






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			:	Optional[int]								=logging.getLogger(__name__)






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Dict     =   "sequence-classification"







		def __init__(					self       ,A__		):
						if type(A__		) == dict:
										_A	:				str   =      Namespace(**A__		)
						_A	:				Optional[int]   =      glue_output_modes[hparams.task]
						_A	:				Any   =      glue_tasks_num_labels[hparams.task]

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







		def  A__      (					self       ,**A__		):
						return self.model(**A__		)







		def  A__      (					self       ,A__       ,A__		):
						_A	:				Optional[int]   =      {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

						_A	:				int   =      self(**A__		)
						_A	:				int   =      outputs[0]

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







		def  A__      (					self		):
						_A	:				Any   =      self.hparams
						_A	:				int   =      processors[args.task]()
						_A	:				Union[str, Any]   =      processor.get_labels()

						for mode in ["train", "dev"]:
										_A	:				List[Any]   =      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	:				List[str]   =      (
														    processor.get_dev_examples(args.data_dir		)
														    if mode == '''dev'''
														    else processor.get_train_examples(args.data_dir		)
														)
														_A	:				Tuple   =      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  A__      (					self       ,A__       ,A__       ,A__ = False		):
						_A	:				Any   =      '''dev''' if mode == '''test''' else mode

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

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







		def  A__      (					self       ,A__       ,A__		):
						_A	:				Optional[Any]   =      {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

						_A	:				Dict   =      self(**A__		)
						_A  ,				_A	:				Tuple   =      outputs[:2]
						_A	:				Optional[int]   =      logits.detach().cpu().numpy()
						_A	:				Optional[Any]   =      inputs['''labels'''].detach().cpu().numpy()

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







		def  A__      (					self       ,A__		):
						_A	:				List[str]   =      torch.stack([x['''val_loss'''] for x in outputs]		).mean().detach().cpu().item()
						_A	:				Union[str, Any]   =      np.concatenate([x['''pred'''] for x in outputs]       ,axis=0		)

						if self.hparams.glue_output_mode == "classification":
										_A	:				Optional[int]   =      np.argmax(A__       ,axis=1		)
						elif self.hparams.glue_output_mode == "regression":
										_A	:				List[Any]   =      np.squeeze(A__		)

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

						_A	:				Tuple   =      {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task       ,A__       ,A__		)}

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







		def  A__      (					self       ,A__		):
						_A  ,				_A  ,				_A	:				List[str]   =      self._eval_end(A__		)
						_A	:				int   =      ret['''log''']
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







		def  A__      (					self       ,A__		):
						_A  ,				_A  ,				_A	:				List[str]   =      self._eval_end(A__		)
						_A	:				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  A__      (					A__       ,A__		):
						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  a__ ()		-> int:
				_A	:				Dict   =      argparse.ArgumentParser()
				add_generic_args(__lowercase					,  os.getcwd()   )
				_A	:				Any   =      GLUETransformer.add_model_specific_args(__lowercase					,  os.getcwd()   )
				_A	:				Optional[int]   =      parser.parse_args()

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

				_A	:				Optional[int]   =      GLUETransformer(__lowercase   )
				_A	:				str   =      generic_train(__lowercase					,  __lowercase   )

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


if __name__ == "__main__":
	main()











import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Dict								={
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Tuple     =   "sew"







		def __init__(					self       ,A__=32       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__=2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=0.1       ,A__=0.0       ,A__=0.1       ,A__=0.1       ,A__=0.02       ,A__=1E-5       ,A__="group"       ,A__="gelu"       ,A__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,A__=False       ,A__=128       ,A__=16       ,A__=True       ,A__=0.05       ,A__=10       ,A__=2       ,A__=0.0       ,A__=10       ,A__=0       ,A__="mean"       ,A__=False       ,A__=False       ,A__=256       ,A__=0       ,A__=1       ,A__=2       ,**A__       ,):
						super().__init__(**A__       ,pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__		)
						_A	:				str   =      hidden_size
						_A	:				Optional[Any]   =      feat_extract_norm
						_A	:				Any   =      feat_extract_activation
						_A	:				Optional[int]   =      list(A__		)
						_A	:				str   =      list(A__		)
						_A	:				Optional[Any]   =      list(A__		)
						_A	:				List[Any]   =      conv_bias
						_A	:				Optional[int]   =      num_conv_pos_embeddings
						_A	:				Any   =      num_conv_pos_embedding_groups
						_A	:				Union[str, Any]   =      len(self.conv_dim		)
						_A	:				List[Any]   =      num_hidden_layers
						_A	:				str   =      intermediate_size
						_A	:				List[Any]   =      squeeze_factor
						_A	:				Tuple   =      hidden_act
						_A	:				Union[str, Any]   =      num_attention_heads
						_A	:				Optional[int]   =      hidden_dropout
						_A	:				Optional[int]   =      attention_dropout
						_A	:				Optional[int]   =      activation_dropout
						_A	:				Tuple   =      feat_proj_dropout
						_A	:				Optional[Any]   =      final_dropout
						_A	:				str   =      layerdrop
						_A	:				int   =      layer_norm_eps
						_A	:				List[str]   =      initializer_range
						_A	:				List[Any]   =      vocab_size

						if (
						    (len(self.conv_stride		) != self.num_feat_extract_layers)
						    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
						    or (len(self.conv_dim		) != self.num_feat_extract_layers)
						):
										raise ValueError(
										    '''Configuration for convolutional layers is incorrect.'''
										    '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						_A	:				Union[str, Any]   =      apply_spec_augment
						_A	:				str   =      mask_time_prob
						_A	:				Any   =      mask_time_length
						_A	:				int   =      mask_time_min_masks
						_A	:				str   =      mask_feature_prob
						_A	:				Optional[Any]   =      mask_feature_length
						_A	:				Dict   =      mask_feature_min_masks

						# ctc loss
						_A	:				Optional[int]   =      ctc_loss_reduction
						_A	:				List[str]   =      ctc_zero_infinity

						# sequence classification
						_A	:				List[Any]   =      use_weighted_layer_sum
						_A	:				Optional[int]   =      classifier_proj_size







		@property
		def  A__      (					self		):
						return functools.reduce(operator.mul       ,self.conv_stride       ,1		)



