

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


_lowerCamelCase								=logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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



class 		a_	(							pl.LightningModule					):



      """simple docstring"""







      def __init__(				self					:							Optional[Any]      ,snake_case					:							argparse.Namespace      ,snake_case					:							Dict=None      ,snake_case					:							Optional[int]="base"      ,snake_case					:							int=None      ,snake_case					:							str=None      ,snake_case					:							Optional[Any]=None      ,**snake_case					:							List[Any]      ,):
           super().__init__()
           # TODO: move to self.save_hyperparameters()
           # self.save_hyperparameters()
           # can also expand arguments into trainer signature for easier reading

           self.save_hyperparameters(snake_case    )
           SCREAMING_SNAKE_CASE										=       0
           SCREAMING_SNAKE_CASE										=       Path(self.hparams.output_dir    )
           SCREAMING_SNAKE_CASE										=       self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
                SCREAMING_SNAKE_CASE										=       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=snake_case      ,**snake_case      ,)
           else:
                SCREAMING_SNAKE_CASE										=       config

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

           if tokenizer is None:
                SCREAMING_SNAKE_CASE										=       AutoTokenizer.from_pretrained(
                    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,cache_dir=snake_case      ,)
           else:
                SCREAMING_SNAKE_CASE										=       tokenizer
           SCREAMING_SNAKE_CASE										=       MODEL_MODES[mode]
           if model is None:
                SCREAMING_SNAKE_CASE										=       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=snake_case      ,)
           else:
                SCREAMING_SNAKE_CASE										=       model
      def 	_lowerCAmelCase					(				self					:							Tuple      ,*snake_case					:							str      ,**snake_case					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=       self.model_type.from_pretrained(*snake_case      ,**snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=       arg_to_scheduler[self.hparams.lr_scheduler]
           SCREAMING_SNAKE_CASE										=       get_schedule_func(
               self.opt      ,num_warmup_steps=self.hparams.warmup_steps      ,num_training_steps=self.total_steps()    )
           SCREAMING_SNAKE_CASE										=       {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
           return scheduler
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=       self.model
           SCREAMING_SNAKE_CASE										=       ['bias', 'LayerNorm.weight']
           SCREAMING_SNAKE_CASE										=       [
               {
                   '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:
                SCREAMING_SNAKE_CASE										=       Adafactor(
                    snake_case      ,lr=self.hparams.learning_rate      ,scale_parameter=snake_case      ,relative_step=snake_case    )

           else:
                SCREAMING_SNAKE_CASE										=       AdamW(
                    snake_case      ,lr=self.hparams.learning_rate      ,eps=self.hparams.adam_epsilon    )
           SCREAMING_SNAKE_CASE										=       optimizer

           SCREAMING_SNAKE_CASE										=       self.get_lr_scheduler()

           return [optimizer], [scheduler]
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							Dict      ,snake_case					:							str    ):
           return self.validation_step(snake_case      ,snake_case    )
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							str    ):
           return self.validation_end(snake_case    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=       max(1      ,self.hparams.gpus    )  # TODO: consider num_tpu_cores
           SCREAMING_SNAKE_CASE										=       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[str]      ,snake_case					:							Optional[Any]    ):
           if mode == "test":
                SCREAMING_SNAKE_CASE										=       len(self.test_dataloader().dataset    )
           else:
                SCREAMING_SNAKE_CASE										=       self.get_dataloader('train'      ,self.hparams.train_batch_size      ,shuffle=snake_case    )
                SCREAMING_SNAKE_CASE										=       len(self.train_dataloader().dataset    )
      def 	_lowerCAmelCase					(				self					:							List[str]      ,snake_case					:							str      ,snake_case					:							int      ,snake_case					:							bool = False    ):
           raise NotImplementedError('You must implement this for your task'    )
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return self.train_loader
      def 	_lowerCAmelCase					(				self					:							int    ):
           return self.get_dataloader('dev'      ,self.hparams.eval_batch_size      ,shuffle=snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           return self.get_dataloader('test'      ,self.hparams.eval_batch_size      ,shuffle=snake_case    )
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Tuple    ):
           return os.path.join(
               self.hparams.data_dir      ,'cached_{}_{}_{}'.format(
                   snake_case      ,list(filter(snake_case      ,self.hparams.model_name_or_path.split('/'    )    )    ).pop()      ,str(self.hparams.max_seq_length    )      ,)      ,)
      @pl.utilities.rank_zero_only
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Dict[str, Any]    ):
           SCREAMING_SNAKE_CASE										=       self.output_dir.joinpath('best_tfmr'    )
           SCREAMING_SNAKE_CASE										=       self.step_count
           self.model.save_pretrained(snake_case    )
           self.tokenizer.save_pretrained(snake_case    )


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







class 		a_	(							pl.Callback					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							Optional[Any]      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=       trainer.lr_schedulers[0]['scheduler']
           SCREAMING_SNAKE_CASE										=       {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
           pl_module.logger.log_metrics(snake_case    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							pl.Trainer      ,snake_case					:							pl.LightningModule    ):
           rank_zero_info('***** Validation results *****'    )
           SCREAMING_SNAKE_CASE										=       trainer.callback_metrics
           # Log results
           for key in sorted(snake_case    ):
                if key not in ["log", "progress_bar"]:
                     rank_zero_info('{} = {}\n'.format(snake_case      ,str(metrics[key]    )    )    )


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







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """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       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_=None,   lowerCAmelCase_=True,   lowerCAmelCase_=[],   lowerCAmelCase_=None,   lowerCAmelCase_=None,   **lowerCAmelCase_,   ):





     """simple docstring"""







     pl.seed_everything(args.seed      )

     # init model
     SCREAMING_SNAKE_CASE										=       Path(model.hparams.output_dir      )
     odir.mkdir(exist_ok=lowerCAmelCase_      )

     # add custom checkpoints
     if checkpoint_callback is None:
          SCREAMING_SNAKE_CASE										=       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:
          SCREAMING_SNAKE_CASE										=       LoggingCallback()

     SCREAMING_SNAKE_CASE										=       {}

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

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

     SCREAMING_SNAKE_CASE										=       args.accumulate_grad_batches
     SCREAMING_SNAKE_CASE										=       extra_train_kwargs.get('accelerator',   lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=       extra_train_kwargs.get('profiler',   lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=       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




def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     SCREAMING_SNAKE_CASE										=1

     while power > 0:
          if power & 1:
               SCREAMING_SNAKE_CASE										=(result * base) % mod
          SCREAMING_SNAKE_CASE										=power >> 1
          SCREAMING_SNAKE_CASE										=(base * base) % mod

     return result







def       snake_case__    (       ):





     """simple docstring"""







     print(modular_exponential(3,   200,   13      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()

