





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 :   Dict  			=  logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_A :   Union[str, Any]  			=  {
    '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 :   Optional[Any]  			=  {
    '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 :   List[Any]  			=  sorted(arg_to_scheduler.keys())
_A :   List[Any]  			=  '{' + ', '.join(arg_to_scheduler_choices) + '}'


class        __SCREAMING_SNAKE_CASE      (			pl.LightningModule ):







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

          lowerCamelCase__       :						List[Any]        =							('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
          for p in extra_model_params:
             if getattr(self.hparams		, A		, A      ):
                assert hasattr(self.config		, A      ), F"model config doesn't have a `{p}` attribute"
                setattr(self.config		, A		, getattr(self.hparams		, A      )      )

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







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, *A					:     List[str]		, **A					:     Optional[Any]      )       ->List[Any]:
          lowerCamelCase__       :						str        =							self.model_type.from_pretrained(*A		, **A      )







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







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->int:
          lowerCamelCase__       :						int        =							self.model
          lowerCamelCase__       :						Tuple        =							['''bias''', '''LayerNorm.weight''']
          lowerCamelCase__       :						Optional[int]        =							[
              {
                  '''params''': [
                      p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )
                  ],  # check this named paramters
                  '''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__       :						Optional[Any]        =							Adafactor(
                 A		, lr=self.hparams.learning_rate		, scale_parameter=A		, relative_step=A      )

          else:
             lowerCamelCase__       :						Any        =							AdamW(
                 A		, lr=self.hparams.learning_rate		, eps=self.hparams.adam_epsilon      )
          lowerCamelCase__       :						Tuple        =							optimizer

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

          return [optimizer], [scheduler]







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Any		, A					:     Tuple      )       ->List[str]:
          return self.validation_step(A		, A      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Optional[int]      )       ->Any:
          return self.validation_end(A      )







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







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     str		, A					:     int		, A					:     bool = False      )       ->List[str]:
          raise NotImplementedError('''You must implement this for your task'''      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[int]:
          return self.train_loader







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->int:
          return self.get_dataloader('''dev'''		, self.hparams.eval_batch_size		, shuffle=A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->int:
          return self.get_dataloader('''test'''		, self.hparams.eval_batch_size		, shuffle=A      )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     int      )       ->Optional[int]:
          return os.path.join(
              self.hparams.data_dir		, '''cached_{}_{}_{}'''.format(
                  A		, list(filter(A		, self.hparams.model_name_or_path.split('''/'''      )      )      ).pop()		, str(self.hparams.max_seq_length      )		, )		, )







       @pl.utilities.rank_zero_only
       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Dict[str, Any]      )       ->None:
          lowerCamelCase__       :						Union[str, Any]        =							self.output_dir.joinpath('''best_tfmr'''      )
          lowerCamelCase__       :						Optional[int]        =							self.step_count
          self.model.save_pretrained(A      )
          self.tokenizer.save_pretrained(A      )







       @staticmethod
       def 						__lowerCamelCase   (				A					:     List[Any]		, A					:     Optional[Any]      )       ->Dict:
          parser.add_argument(
              '''--model_name_or_path'''		, default=A		, type=A		, required=A		, help='''Path to pretrained model or model identifier from huggingface.co/models'''		, )
          parser.add_argument(
              '''--config_name'''		, default=''''''		, type=A		, help='''Pretrained config name or path if not the same as model_name'''      )
          parser.add_argument(
              '''--tokenizer_name'''		, default=A		, type=A		, help='''Pretrained tokenizer name or path if not the same as model_name'''		, )
          parser.add_argument(
              '''--cache_dir'''		, default=str(Path(A      ).parent / '''test_run''' / '''cache'''      )		, type=A		, help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''		, )
          parser.add_argument(
              '''--encoder_layerdrop'''		, type=A		, help='''Encoder layer dropout probability (Optional). Goes into model.config'''		, )
          parser.add_argument(
              '''--decoder_layerdrop'''		, type=A		, help='''Decoder layer dropout probability (Optional). Goes into model.config'''		, )
          parser.add_argument(
              '''--dropout'''		, type=A		, help='''Dropout probability (Optional). Goes into model.config'''		, )
          parser.add_argument(
              '''--attention_dropout'''		, type=A		, help='''Attention dropout probability (Optional). Goes into model.config'''		, )
          parser.add_argument('''--learning_rate'''		, default=5e-5		, type=A		, help='''The initial learning rate for Adam.'''      )
          parser.add_argument(
              '''--lr_scheduler'''		, default='''linear'''		, choices=A		, metavar=A		, type=A		, help='''Learning rate scheduler'''		, )
          parser.add_argument('''--weight_decay'''		, default=0.0		, type=A		, help='''Weight decay if we apply some.'''      )
          parser.add_argument('''--adam_epsilon'''		, default=1e-8		, type=A		, help='''Epsilon for Adam optimizer.'''      )
          parser.add_argument('''--warmup_steps'''		, default=0		, type=A		, help='''Linear warmup over warmup_steps.'''      )
          parser.add_argument('''--num_workers'''		, default=4		, type=A		, help='''kwarg passed to DataLoader'''      )
          parser.add_argument('''--num_train_epochs'''		, dest='''max_epochs'''		, default=3		, type=A      )
          parser.add_argument('''--train_batch_size'''		, default=3_2		, type=A      )
          parser.add_argument('''--eval_batch_size'''		, default=3_2		, type=A      )
          parser.add_argument('''--adafactor'''		, action='''store_true'''      )


