'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a=	logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

a=	{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a=	{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a=	sorted(arg_to_scheduler.keys())
a=	'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''






class       __lowercase		(    pl.LightningModule    ):







     """simple docstring"""






     def __init__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase=None  ,      _lowerCamelCase="base"  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      **_lowerCamelCase  ,      ):
         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(_lowerCamelCase     )
         __UpperCamelCase						:						List[Any]		     =	0
         __UpperCamelCase						:						Union[str, Any]		     =	Path(self.hparams.output_dir     )
         __UpperCamelCase						:						List[str]		     =	self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
             __UpperCamelCase						:						Any		     =	AutoConfig.from_pretrained(
                 self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path  ,      **({'num_labels': num_labels} if num_labels is not None else {})  ,      cache_dir=_lowerCamelCase  ,      **_lowerCamelCase  ,      )
         else:
             __UpperCamelCase						:						PretrainedConfig		     =	config

         __UpperCamelCase						:						Dict		     =	('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
         for p in extra_model_params:
             if getattr(self.hparams  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
                 assert hasattr(self.config  ,      _lowerCamelCase     ), f"""model config doesn't have a `{p}` attribute"""
                 setattr(self.config  ,      _lowerCamelCase  ,      getattr(self.hparams  ,      _lowerCamelCase     )     )

         if tokenizer is None:
             __UpperCamelCase						:						Union[str, Any]		     =	AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,      cache_dir=_lowerCamelCase  ,      )
         else:
             __UpperCamelCase						:						PreTrainedTokenizer		     =	tokenizer
         __UpperCamelCase						:						Any		     =	MODEL_MODES[mode]
         if model is None:
             __UpperCamelCase						:						Any		     =	self.model_type.from_pretrained(
                 self.hparams.model_name_or_path  ,      from_tf=bool('.ckpt' in self.hparams.model_name_or_path     )  ,      config=self.config  ,      cache_dir=_lowerCamelCase  ,      )
         else:
             __UpperCamelCase						:						int		     =	model







     def lowerCAmelCase  (		self  ,      *_lowerCamelCase  ,      **_lowerCamelCase     ):
         __UpperCamelCase						:						List[Any]		     =	self.model_type.from_pretrained(*_lowerCamelCase  ,      **_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	arg_to_scheduler[self.hparams.lr_scheduler]
         __UpperCamelCase						:						Any		     =	get_schedule_func(
             self.opt  ,      num_warmup_steps=self.hparams.warmup_steps  ,      num_training_steps=self.total_steps()     )
         __UpperCamelCase						:						int		     =	{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
         return scheduler







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						int		     =	self.model
         __UpperCamelCase						:						Union[str, Any]		     =	['bias', 'LayerNorm.weight']
         __UpperCamelCase						:						Optional[Any]		     =	[
             {
                 'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )],
                 'weight_decay': self.hparams.weight_decay,
             },
             {
                 'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay     )],
                 'weight_decay': 0.0,
             },
         ]
         if self.hparams.adafactor:
             __UpperCamelCase						:						str		     =	Adafactor(
                 _lowerCamelCase  ,      lr=self.hparams.learning_rate  ,      scale_parameter=_lowerCamelCase  ,      relative_step=_lowerCamelCase     )

         else:
             __UpperCamelCase						:						Dict		     =	AdamW(
                 _lowerCamelCase  ,      lr=self.hparams.learning_rate  ,      eps=self.hparams.adam_epsilon     )
         __UpperCamelCase						:						Tuple		     =	optimizer

         __UpperCamelCase						:						Optional[Any]		     =	self.get_lr_scheduler()

         return [optimizer], [scheduler]







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         return self.validation_step(_lowerCamelCase  ,      _lowerCamelCase     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         return self.validation_end(_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						int		     =	max(1  ,      self.hparams.gpus     )  # TODO: consider num_tpu_cores
         __UpperCamelCase						:						int		     =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         if mode == "test":
             __UpperCamelCase						:						int		     =	len(self.test_dataloader().dataset     )
         else:
             __UpperCamelCase						:						List[str]		     =	self.get_dataloader('train'  ,      self.hparams.train_batch_size  ,      shuffle=_lowerCamelCase     )
             __UpperCamelCase						:						Any		     =	len(self.train_dataloader().dataset     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = False     ):
         raise NotImplementedError('You must implement this for your task'     )







     def lowerCAmelCase  (		self     ):
         return self.train_loader







     def lowerCAmelCase  (		self     ):
         return self.get_dataloader('dev'  ,      self.hparams.eval_batch_size  ,      shuffle=_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         return self.get_dataloader('test'  ,      self.hparams.eval_batch_size  ,      shuffle=_lowerCamelCase     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         return os.path.join(
             self.hparams.data_dir  ,      'cached_{}_{}_{}'.format(
                 _lowerCamelCase  ,      list(filter(_lowerCamelCase  ,      self.hparams.model_name_or_path.split('/'     )     )     ).pop()  ,      str(self.hparams.max_seq_length     )  ,      )  ,      )







