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_			    =							logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

UpperCAmelCase_			    =							{
    '''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_			    =							{
    '''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_			    =							sorted(arg_to_scheduler.keys())
UpperCAmelCase_			    =							'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''




class  __magic_name__  (		pl.LightningModule    ):





   """simple docstring"""




   def __init__(	self					:  Dict		,  _lowercase					:  argparse.Namespace		,  _lowercase					:  str=None		,  _lowercase					:  List[Any]="base"		,  _lowercase					:  Optional[Any]=None		,  _lowercase					:  Union[str, Any]=None		,  _lowercase					:  List[str]=None		,  **_lowercase					:  List[Any]		,  ):



    """simple docstring"""



    super().__init__()
    # TODO: move to self.save_hyperparameters()
    # self.save_hyperparameters()
    # can also expand arguments into trainer signature for easier reading

    self.save_hyperparameters(_lowercase							)
    _UpperCamelCase:     Optional[int]							= 0
    _UpperCamelCase:     List[Any]							= Path(self.hparams.output_dir							)
    _UpperCamelCase:     Tuple							= self.hparams.cache_dir if self.hparams.cache_dir else None
    if config is None:
     _UpperCamelCase:     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=_lowercase		,  **_lowercase		,  )
    else:
     _UpperCamelCase:     PretrainedConfig							= config

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

    if tokenizer is None:
     _UpperCamelCase:     Optional[Any]							= AutoTokenizer.from_pretrained(
         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,  cache_dir=_lowercase		,  )
    else:
     _UpperCamelCase:     PreTrainedTokenizer							= tokenizer
    _UpperCamelCase:     str							= MODEL_MODES[mode]
    if model is None:
     _UpperCamelCase:     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=_lowercase		,  )
    else:
     _UpperCamelCase:     Tuple							= model



   def   lowerCAmelCase						(	self					:  List[str]		,  *_lowercase					:  Dict		,  **_lowercase					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.model_type.from_pretrained(*_lowercase		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.model
    _UpperCamelCase:     str							= ['''bias''', '''LayerNorm.weight''']
    _UpperCamelCase:     Any							= [
        {
            '''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:
     _UpperCamelCase:     Tuple							= Adafactor(
         _lowercase		,  lr=self.hparams.learning_rate		,  scale_parameter=_lowercase		,  relative_step=_lowercase							)

    else:
     _UpperCamelCase:     Dict							= AdamW(
         _lowercase		,  lr=self.hparams.learning_rate		,  eps=self.hparams.adam_epsilon							)
    _UpperCamelCase:     str							= optimizer

    _UpperCamelCase:     Dict							= self.get_lr_scheduler()

    return [optimizer], [scheduler]



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  Any		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    return self.validation_step(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  str							):



    """simple docstring"""



    return self.validation_end(_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= max(1		,  self.hparams.gpus							)  # TODO: consider num_tpu_cores
    _UpperCamelCase:     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   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Optional[int]							):



    """simple docstring"""



    if stage == "test":
     _UpperCamelCase:     Optional[Any]							= len(self.test_dataloader().dataset							)
    else:
     _UpperCamelCase:     str							= self.get_dataloader('''train'''		,  self.hparams.train_batch_size		,  shuffle=_lowercase							)
     _UpperCamelCase:     Tuple							= len(self.train_dataloader().dataset							)



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  str		,  _lowercase					:  int		,  _lowercase					:  bool = False							):



    """simple docstring"""



    raise NotImplementedError('''You must implement this for your task'''							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    return self.train_loader



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    return self.get_dataloader('''dev'''		,  self.hparams.eval_batch_size		,  shuffle=_lowercase							)



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    return self.get_dataloader('''test'''		,  self.hparams.eval_batch_size		,  shuffle=_lowercase							)



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Any							):



    """simple docstring"""



    return os.path.join(
        self.hparams.data_dir		,  '''cached_{}_{}_{}'''.format(
            _lowercase		,  list(filter(_lowercase		,  self.hparams.model_name_or_path.split('''/'''							)							)							).pop()		,  str(self.hparams.max_seq_length							)		,  )		,  )



   @pl.utilities.rank_zero_only
   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Dict[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self.output_dir.joinpath('''best_tfmr'''							)
    _UpperCamelCase:     str							= self.step_count
    self.model.save_pretrained(_lowercase							)
    self.tokenizer.save_pretrained(_lowercase							)



   @staticmethod
   def   lowerCAmelCase						(	_lowercase					:  Any		,  _lowercase					:  str							):



    """simple docstring"""



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




class  __magic_name__  (		pl.Callback    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Dict		,  _lowercase					:  Optional[int]							):



