


"""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 							lowerCamelCase       (			pl.LightningModule ):


							"""simple docstring"""






							def __init__(						self :     List[str]		, lowerCamelCase_ :     argparse.Namespace		, lowerCamelCase_ :     List[str]=None		, lowerCamelCase_ :     List[str]="base"		, lowerCamelCase_ :     List[str]=None		, lowerCamelCase_ :     Dict=None		, lowerCamelCase_ :     Tuple=None		, **lowerCamelCase_ :     Dict		, ):
													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__		=    0
													UpperCAmelCase__		=    Path(self.hparams.output_dir				)
													UpperCAmelCase__		=    self.hparams.cache_dir if self.hparams.cache_dir else None
													if config is None:
																			UpperCAmelCase__		=    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__		=    config

													UpperCAmelCase__		=    ('''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__		=    AutoTokenizer.from_pretrained(
																			    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		, cache_dir=lowerCamelCase_		, )
													else:
																			UpperCAmelCase__		=    tokenizer
													UpperCAmelCase__		=    MODEL_MODES[mode]
													if model is None:
																			UpperCAmelCase__		=    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__		=    model






							def 					lowerCAmelCase     (						self :     Tuple		, *lowerCamelCase_ :     Optional[Any]		, **lowerCamelCase_ :     List[Any]				):
													UpperCAmelCase__		=    self.model_type.from_pretrained(*lowerCamelCase_		, **lowerCamelCase_				)






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






							def 					lowerCAmelCase     (						self :     Any				):
													UpperCAmelCase__		=    self.model
													UpperCAmelCase__		=    ['''bias''', '''LayerNorm.weight''']
													UpperCAmelCase__		=    [
													    {
													        '''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__		=    Adafactor(
																			    lowerCamelCase_		, lr=self.hparams.learning_rate		, scale_parameter=lowerCamelCase_		, relative_step=lowerCamelCase_				)

													else:
																			UpperCAmelCase__		=    AdamW(
																			    lowerCamelCase_		, lr=self.hparams.learning_rate		, eps=self.hparams.adam_epsilon				)
													UpperCAmelCase__		=    optimizer

													UpperCAmelCase__		=    self.get_lr_scheduler()

													return [optimizer], [scheduler]






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     Optional[int]		, lowerCamelCase_ :     Union[str, Any]				):
													return self.validation_step(lowerCamelCase_		, lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     Optional[Any]				):
													return self.validation_end(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Tuple				):
													UpperCAmelCase__		=    max(1		, self.hparams.gpus				)  # TODO: consider num_tpu_cores
													UpperCAmelCase__		=    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 :     List[Any]		, lowerCamelCase_ :     List[Any]				):
													if mode == "test":
																			UpperCAmelCase__		=    len(self.test_dataloader().dataset				)
													else:
																			UpperCAmelCase__		=    self.get_dataloader('''train'''		, self.hparams.train_batch_size		, shuffle=lowerCamelCase_				)
																			UpperCAmelCase__		=    len(self.train_dataloader().dataset				)






							def 					lowerCAmelCase     (						self :     Union[str, Any]		, lowerCamelCase_ :     str		, lowerCamelCase_ :     int		, lowerCamelCase_ :     bool = False				):
													raise NotImplementedError('''You must implement this for your task'''				)






							def 					lowerCAmelCase     (						self :     int				):
													return self.train_loader






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






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






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     Any				):
													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 :     Optional[Any]		, lowerCamelCase_ :     Dict[str, Any]				):
													UpperCAmelCase__		=    self.output_dir.joinpath('''best_tfmr'''				)
													UpperCAmelCase__		=    self.step_count
													self.model.save_pretrained(lowerCamelCase_				)
													self.tokenizer.save_pretrained(lowerCamelCase_				)






