




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__    :							Union[str, Any]       		=     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCAmelCase__    :							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
UpperCAmelCase__    :							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
}
UpperCAmelCase__    :							Optional[int]       		=     sorted(arg_to_scheduler.keys())
UpperCAmelCase__    :							Any       		=     """{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class   UpperCAmelCase    (      pl.LightningModule ):






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

     _lowercase:			Union[str, Any]      =     ('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:
       _lowercase:			List[Any]      =     AutoTokenizer.from_pretrained(
           self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,       cache_dir=snake_case_    ,       )
     else:
       _lowercase:			Optional[int]      =     tokenizer
     _lowercase:			Tuple      =     MODEL_MODES[mode]
     if model is None:
       _lowercase:			List[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=snake_case_    ,       )
     else:
       _lowercase:			Dict      =     model






   def 			_a    (						self :							Dict    ,       *UpperCamelCase_ :							List[str]    ,       **UpperCamelCase_ :							Optional[Any]  )->      Optional[int]:
     _lowercase:			List[str]      =     self.model_type.from_pretrained(*snake_case_    ,       **snake_case_  )






   def 			_a    (						self :							Optional[Any]  )->      Any:
     _lowercase:			str      =     arg_to_scheduler[self.hparams.lr_scheduler]
     _lowercase:			Optional[Any]      =     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 			_a    (						self :							Any  )->      Optional[int]:
     _lowercase:			str      =     self.model
     _lowercase:			Union[str, Any]      =     ['bias', 'LayerNorm.weight']
     _lowercase:			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:
       _lowercase:			Union[str, Any]      =     Adafactor(
           snake_case_    ,       lr=self.hparams.learning_rate    ,       scale_parameter=snake_case_    ,       relative_step=snake_case_  )

     else:
       _lowercase:			Any      =     AdamW(
           snake_case_    ,       lr=self.hparams.learning_rate    ,       eps=self.hparams.adam_epsilon  )
     _lowercase:			List[Any]      =     optimizer

     _lowercase:			Dict      =     self.get_lr_scheduler()

     return [optimizer], [scheduler]






   def 			_a    (						self :							List[str]    ,       UpperCamelCase_ :							str    ,       UpperCamelCase_ :							Optional[Any]  )->      Dict:
     return self.validation_step(snake_case_    ,       snake_case_  )






   def 			_a    (						self :							Union[str, Any]    ,       UpperCamelCase_ :							List[Any]  )->      Dict:
     return self.validation_end(snake_case_  )






   def 			_a    (						self :							Optional[Any]  )->      int:
     _lowercase:			Tuple      =     max(1    ,       self.hparams.gpus  )  # TODO: consider num_tpu_cores
     _lowercase:			Tuple      =     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
     return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






   def 			_a    (						self :							Any    ,       UpperCamelCase_ :							Tuple  )->      Union[str, Any]:
     if stage == "test":
       _lowercase:			int      =     len(self.test_dataloader().dataset  )
     else:
       _lowercase:			Dict      =     self.get_dataloader('train'    ,       self.hparams.train_batch_size    ,       shuffle=snake_case_  )
       _lowercase:			Union[str, Any]      =     len(self.train_dataloader().dataset  )






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							Dict    ,       UpperCamelCase_ :							Optional[int]    ,       UpperCamelCase_ :							List[Any] = False  )->      Optional[Any]:
     raise NotImplementedError('You must implement this for your task'  )






   def 			_a    (						self :							Tuple  )->      str:
     return self.train_loader






   def 			_a    (						self :							Union[str, Any]  )->      str:
     return self.get_dataloader('dev'    ,       self.hparams.eval_batch_size    ,       shuffle=snake_case_  )






   def 			_a    (						self :							Dict  )->      int:
     return self.get_dataloader('test'    ,       self.hparams.eval_batch_size    ,       shuffle=snake_case_  )






   def 			_a    (						self :							Union[str, Any]    ,       UpperCamelCase_ :							str  )->      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 			_a    (						self :							Tuple    ,       UpperCamelCase_ :							List[str]  )->      None:
     _lowercase:			Optional[Any]      =     self.output_dir.joinpath('best_tfmr'  )
     _lowercase:			Any      =     self.step_count
     self.model.save_pretrained(snake_case_  )
     self.tokenizer.save_pretrained(snake_case_  )