    """simple docstring"""



    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  __magic_name__  (		pl.Callback    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  int		,  _lowercase					:  Dict							):



    """simple docstring"""



    for name, param in pl_module.model.rag.named_parameters():
     if param.grad is None:
      print(_lowercase							)




class  __magic_name__  (		pl.Callback    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  List[str]		,  _lowercase					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     str							= trainer.lr_schedulers[0]['''scheduler''']
    _UpperCamelCase:     Tuple							= {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
    pl_module.logger.log_metrics(_lowercase							)



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  pl.Trainer		,  _lowercase					:  pl.LightningModule							):



    """simple docstring"""



    rank_zero_info('''***** Validation results *****'''							)
    _UpperCamelCase:     List[str]							= trainer.callback_metrics
    # Log results
    for key in sorted(_lowercase							):
     if key not in ["log", "progress_bar"]:
      rank_zero_info('''{} = {}\n'''.format(_lowercase		,  str(metrics[key]							)							)							)



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  pl.Trainer		,  _lowercase					:  pl.LightningModule							):



    """simple docstring"""



    rank_zero_info('''***** Test results *****'''							)
    _UpperCamelCase:     Union[str, Any]							= trainer.callback_metrics
    # Log and save results to file
    _UpperCamelCase:     Optional[int]							= os.path.join(pl_module.hparams.output_dir		,  '''test_results.txt'''							)
    with open(_lowercase		,  '''w'''							) as writer:
     for key in sorted(_lowercase							):
      if key not in ["log", "progress_bar"]:
       rank_zero_info('''{} = {}\n'''.format(_lowercase		,  str(metrics[key]							)							)							)
       writer.write('''{} = {}\n'''.format(_lowercase		,  str(metrics[key]							)							)							)




def       lowerCAmelCase_     (		lowercase:							List[Any]  ,					lowercase:							Dict		)    ->				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(lowercase		).parent / '''test_run''' / '''model_checkpoints'''		)  ,					type=lowercase  ,					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=lowercase  ,					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=lowercase		)
 parser.add_argument('''--max_grad_norm'''  ,					dest='''gradient_clip_val'''  ,					default=1.0  ,					type=lowercase  ,					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=lowercase  ,					default=1  ,					help='''Number of updates steps to accumulate before performing a backward/update pass.'''  ,					)
 parser.add_argument('''--seed'''  ,					type=lowercase  ,					default=42  ,					help='''random seed for initialization'''		)
 parser.add_argument(
     '''--data_dir'''  ,					default=str(Path(lowercase		).parent / '''test_run''' / '''dummy-train-data'''		)  ,					type=lowercase  ,					help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''  ,					)





def       lowerCAmelCase_     (		lowercase:							BaseTransformer  ,					lowercase:							argparse.Namespace  ,					lowercase:							Optional[int]=None  ,					lowercase:							Any=True  ,					lowercase:							List[Any]=[]  ,					lowercase:							Dict=None  ,					lowercase:							Optional[Any]=None  ,					**lowercase:							Tuple  ,					)    ->				Optional[int]:







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

 # init model
 _UpperCamelCase:     Any							= Path(model.hparams.output_dir		)
 odir.mkdir(exist_ok=lowercase		)

 # add custom checkpoints
 if checkpoint_callback is None:
  _UpperCamelCase:     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(lowercase		)
 if logging_callback is None:
  _UpperCamelCase:     int							= LoggingCallback()

 _UpperCamelCase:     Optional[Any]							= {}

 if args.fpaa:
  _UpperCamelCase:     Any							= 16

 if args.gpus > 1:
  _UpperCamelCase:     Union[str, Any]							= '''auto'''
  _UpperCamelCase:     Union[str, Any]							= '''ddp'''

 _UpperCamelCase:     List[Any]							= args.accumulate_grad_batches
 _UpperCamelCase:     Optional[int]							= None
 _UpperCamelCase:     List[str]							= '''auto'''

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

 if args.do_train:
  trainer.fit(lowercase		)

 else:
  print('''RAG modeling tests with new set functions successfuly executed!'''		)
 return trainer
def       lowerCAmelCase_     (		lowercase:							float		)    ->				float:







 '''simple docstring'''

 if edge <= 0 or not isinstance(lowercase  ,					lowercase		):
  raise ValueError('''Length must be a positive.'''		)
 return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)




def       lowerCAmelCase_     (		lowercase:							float		)    ->				float:







 '''simple docstring'''

 if edge <= 0 or not isinstance(lowercase  ,					lowercase		):
  raise ValueError('''Length must be a positive.'''		)
 return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
  import doctest

  doctest.testmod()