     @pl.utilities.rank_zero_only
     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						List[str]		     =	self.output_dir.joinpath('best_tfmr'     )
         __UpperCamelCase						:						str		     =	self.step_count
         self.model.save_pretrained(_lowerCamelCase     )
         self.tokenizer.save_pretrained(_lowerCamelCase     )







     @staticmethod
     def lowerCAmelCase  (		_lowerCamelCase  ,      _lowerCamelCase     ):
         parser.add_argument(
             '--model_name_or_path'  ,      default=_lowerCamelCase  ,      type=_lowerCamelCase  ,      required=_lowerCamelCase  ,      help='Path to pretrained model or model identifier from huggingface.co/models'  ,      )
         parser.add_argument(
             '--config_name'  ,      default=''  ,      type=_lowerCamelCase  ,      help='Pretrained config name or path if not the same as model_name'     )
         parser.add_argument(
             '--tokenizer_name'  ,      default=_lowerCamelCase  ,      type=_lowerCamelCase  ,      help='Pretrained tokenizer name or path if not the same as model_name'  ,      )
         parser.add_argument(
             '--cache_dir'  ,      default=''  ,      type=_lowerCamelCase  ,      help='Where do you want to store the pre-trained models downloaded from huggingface.co'  ,      )
         parser.add_argument(
             '--encoder_layerdrop'  ,      type=_lowerCamelCase  ,      help='Encoder layer dropout probability (Optional). Goes into model.config'  ,      )
         parser.add_argument(
             '--decoder_layerdrop'  ,      type=_lowerCamelCase  ,      help='Decoder layer dropout probability (Optional). Goes into model.config'  ,      )
         parser.add_argument(
             '--dropout'  ,      type=_lowerCamelCase  ,      help='Dropout probability (Optional). Goes into model.config'  ,      )
         parser.add_argument(
             '--attention_dropout'  ,      type=_lowerCamelCase  ,      help='Attention dropout probability (Optional). Goes into model.config'  ,      )
         parser.add_argument('--learning_rate'  ,      default=5E-5  ,      type=_lowerCamelCase  ,      help='The initial learning rate for Adam.'     )
         parser.add_argument(
             '--lr_scheduler'  ,      default='linear'  ,      choices=_lowerCamelCase  ,      metavar=_lowerCamelCase  ,      type=_lowerCamelCase  ,      help='Learning rate scheduler'  ,      )
         parser.add_argument('--weight_decay'  ,      default=0.0  ,      type=_lowerCamelCase  ,      help='Weight decay if we apply some.'     )
         parser.add_argument('--adam_epsilon'  ,      default=1E-8  ,      type=_lowerCamelCase  ,      help='Epsilon for Adam optimizer.'     )
         parser.add_argument('--warmup_steps'  ,      default=0  ,      type=_lowerCamelCase  ,      help='Linear warmup over warmup_steps.'     )
         parser.add_argument('--num_workers'  ,      default=4  ,      type=_lowerCamelCase  ,      help='kwarg passed to DataLoader'     )
         parser.add_argument('--num_train_epochs'  ,      dest='max_epochs'  ,      default=3  ,      type=_lowerCamelCase     )
         parser.add_argument('--train_batch_size'  ,      default=3_2  ,      type=_lowerCamelCase     )
         parser.add_argument('--eval_batch_size'  ,      default=3_2  ,      type=_lowerCamelCase     )
         parser.add_argument('--adafactor'  ,      action='store_true'     )






class       __lowercase		(    pl.Callback    ):







     """simple docstring"""






     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						int		     =	trainer.lr_schedulers[0]['scheduler']
         __UpperCamelCase						:						Union[str, Any]		     =	{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
         pl_module.logger.log_metrics(_lowerCamelCase     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         rank_zero_info('***** Validation results *****'     )
         __UpperCamelCase						:						List[Any]		     =	trainer.callback_metrics
         # Log results
         for key in sorted(_lowerCamelCase     ):
             if key not in ["log", "progress_bar"]:
                 rank_zero_info('{} = {}\n'.format(_lowerCamelCase  ,      str(metrics[key]     )     )     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         rank_zero_info('***** Test results *****'     )
         __UpperCamelCase						:						Any		     =	trainer.callback_metrics
         # Log and save results to file
         __UpperCamelCase						:						List[Any]		     =	os.path.join(pl_module.hparams.output_dir  ,      'test_results.txt'     )
         with open(_lowerCamelCase  ,      'w'     ) as writer:
             for key in sorted(_lowerCamelCase     ):
                 if key not in ["log", "progress_bar"]:
                     rank_zero_info('{} = {}\n'.format(_lowerCamelCase  ,      str(metrics[key]     )     )     )
                     writer.write('{} = {}\n'.format(_lowerCamelCase  ,      str(metrics[key]     )     )     )







def 			_UpperCamelCase			(  _a						:						Tuple					,  _a						:						Dict      ):




