






'''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


UpperCamelCase_					=				logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

UpperCamelCase_					=				{
    """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
UpperCamelCase_					=				{
    """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
}
UpperCamelCase_					=				sorted(arg_to_scheduler.keys())
UpperCamelCase_					=				"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class 					__UpperCAmelCase (		pl.LightningModule	):

      '''simple docstring'''





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

             self.save_hyperparameters(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	0
             UpperCAmelCase__	:   Optional[int]												=	Path(self.hparams.output_dir						)
             UpperCAmelCase__	:   Any												=	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=_UpperCAmelCase  ,		**_UpperCAmelCase  ,		)
             else:
                    UpperCAmelCase__	:   Dict												=	config

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

             if tokenizer is None:
                    UpperCAmelCase__	:   List[str]												=	AutoTokenizer.from_pretrained(
                        self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,		cache_dir=_UpperCAmelCase  ,		)
             else:
                    UpperCAmelCase__	:   int												=	tokenizer
             UpperCAmelCase__	:   str												=	MODEL_MODES[mode]
             if model is None:
                    UpperCAmelCase__	:   Dict												=	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=_UpperCAmelCase  ,		)
             else:
                    UpperCAmelCase__	:   str												=	model




      def 							lowerCamelCase     (					self  ,		*_UpperCAmelCase  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   str												=	self.model_type.from_pretrained(*_UpperCAmelCase  ,		**_UpperCAmelCase						)




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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[Any]												=	self.model
             UpperCAmelCase__	:   int												=	['''bias''', '''LayerNorm.weight''']
             UpperCAmelCase__	:   Union[str, 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__	:   Optional[Any]												=	Adafactor(
                        _UpperCAmelCase  ,		lr=self.hparams.learning_rate  ,		scale_parameter=_UpperCAmelCase  ,		relative_step=_UpperCAmelCase						)

             else:
                    UpperCAmelCase__	:   Optional[Any]												=	AdamW(
                        _UpperCAmelCase  ,		lr=self.hparams.learning_rate  ,		eps=self.hparams.adam_epsilon						)
             UpperCAmelCase__	:   Optional[Any]												=	optimizer

             UpperCAmelCase__	:   List[Any]												=	self.get_lr_scheduler()

             return [optimizer], [scheduler]




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             return self.validation_step(_UpperCAmelCase  ,		_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             return self.validation_end(_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	max(1  ,		self.hparams.gpus						)  # TODO: consider num_tpu_cores
             UpperCAmelCase__	:   Optional[Any]												=	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  ,		_UpperCAmelCase						):
             if mode == "test":
                    UpperCAmelCase__	:   List[str]												=	len(self.test_dataloader().dataset						)
             else:
                    UpperCAmelCase__	:   Optional[Any]												=	self.get_dataloader('''train'''  ,		self.hparams.train_batch_size  ,		shuffle=_UpperCAmelCase						)
                    UpperCAmelCase__	:   Union[str, Any]												=	len(self.train_dataloader().dataset						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = 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=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             return self.get_dataloader('''test'''  ,		self.hparams.eval_batch_size  ,		shuffle=_UpperCAmelCase						)




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




      @pl.utilities.rank_zero_only
      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   int												=	self.output_dir.joinpath('''best_tfmr'''						)
             UpperCAmelCase__	:   Optional[int]												=	self.step_count
             self.model.save_pretrained(_UpperCAmelCase						)
             self.tokenizer.save_pretrained(_UpperCAmelCase						)




