docstring'''

												BaseTransformer.add_model_specific_args(__A				,		__A    )
												parser.add_argument(
												    '''--max_seq_length'''				,		default=128				,		type=__A				,		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=__A				,		required=__A				,		help='''The GLUE task to run'''				,		)
												parser.add_argument(
												    '''--gpus'''				,		default=0				,		type=__A				,		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       (      ):
					a__          =  argparse.ArgumentParser()
					add_generic_args(lowerCamelCase_ ,     os.getcwd())
					a__          =  GLUETransformer.add_model_specific_args(lowerCamelCase_ ,     os.getcwd())
					a__          =  parser.parse_args()

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

					a__          =  GLUETransformer(lowerCamelCase_)
					a__          =  generic_train(lowerCamelCase_ ,     lowerCamelCase_)

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


if __name__ == "__main__":
	main()










"""simple docstring"""







from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='EncodecFeatureExtractor'
							_SCREAMING_SNAKE_CASE    =('T5Tokenizer', 'T5TokenizerFast')




							def __init__(   self:     List[Any]				,		__A:     Any				,		__A:     Dict    ):

												'''simple docstring'''

												super().__init__(__A				,		__A    )
												a__          =  self.feature_extractor
												a__          =  False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[Any]=None				,		__A:     Optional[Any]=None				,		__A:     List[Any]=True    ):

												'''simple docstring'''

												return self.tokenizer.get_decoder_prompt_ids(task=__A				,		language=__A				,		no_timestamps=__A    )




							def __call__(   self:     Union[str, Any]				,		*__A:     int				,		**__A:     Dict    ):

												'''simple docstring'''

												if self._in_target_context_manager:
																	return self.current_processor(*__A				,		**__A    )

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''sampling_rate'''				,		__A    )
												a__          =  kwargs.pop('''text'''				,		__A    )
												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

												if audio is None and text is None:
																	raise ValueError('''You need to specify either an `audio` or `text` input to process.'''    )

												if text is not None:
																	a__          =  self.tokenizer(__A				,		**__A    )

												if audio is not None:
																	a__          =  self.feature_extractor(__A				,		*__A				,		sampling_rate=__A				,		**__A    )

												if audio is None:
																	return inputs

												elif text is None:
																	return audio_inputs

												else:
																	a__          =  audio_inputs['''input_values''']
																	if "padding_mask" in audio_inputs:
																						a__          =  audio_inputs['''padding_mask''']
																	return inputs




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     List[str]				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''padding_mask'''				,		__A    )

												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

												if audio_values is not None:
																	return self._decode_audio(__A				,		padding_mask=__A    )
												else:
																	return self.tokenizer.batch_decode(*__A				,		**__A    )




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     Optional[int]				,		**__A:     Optional[Any]    ):

												'''simple docstring'''

												return self.tokenizer.decode(*__A				,		**__A    )





							def  lowercase		(   self:     Union[str, Any]				,		__A:     Dict				,		__A:     Optional = None    ):

												'''simple docstring'''

												a__          =  to_numpy(__A    )
												a__       ,a__       ,a__          =  audio_values.shape

												if padding_mask is None:
																	return list(__A    )

												a__          =  to_numpy(__A    )

												# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
												# token (so that the generated audio values are **not** treated as padded tokens)
												a__          =  seq_len - padding_mask.shape[-1]
												a__          =  1 - self.feature_extractor.padding_value
												a__          =  np.pad(__A				,		((0, 0), (0, difference))				,		'''constant'''				,		constant_values=__A    )

												a__          =  audio_values.tolist()
												for i in range(__A    ):
																	a__          =  np.asarray(audio_values[i]    )[
																	    padding_mask[i][None, :] != self.feature_extractor.padding_value
																	]
																	a__          =  sliced_audio.reshape(__A				,		-1    )

												return audio_values


