
"""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		):




	def __init__( self		, UpperCAmelCase_		, UpperCAmelCase_=None		, UpperCAmelCase_="base"		, UpperCAmelCase_=None		, UpperCAmelCase_=None		, UpperCAmelCase_=None		, **UpperCAmelCase_		, )		->							List[str]:
			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					)
			lowerCamelCase     :			int		=    0
			lowerCamelCase     :			List[str]		=    Path(self.hparams.output_dir					)
			lowerCamelCase     :			int		=    self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
					lowerCamelCase     :			Union[str, 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:
					lowerCamelCase     :			PretrainedConfig		=    config

			lowerCamelCase     :			List[str]		=    ("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:
					lowerCamelCase     :			Tuple		=    AutoTokenizer.from_pretrained(
					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		, cache_dir=__lowerCamelCase		, )
			else:
					lowerCamelCase     :			PreTrainedTokenizer		=    tokenizer
			lowerCamelCase     :			int		=    MODEL_MODES[mode]
			if model is None:
					lowerCamelCase     :			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:
					lowerCamelCase     :			Union[str, Any]		=    model




	def 			_UpperCamelCase    ( self		, *UpperCAmelCase_		, **UpperCAmelCase_					)		->							List[str]:
			lowerCamelCase     :			str		=    self.model_type.from_pretrained(*__lowerCamelCase		, **__lowerCamelCase					)




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Union[str, Any]		=    arg_to_scheduler[self.hparams.lr_scheduler]
			lowerCamelCase     :			Dict		=    get_schedule_func(
			    self.opt		, num_warmup_steps=self.hparams.warmup_steps		, num_training_steps=self.total_steps()					)
			lowerCamelCase     :			Optional[int]		=    {"scheduler": scheduler, "interval": "step", "frequency": 1}
			return scheduler




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			lowerCamelCase     :			str		=    self.model
			lowerCamelCase     :			List[str]		=    ["bias", "LayerNorm.weight"]
			lowerCamelCase     :			List[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:
					lowerCamelCase     :			str		=    Adafactor(
					    __lowerCamelCase		, lr=self.hparams.learning_rate		, scale_parameter=__lowerCamelCase		, relative_step=__lowerCamelCase					)

			else:
					lowerCamelCase     :			int		=    AdamW(
					    __lowerCamelCase		, lr=self.hparams.learning_rate		, eps=self.hparams.adam_epsilon					)
			lowerCamelCase     :			int		=    optimizer

			lowerCamelCase     :			Optional[int]		=    self.get_lr_scheduler()

			return [optimizer], [scheduler]




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Tuple:
			return self.validation_step(__lowerCamelCase		, __lowerCamelCase					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							Dict:
			return self.validation_end(__lowerCamelCase					)




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




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_ = False					)		->							Dict:
			raise NotImplementedError('You must implement this for your task'					)




	def 			_UpperCamelCase    ( self					)		->							int:
			return self.train_loader




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			return self.get_dataloader('dev'		, self.hparams.eval_batch_size		, shuffle=__lowerCamelCase					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			return self.get_dataloader('test'		, self.hparams.eval_batch_size		, shuffle=__lowerCamelCase					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							int:
			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 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							None:
			lowerCamelCase     :			int		=    self.output_dir.joinpath('best_tfmr'					)
			lowerCamelCase     :			str		=    self.step_count
			self.model.save_pretrained(__lowerCamelCase					)
			self.tokenizer.save_pretrained(__lowerCamelCase					)




	@staticmethod
	def 			_UpperCamelCase    ( UpperCAmelCase_		, UpperCAmelCase_					)		->							str:
			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        _lowercase   (				pl.Callback		):




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Tuple:
			lowerCamelCase     :			Union[str, Any]		=    trainer.lr_schedulers[0]["scheduler"]
			lowerCamelCase     :			Union[str, Any]		=    {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
			pl_module.logger.log_metrics(__lowerCamelCase					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Union[str, Any]:
			rank_zero_info('***** Validation results *****'					)
			lowerCamelCase     :			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 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							str:
			rank_zero_info('***** Test results *****'					)
			lowerCamelCase     :			int		=    trainer.callback_metrics
			# Log and save results to file
			lowerCamelCase     :			str		=    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_,						a_			):





		'''simple docstring'''


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





def 		UpperCAmelCase					(		a_,						a_,						a_=None,						a_=True,						a_=[],						a_=None,						a_=None,						**a_,						):





		'''simple docstring'''


		pl.seed_everything(args.seed			)

		# init model
		lowerCamelCase     :			List[str]		=    Path(model.hparams.output_dir			)
		odir.mkdir(exist_ok=__lowerCamelCase			)

		# add custom checkpoints
		if checkpoint_callback is None:
				lowerCamelCase     :			List[str]		=    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(__lowerCamelCase			)
		if logging_callback is None:
				lowerCamelCase     :			Any		=    LoggingCallback()

		lowerCamelCase     :			List[Any]		=    {}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				lowerCamelCase     :			Union[str, Any]		=    16
				lowerCamelCase     :			int		=    args.fpaa_opt_level

		if args.gpus > 1:
				lowerCamelCase     :			str		=    "ddp"

		lowerCamelCase     :			int		=    args.accumulate_grad_batches
		lowerCamelCase     :			Optional[int]		=    extra_train_kwargs.get('accelerator',						__lowerCamelCase			)
		lowerCamelCase     :			Optional[int]		=    extra_train_kwargs.get('profiler',						__lowerCamelCase			)

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

		if args.do_train:
				trainer.fit(__lowerCamelCase			)

		return trainer













from itertools import permutations






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Dict  			=							list(map("".join,				permutations("0123456789"		)		)		)
     return result[999999]


if __name__ == "__main__":
       print(solution())