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     Tuple		, lowerCamelCase_ :     Union[str, Any]				):
													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=32		, type=lowerCamelCase_				)
													parser.add_argument('''--eval_batch_size'''		, default=32		, type=lowerCamelCase_				)
													parser.add_argument('''--adafactor'''		, action='''store_true'''				)




class 							lowerCamelCase       (			pl.Callback ):


							"""simple docstring"""






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






							def 					lowerCAmelCase     (						self :     Optional[Any]		, lowerCamelCase_ :     pl.Trainer		, lowerCamelCase_ :     pl.LightningModule				):
													rank_zero_info('''***** Validation results *****'''				)
													UpperCAmelCase__		=    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 :     Union[str, Any]		, lowerCamelCase_ :     pl.Trainer		, lowerCamelCase_ :     pl.LightningModule				):
													rank_zero_info('''***** Test results *****'''				)
													UpperCAmelCase__		=    trainer.callback_metrics
													# Log and save results to file
													UpperCAmelCase__		=    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 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						parser.add_argument(
						    '''--output_dir'''	,			default=_UpperCamelCase	,			type=_UpperCamelCase	,			required=_UpperCamelCase	,			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=_UpperCamelCase	,			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=_UpperCamelCase )
						parser.add_argument('''--max_grad_norm'''	,			dest='''gradient_clip_val'''	,			default=1.0	,			type=_UpperCamelCase	,			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=_UpperCamelCase	,			default=1	,			help='''Number of updates steps to accumulate before performing a backward/update pass.'''	,			)
						parser.add_argument('''--seed'''	,			type=_UpperCamelCase	,			default=42	,			help='''random seed for initialization''' )
						parser.add_argument(
						    '''--data_dir'''	,			default=_UpperCamelCase	,			type=_UpperCamelCase	,			required=_UpperCamelCase	,			help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''	,			)






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=True	,			_UpperCamelCase=[]	,			_UpperCamelCase=None	,			_UpperCamelCase=None	,			**_UpperCamelCase	,			):





						"""simple docstring"""

						pl.seed_everything(args.seed )

						# init model
						UpperCAmelCase__		=    Path(model.hparams.output_dir )
						odir.mkdir(exist_ok=_UpperCamelCase )

						# add custom checkpoints
						if checkpoint_callback is None:
												UpperCAmelCase__		=    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(_UpperCamelCase )
						if logging_callback is None:
												UpperCAmelCase__		=    LoggingCallback()

						UpperCAmelCase__		=    {}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												UpperCAmelCase__		=    16
												UpperCAmelCase__		=    args.fpaa_opt_level

						if args.gpus > 1:
												UpperCAmelCase__		=    '''ddp'''

						UpperCAmelCase__		=    args.accumulate_grad_batches
						UpperCAmelCase__		=    extra_train_kwargs.get('''accelerator'''	,			_UpperCamelCase )
						UpperCAmelCase__		=    extra_train_kwargs.get('''profiler'''	,			_UpperCamelCase )

						UpperCAmelCase__		=    pl.Trainer.from_argparse_args(
						    _UpperCamelCase	,			weights_summary=_UpperCamelCase	,			callbacks=[logging_callback] + extra_callbacks	,			logger=_UpperCamelCase	,			checkpoint_callback=_UpperCamelCase	,			**_UpperCamelCase	,			)

						if args.do_train:
												trainer.fit(_UpperCamelCase )

						return trainer



"""simple docstring"""







from collections import defaultdict






def 					a__ (       _UpperCamelCase = 5 ):





						"""simple docstring"""

						UpperCAmelCase__		=    defaultdict(_UpperCamelCase )
						UpperCAmelCase__		=    0

						while True:
												UpperCAmelCase__		=    get_digits(_UpperCamelCase )
												freqs[digits].append(_UpperCamelCase )

												if len(freqs[digits] ) == max_base:
																		UpperCAmelCase__		=    freqs[digits][0] ** 3
																		return base

												num += 1






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return "".join(sorted(str(num**3 ) ) )


if __name__ == "__main__":
						print(f'''{solution() = }''')