   @staticmethod
   def 			_a    (						UpperCamelCase_ :							str    ,       UpperCamelCase_ :							Any  )->      Any:
     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   UpperCAmelCase    (      pl.Callback ):






   def 			_a    (						self :							Optional[int]    ,       UpperCamelCase_ :							Any    ,       UpperCamelCase_ :							Tuple  )->      Union[str, Any]:
     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   UpperCAmelCase    (      pl.Callback ):






   def 			_a    (						self :							Tuple    ,       UpperCamelCase_ :							Optional[Any]    ,       UpperCamelCase_ :							List[Any]  )->      List[Any]:
     _lowercase:			Tuple      =     trainer.lr_schedulers[0]['scheduler']
     _lowercase:			List[str]      =     {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
     pl_module.logger.log_metrics(snake_case_  )






   def 			_a    (						self :							List[str]    ,       UpperCamelCase_ :							Dict    ,       UpperCamelCase_ :							str  )->      List[str]:
     rank_zero_info('***** Validation results *****'  )
     _lowercase:			Union[str, Any]      =     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 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							Union[str, Any]    ,       UpperCamelCase_ :							List[str]  )->      Optional[Any]:
     rank_zero_info('***** Test results *****'  )
     _lowercase:			int      =     trainer.callback_metrics
     # Log and save results to file
     _lowercase:			Dict      =     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 		_lowerCamelCase					(       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 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase=None       ,  UpperCamelCase=True       ,  UpperCamelCase=None       ,  UpperCamelCase=[]       ,  UpperCamelCase=None       ,  UpperCamelCase=None       ,  **UpperCamelCase       ,  )      ->		Union[str, Any]:





  """simple docstring"""





  pl.seed_everything(args.seed				)

  # init model
  _lowercase:			Any      =     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[int]      =     LoggingCallback()

  _lowercase:			Optional[int]      =     {}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    _lowercase:			Dict      =     16
    # train_params["amp_level"] = args.fp16_opt_level

  if args.gpus > 1:
    _lowercase:			List[Any]      =     'auto'  # "ddp"
    _lowercase:			Union[str, Any]      =     'ddp'

  _lowercase:			Tuple      =     args.accumulate_grad_batches
  _lowercase:			Optional[int]      =     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  _lowercase:			List[Any]      =     'auto'

  _lowercase:			Union[str, Any]      =     pl.Trainer.from_argparse_args(
      UpperCamelCase       ,  weights_summary=UpperCamelCase       ,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]       ,  logger=UpperCamelCase       ,  **UpperCamelCase       ,  )

  if args.do_train:
    trainer.fit(UpperCamelCase				)

  return trainer








"""simple docstring"""





def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE = 10_00       )       ->						str:
    """simple docstring"""

    if (not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )) or (digit_position <= 0):
        raise ValueError('Digit position must be a positive integer'       )
    elif (not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )) or (precision < 0):
        raise ValueError('Precision must be a nonnegative integer'       )

    # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
    # accurate
    UpperCamelCase__								=     (
        4 * _subsum(SCREAMING_SNAKE_CASE			,						1			,						SCREAMING_SNAKE_CASE       )
        - 2 * _subsum(SCREAMING_SNAKE_CASE			,						4			,						SCREAMING_SNAKE_CASE       )
        - _subsum(SCREAMING_SNAKE_CASE			,						5			,						SCREAMING_SNAKE_CASE       )
        - _subsum(SCREAMING_SNAKE_CASE			,						6			,						SCREAMING_SNAKE_CASE       )
    )

    # return the first hex digit of the fractional part of the result
    return hex(int((sum_result % 1) * 16       )       )[2:]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						float:
    """simple docstring"""

    UpperCamelCase__								=     0.0
    for sum_index in range(digit_pos_to_extract + precision       ):
        UpperCamelCase__								=     8 * sum_index + denominator_addend
        if sum_index < digit_pos_to_extract:
            # if the exponential term is an integer and we mod it by the denominator
            # before dividing, only the integer part of the sum will change;
            # the fractional part will not
            UpperCamelCase__								=     pow(
                16			,						digit_pos_to_extract - 1 - sum_index			,						SCREAMING_SNAKE_CASE       )
        else:
            UpperCamelCase__								=     pow(16			,						digit_pos_to_extract - 1 - sum_index       )
        total += exponential_term / denominator
    return total


if __name__ == "__main__":
  import doctest

  doctest.testmod()