class        __SCREAMING_SNAKE_CASE      (			pl.Callback ):







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     Any		, A					:     int      )       ->List[str]:
          if (
              trainer.is_global_zero and trainer.global_rank == 0
          ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
             pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.


class        __SCREAMING_SNAKE_CASE      (			pl.Callback ):







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     Union[str, Any]		, A					:     Union[str, Any]      )       ->str:
          # print(pl_module.model.rag)
          for name, param in pl_module.model.rag.named_parameters():
             if param.grad is None:
                print(A      )


class        __SCREAMING_SNAKE_CASE      (			pl.Callback ):







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     List[Any]		, A					:     str      )       ->str:
          lowerCamelCase__       :						Dict        =							trainer.lr_schedulers[0]['''scheduler''']
          lowerCamelCase__       :						List[Any]        =							{F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
          pl_module.logger.log_metrics(A      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     pl.Trainer		, A					:     pl.LightningModule      )       ->int:
          rank_zero_info('''***** Validation results *****'''      )
          lowerCamelCase__       :						int        =							trainer.callback_metrics
          # Log results
          for key in sorted(A      ):
             if key not in ["log", "progress_bar"]:
                rank_zero_info('''{} = {}\n'''.format(A		, str(metrics[key]      )      )      )







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



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    None:







   """simple docstring"""
   #  To allow all pl args uncomment the following line
   #  parser = pl.Trainer.add_argparse_args(parser)
   parser.add_argument(
       '''--output_dir'''      ,  default=str(Path(UpperCAmelCase					).parent / '''test_run''' / '''model_checkpoints'''					)      ,  type=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=str(Path(UpperCAmelCase					).parent / '''test_run''' / '''dummy-train-data'''					)      ,  type=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      ,  )    ->    str:







   """simple docstring"""
   pl.seed_everything(args.seed					)

   # init model
   lowerCamelCase__       :						Dict        =							Path(model.hparams.output_dir					)
   odir.mkdir(exist_ok=UpperCAmelCase					)

   # add custom checkpoints
   if checkpoint_callback is None:
      lowerCamelCase__       :						Optional[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(UpperCAmelCase					)
   if logging_callback is None:
      lowerCamelCase__       :						int        =							LoggingCallback()

   lowerCamelCase__       :						List[str]        =							{}

   if args.fpaa:
      lowerCamelCase__       :						int        =							16

   if args.gpus > 1:
      lowerCamelCase__       :						List[str]        =							'''auto'''
      lowerCamelCase__       :						Optional[int]        =							'''ddp'''

   lowerCamelCase__       :						int        =							args.accumulate_grad_batches
   lowerCamelCase__       :						Union[str, Any]        =							None
   lowerCamelCase__       :						Dict        =							'''auto'''

   lowerCamelCase__       :						Optional[Any]        =							pl.Trainer.from_argparse_args(
       UpperCAmelCase      ,  weights_summary=UpperCAmelCase      ,  callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]      ,  logger=UpperCAmelCase      ,  val_check_interval=1      ,  num_sanity_val_steps=2      ,  **UpperCAmelCase      ,  )

   if args.do_train:
      trainer.fit(UpperCAmelCase					)

   else:
      print('''RAG modeling tests with new set functions successfuly executed!'''					)
   return trainer










def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if b == 0:
      return 1
   if (b % 2) == 0:
      return actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)
   else:
      return a * actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   if b < 0:
      return 1 / actual_power(UpperCAmelCase      ,  UpperCAmelCase					)
   return actual_power(UpperCAmelCase      ,  UpperCAmelCase					)


if __name__ == "__main__":
       print(power(-2, -3))



