



"""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:					Optional[int]     				=	logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_UpperCamelCase:					Dict     				=	{
    '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:					int     				=	{
    '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:					Optional[int]     				=	sorted(arg_to_scheduler.keys())
_UpperCamelCase:					Dict     				=	'{' + ', '.join(arg_to_scheduler_choices) + '}'


class 	a__		(    pl.LightningModule  ):


   def __init__(				self   :				Any,  lowerCAmelCase   :				argparse.Namespace,  lowerCAmelCase   :				List[str]=None,  lowerCAmelCase   :				int="base",  lowerCAmelCase   :				int=None,  lowerCAmelCase   :				List[str]=None,  lowerCAmelCase   :				Union[str, Any]=None,  **lowerCAmelCase   :				List[str],  )     ->							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      )
         lowercase	:   List[str]											= 0
         lowercase	:   Optional[Any]											= Path(self.hparams.output_dir      )
         lowercase	:   Optional[Any]											= self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               lowercase	:   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:
               lowercase	:   PretrainedConfig											= config

         lowercase	:   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:
               lowercase	:   Optional[int]											= AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,  cache_dir=lowerCAmelCase,  )
         else:
               lowercase	:   PreTrainedTokenizer											= tokenizer
         lowercase	:   Union[str, Any]											= MODEL_MODES[mode]
         if model is None:
               lowercase	:   int											= 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:
               lowercase	:   Union[str, Any]											= model


   def 							lowercase					(				self   :				Any,  *lowerCAmelCase   :				List[Any],  **lowerCAmelCase   :				List[str]      )     ->							List[Any]:
         lowercase	:   Dict											= self.model_type.from_pretrained(*lowerCAmelCase,  **lowerCAmelCase      )


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


   def 							lowercase					(				self   :				Tuple      )     ->							Any:
         lowercase	:   int											= self.model
         lowercase	:   Tuple											= ['bias', 'LayerNorm.weight']
         lowercase	:   str											= [
             {
                 '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:
               lowercase	:   Any											= Adafactor(
                   lowerCAmelCase,  lr=self.hparams.learning_rate,  scale_parameter=lowerCAmelCase,  relative_step=lowerCAmelCase      )

         else:
               lowercase	:   Union[str, Any]											= AdamW(
                   lowerCAmelCase,  lr=self.hparams.learning_rate,  eps=self.hparams.adam_epsilon      )
         lowercase	:   List[Any]											= optimizer

         lowercase	:   Union[str, Any]											= self.get_lr_scheduler()

         return [optimizer], [scheduler]


   def 							lowercase					(				self   :				Optional[int],  lowerCAmelCase   :				Any,  lowerCAmelCase   :				List[Any]      )     ->							Any:
         return self.validation_step(lowerCAmelCase,  lowerCAmelCase      )


   def 							lowercase					(				self   :				int,  lowerCAmelCase   :				int      )     ->							Optional[Any]:
         return self.validation_end(lowerCAmelCase      )


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


   def 							lowercase					(				self   :				Optional[int],  lowerCAmelCase   :				str,  lowerCAmelCase   :				int,  lowerCAmelCase   :				bool = False      )     ->							List[str]:
         raise NotImplementedError('You must implement this for your task'      )


   def 							lowercase					(				self   :				Union[str, Any]      )     ->							List[str]:
         return self.train_loader


   def 							lowercase					(				self   :				List[str]      )     ->							List[Any]:
         return self.get_dataloader('dev',  self.hparams.eval_batch_size,  shuffle=lowerCAmelCase      )


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


   def 							lowercase					(				self   :				Dict,  lowerCAmelCase   :				int      )     ->							List[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 							lowercase					(				self   :				str,  lowerCAmelCase   :				Dict[str, Any]      )     ->							None:
         lowercase	:   int											= self.output_dir.joinpath('best_tfmr'      )
         lowercase	:   Tuple											= self.step_count
         self.model.save_pretrained(lowerCAmelCase      )
         self.tokenizer.save_pretrained(lowerCAmelCase      )






   @staticmethod
   def 							lowercase					(				lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				List[Any]      )     ->							Optional[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 	a__		(    pl.Callback  ):


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				Dict      )     ->							Union[str, Any]:
         lowercase	:   List[Any]											= trainer.lr_schedulers[0]['scheduler']
         lowercase	:   Union[str, Any]											= {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
         pl_module.logger.log_metrics(lowerCAmelCase      )


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				pl.Trainer,  lowerCAmelCase   :				pl.LightningModule      )     ->							Optional[Any]:
         rank_zero_info('***** Validation results *****'      )
         lowercase	:   Optional[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 							lowercase					(				self   :				str,  lowerCAmelCase   :				pl.Trainer,  lowerCAmelCase   :				pl.LightningModule      )     ->							List[str]:
         rank_zero_info('***** Test results *****'      )
         lowercase	:   Tuple											= trainer.callback_metrics
         # Log and save results to file
         lowercase	:   Union[str, 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 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase					)  ->      None:






      '''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 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase=None						,   _UpperCAmelCase=True						,   _UpperCAmelCase=[]						,   _UpperCAmelCase=None						,   _UpperCAmelCase=None						,   **_UpperCAmelCase						,   )  ->      str:






      '''simple docstring'''


      pl.seed_everything(args.seed					)

      # init model
      lowercase	:   Dict											= Path(model.hparams.output_dir					)
      odir.mkdir(exist_ok=_UpperCAmelCase					)

      # add custom checkpoints
      if checkpoint_callback is None:
            lowercase	:   List[Any]											= 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:
            lowercase	:   Optional[Any]											= LoggingCallback()

      lowercase	:   List[str]											= {}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            lowercase	:   str											= 16
            lowercase	:   Optional[Any]											= args.fpaa_opt_level

      if args.gpus > 1:
            lowercase	:   List[Any]											= 'ddp'

      lowercase	:   str											= args.accumulate_grad_batches
      lowercase	:   Dict											= extra_train_kwargs.get('accelerator'						,   _UpperCAmelCase					)
      lowercase	:   Optional[Any]											= extra_train_kwargs.get('profiler'						,   _UpperCAmelCase					)

      lowercase	:   List[Any]											= 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 __future__ import annotations


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      lowercase	:   Optional[Any]											= [0] * max_one

      for i in range(0						,   _UpperCAmelCase					):
            lowercase	:   List[Any]											= i

      for i in range(2						,   _UpperCAmelCase					):
            if totients[i] == i:
                  for j in range(_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					):
                        totients[j] -= totients[j] // i

      return totients


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase					)  ->      bool:






      '''simple docstring'''


      return sorted(str(_UpperCAmelCase					)					) == sorted(str(_UpperCAmelCase					)					)


def 	lowercase__  (      _UpperCAmelCase = 10_00_00_00					)  ->      int:






      '''simple docstring'''


      lowercase	:   Union[str, Any]											= 1  # i
      lowercase	:   Any											= 0  # φ(i)
      lowercase	:   int											= get_totients(max_n + 1					)

      for i in range(2						,   max_n + 1					):
            lowercase	:   Any											= totients[i]

            if i * min_denominator < min_numerator * t and has_same_digits(_UpperCAmelCase						,   _UpperCAmelCase					):
                  lowercase	:   List[Any]											= i
                  lowercase	:   Optional[Any]											= t

      return min_numerator


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