    """simple docstring"""



    parser.add_argument(
        '--output_dir'					,  default=_a					,  type=_a					,  required=_a					,  help='The output directory where the model predictions and checkpoints will be written.'					,  )
    parser.add_argument(
        '--fp16'					,  action='store_true'					,  help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'					,  )

    parser.add_argument(
        '--fp16_opt_level'					,  type=_a					,  default='O2'					,  help=(
            'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
            'See details at https://nvidia.github.io/apex/amp.html'
        )					,  )
    parser.add_argument('--n_tpu_cores'					,  dest='tpu_cores'					,  type=_a      )
    parser.add_argument('--max_grad_norm'					,  dest='gradient_clip_val'					,  default=1.0					,  type=_a					,  help='Max gradient norm'      )
    parser.add_argument('--do_train'					,  action='store_true'					,  help='Whether to run training.'      )
    parser.add_argument('--do_predict'					,  action='store_true'					,  help='Whether to run predictions on the test set.'      )
    parser.add_argument(
        '--gradient_accumulation_steps'					,  dest='accumulate_grad_batches'					,  type=_a					,  default=1					,  help='Number of updates steps to accumulate before performing a backward/update pass.'					,  )
    parser.add_argument('--seed'					,  type=_a					,  default=4_2					,  help='random seed for initialization'      )
    parser.add_argument(
        '--data_dir'					,  default=_a					,  type=_a					,  required=_a					,  help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'					,  )







def 			_UpperCamelCase			(  _a						:						BaseTransformer					,  _a						:						argparse.Namespace					,  _a						:						Dict=None					,  _a						:						Union[str, Any]=True					,  _a						:						int=[]					,  _a						:						Tuple=None					,  _a						:						Dict=None					,  **_a						:						Optional[int]					,  ):




    """simple docstring"""



    pl.seed_everything(args.seed      )

    # init model
    __UpperCamelCase						:						Optional[int]		     =	Path(model.hparams.output_dir      )
    odir.mkdir(exist_ok=_a      )

    # add custom checkpoints
    if checkpoint_callback is None:
        __UpperCamelCase						:						int		     =	pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir					,  prefix='checkpoint'					,  monitor='val_loss'					,  mode='min'					,  save_top_k=1      )
    if early_stopping_callback:
        extra_callbacks.append(_a      )
    if logging_callback is None:
        __UpperCamelCase						:						Any		     =	LoggingCallback()

    __UpperCamelCase						:						Any		     =	{}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        __UpperCamelCase						:						Any		     =	1_6
        __UpperCamelCase						:						List[Any]		     =	args.fpaa_opt_level

    if args.gpus > 1:
        __UpperCamelCase						:						int		     =	'ddp'

    __UpperCamelCase						:						Tuple		     =	args.accumulate_grad_batches
    __UpperCamelCase						:						Optional[Any]		     =	extra_train_kwargs.get('accelerator'					,  _a      )
    __UpperCamelCase						:						Optional[Any]		     =	extra_train_kwargs.get('profiler'					,  _a      )

    __UpperCamelCase						:						Any		     =	pl.Trainer.from_argparse_args(
        _a					,  weights_summary=_a					,  callbacks=[logging_callback] + extra_callbacks					,  logger=_a					,  checkpoint_callback=_a					,  **_a					,  )

    if args.do_train:
        trainer.fit(_a      )

    return trainer




'''simple docstring'''
from .stack import Stack







def 			_UpperCamelCase			(  _a						:						str      ):




    """simple docstring"""



    __UpperCamelCase						:						Stack[str]		     =	Stack()
    __UpperCamelCase						:						Any		     =	{'(': ')', '[': ']', '{': '}'}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(_a      )
        elif bracket in (")", "]", "}"):
            if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                return False
    return stack.is_empty()


if __name__ == "__main__":
  from doctest import testmod

  testmod()

  a=	['''((()))''', '''((())''', '''(()))''']
  print('''Balanced parentheses demonstration:\n''')
  for example in examples:
    a=	'''''' if balanced_parentheses(example) else '''not '''
    print(F"""{example} is {not_str}balanced""")