      @staticmethod
      def 							lowerCamelCase     (					_UpperCAmelCase  ,		_UpperCAmelCase						):
             parser.add_argument(
                 '''--model_name_or_path'''  ,		default=_UpperCAmelCase  ,		type=_UpperCAmelCase  ,		required=_UpperCAmelCase  ,		help='''Path to pretrained model or model identifier from huggingface.co/models'''  ,		)
             parser.add_argument(
                 '''--config_name'''  ,		default=''''''  ,		type=_UpperCAmelCase  ,		help='''Pretrained config name or path if not the same as model_name'''						)
             parser.add_argument(
                 '''--tokenizer_name'''  ,		default=_UpperCAmelCase  ,		type=_UpperCAmelCase  ,		help='''Pretrained tokenizer name or path if not the same as model_name'''  ,		)
             parser.add_argument(
                 '''--cache_dir'''  ,		default=''''''  ,		type=_UpperCAmelCase  ,		help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''  ,		)
             parser.add_argument(
                 '''--encoder_layerdrop'''  ,		type=_UpperCAmelCase  ,		help='''Encoder layer dropout probability (Optional). Goes into model.config'''  ,		)
             parser.add_argument(
                 '''--decoder_layerdrop'''  ,		type=_UpperCAmelCase  ,		help='''Decoder layer dropout probability (Optional). Goes into model.config'''  ,		)
             parser.add_argument(
                 '''--dropout'''  ,		type=_UpperCAmelCase  ,		help='''Dropout probability (Optional). Goes into model.config'''  ,		)
             parser.add_argument(
                 '''--attention_dropout'''  ,		type=_UpperCAmelCase  ,		help='''Attention dropout probability (Optional). Goes into model.config'''  ,		)
             parser.add_argument('''--learning_rate'''  ,		default=5E-5  ,		type=_UpperCAmelCase  ,		help='''The initial learning rate for Adam.'''						)
             parser.add_argument(
                 '''--lr_scheduler'''  ,		default='''linear'''  ,		choices=_UpperCAmelCase  ,		metavar=_UpperCAmelCase  ,		type=_UpperCAmelCase  ,		help='''Learning rate scheduler'''  ,		)
             parser.add_argument('''--weight_decay'''  ,		default=0.0  ,		type=_UpperCAmelCase  ,		help='''Weight decay if we apply some.'''						)
             parser.add_argument('''--adam_epsilon'''  ,		default=1E-8  ,		type=_UpperCAmelCase  ,		help='''Epsilon for Adam optimizer.'''						)
             parser.add_argument('''--warmup_steps'''  ,		default=0  ,		type=_UpperCAmelCase  ,		help='''Linear warmup over warmup_steps.'''						)
             parser.add_argument('''--num_workers'''  ,		default=4  ,		type=_UpperCAmelCase  ,		help='''kwarg passed to DataLoader'''						)
             parser.add_argument('''--num_train_epochs'''  ,		dest='''max_epochs'''  ,		default=3  ,		type=_UpperCAmelCase						)
             parser.add_argument('''--train_batch_size'''  ,		default=32  ,		type=_UpperCAmelCase						)
             parser.add_argument('''--eval_batch_size'''  ,		default=32  ,		type=_UpperCAmelCase						)
             parser.add_argument('''--adafactor'''  ,		action='''store_true'''						)




class 					__UpperCAmelCase (		pl.Callback	):

      '''simple docstring'''





      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   List[Any]												=	trainer.lr_schedulers[0]['''scheduler''']
             UpperCAmelCase__	:   Optional[Any]												=	{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
             pl_module.logger.log_metrics(_UpperCAmelCase						)




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




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






def  lowerCAmelCase__		(					a_  :     Optional[Any]			,						a_  :     Optional[Any]			)   -> str:
       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  lowerCAmelCase__		(					a_  :     Any			,						a_  :     Dict			,						a_  :     Optional[Any]=None			,						a_  :     str=True			,						a_  :     int=[]			,						a_  :     str=None			,						a_  :     List[Any]=None			,						**a_  :     Any			,						)   -> Dict:
       pl.seed_everything(args.seed			)

       # init model
       UpperCAmelCase__	:   Any												=	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__	:   Dict												=	LoggingCallback()

       UpperCAmelCase__	:   str												=	{}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              UpperCAmelCase__	:   Dict												=	1_6
              UpperCAmelCase__	:   Union[str, Any]												=	args.fpaa_opt_level

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

       UpperCAmelCase__	:   Optional[int]												=	args.accumulate_grad_batches
       UpperCAmelCase__	:   Optional[int]												=	extra_train_kwargs.get('''accelerator'''			,						a_			)
       UpperCAmelCase__	:   Union[str, Any]												=	extra_train_kwargs.get('''profiler'''			,						a_			)

       UpperCAmelCase__	:   Optional[int]												=	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





def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			if n == 0:
						return 1

			elif n % 2 == 1:
						return binary_exponentiation(SCREAMING_SNAKE_CASE       ,						n - 1 ) * a

			else:
						SCREAMING_SNAKE_CASE     =       binary_exponentiation(SCREAMING_SNAKE_CASE       ,						n / 2 )
						return b * b


if __name__ == "__main__":
		try:
				__A    :				Tuple    						=	int(input("""Enter Base : """).strip())
				__A    :				Any    						=	int(input("""Enter Power : """).strip())
		except ValueError:
				print("""Invalid literal for integer""")

		__A    :				Union[str, Any]    						=	binary_exponentiation(BASE, POWER)
		print(f'''{BASE}^({POWER}) : {RESULT}''')






