
import functools
import operator

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


_lowerCamelCase	:    Optional[Any]          =      logging.get_logger(__name__)

_lowerCamelCase	:    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        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Dict          = "sew"



	def __init__(  self     :   int      ,  lowercase     :   List[Any]=32      ,  lowercase     :   Optional[Any]=768      ,  lowercase     :   Any=12      ,  lowercase     :   Tuple=12      ,  lowercase     :   Tuple=3_072      ,  lowercase     :   Tuple=2      ,  lowercase     :   Union[str, Any]="gelu"      ,  lowercase     :   Any=0.1      ,  lowercase     :   List[str]=0.1      ,  lowercase     :   List[str]=0.1      ,  lowercase     :   List[str]=0.0      ,  lowercase     :   Optional[int]=0.1      ,  lowercase     :   Optional[Any]=0.1      ,  lowercase     :   Optional[int]=0.02      ,  lowercase     :   Optional[Any]=1E-5      ,  lowercase     :   Dict="group"      ,  lowercase     :   Any="gelu"      ,  lowercase     :   Optional[int]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)      ,  lowercase     :   Any=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,  lowercase     :   Union[str, Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,  lowercase     :   Union[str, Any]=False      ,  lowercase     :   Tuple=128      ,  lowercase     :   Tuple=16      ,  lowercase     :   Union[str, Any]=True      ,  lowercase     :   str=0.05      ,  lowercase     :   int=10      ,  lowercase     :   List[str]=2      ,  lowercase     :   List[Any]=0.0      ,  lowercase     :   List[Any]=10      ,  lowercase     :   Dict=0      ,  lowercase     :   List[str]="mean"      ,  lowercase     :   List[Any]=False      ,  lowercase     :   str=False      ,  lowercase     :   Dict=256      ,  lowercase     :   List[str]=0      ,  lowercase     :   Union[str, Any]=1      ,  lowercase     :   Union[str, Any]=2      ,  **lowercase     :   str      ,  ):


				'''simple docstring'''


				super().__init__(**lowercase      ,  pad_token_id=lowercase      ,  bos_token_id=lowercase      ,  eos_token_id=lowercase     )
				_snake_case          =      hidden_size
				_snake_case          =      feat_extract_norm
				_snake_case          =      feat_extract_activation
				_snake_case          =      list(lowercase     )
				_snake_case          =      list(lowercase     )
				_snake_case          =      list(lowercase     )
				_snake_case          =      conv_bias
				_snake_case          =      num_conv_pos_embeddings
				_snake_case          =      num_conv_pos_embedding_groups
				_snake_case          =      len(self.conv_dim     )
				_snake_case          =      num_hidden_layers
				_snake_case          =      intermediate_size
				_snake_case          =      squeeze_factor
				_snake_case          =      hidden_act
				_snake_case          =      num_attention_heads
				_snake_case          =      hidden_dropout
				_snake_case          =      attention_dropout
				_snake_case          =      activation_dropout
				_snake_case          =      feat_proj_dropout
				_snake_case          =      final_dropout
				_snake_case          =      layerdrop
				_snake_case          =      layer_norm_eps
				_snake_case          =      initializer_range
				_snake_case          =      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
				_snake_case          =      apply_spec_augment
				_snake_case          =      mask_time_prob
				_snake_case          =      mask_time_length
				_snake_case          =      mask_time_min_masks
				_snake_case          =      mask_feature_prob
				_snake_case          =      mask_feature_length
				_snake_case          =      mask_feature_min_masks

				# ctc loss
				_snake_case          =      ctc_loss_reduction
				_snake_case          =      ctc_zero_infinity

				# sequence classification
				_snake_case          =      use_weighted_layer_sum
				_snake_case          =      classifier_proj_size





	@property
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				return functools.reduce(operator.mul      ,  self.conv_stride      ,  1     )

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()