




"""simple docstring"""

def 		_snake_case	(   _snake_case				:   int				)       ->   bool:






      '''simple docstring'''


      if number < 0:
            raise ValueError('number must not be negative'				)
      return number & (number - 1) == 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()





"""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


__snake_case       =       logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

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







class _lowerCAmelCase							( pl.LightningModule      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__=None		,      UpperCamelCase__="base"		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      **UpperCamelCase__		,      )      ->						Optional[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(UpperCamelCase__		)
       snake_case       : Tuple    =       0
       snake_case       : List[str]    =       Path(self.hparams.output_dir		)
       snake_case       : Any    =       self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
           snake_case       : 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=UpperCamelCase__		,      **UpperCamelCase__		,      )
       else:
           snake_case       : PretrainedConfig    =       config

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

       if tokenizer is None:
           snake_case       : Dict    =       AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,      cache_dir=UpperCamelCase__		,      )
       else:
           snake_case       : PreTrainedTokenizer    =       tokenizer
       snake_case       : List[str]    =       MODEL_MODES[mode]
       if model is None:
           snake_case       : Union[str, 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=UpperCamelCase__		,      )
       else:
           snake_case       : List[Any]    =       model







   def 					lowerCamelCase						(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : List[Any]    =       self.model_type.from_pretrained(*UpperCamelCase__		,      **UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Dict    =       arg_to_scheduler[self.hparams.lr_scheduler]
       snake_case       : Any    =       get_schedule_func(
           self.opt		,      num_warmup_steps=self.hparams.warmup_steps		,      num_training_steps=self.total_steps()		)
       snake_case       : List[str]    =       {"scheduler": scheduler, "interval": "step", "frequency": 1}
       return scheduler







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Dict    =       self.model
       snake_case       : Tuple    =       ["bias", "LayerNorm.weight"]
       snake_case       : 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:
           snake_case       : Optional[int]    =       Adafactor(
               UpperCamelCase__		,      lr=self.hparams.learning_rate		,      scale_parameter=UpperCamelCase__		,      relative_step=UpperCamelCase__		)

       else:
           snake_case       : Optional[Any]    =       AdamW(
               UpperCamelCase__		,      lr=self.hparams.learning_rate		,      eps=self.hparams.adam_epsilon		)
       snake_case       : Any    =       optimizer

       snake_case       : List[Any]    =       self.get_lr_scheduler()

       return [optimizer], [scheduler]







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.validation_step(UpperCamelCase__		,      UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       return self.validation_end(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : List[str]    =       max(1		,      self.hparams.gpus		)  # TODO: consider num_tpu_cores
       snake_case       : List[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		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       if stage == "test":
           snake_case       : Dict    =       len(self.test_dataloader().dataset		)
       else:
           snake_case       : str    =       self.get_dataloader("train"		,      self.hparams.train_batch_size		,      shuffle=UpperCamelCase__		)
           snake_case       : Dict    =       len(self.train_dataloader().dataset		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__ = False		)      ->						int:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       return self.train_loader







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       return self.get_dataloader("dev"		,      self.hparams.eval_batch_size		,      shuffle=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.get_dataloader("test"		,      self.hparams.eval_batch_size		,      shuffle=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




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







   @pl.utilities.rank_zero_only
   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       snake_case       : str    =       self.output_dir.joinpath("best_tfmr"		)
       snake_case       : int    =       self.step_count
       self.model.save_pretrained(UpperCamelCase__		)
       self.tokenizer.save_pretrained(UpperCamelCase__		)







   @staticmethod
   def 					lowerCamelCase						(      UpperCamelCase__		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




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







class _lowerCAmelCase							( pl.Callback      ):







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						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 _lowerCAmelCase							( pl.Callback      ):







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




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







class _lowerCAmelCase							( pl.Callback      ):







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : List[str]    =       trainer.lr_schedulers[0]["scheduler"]
       snake_case       : str    =       {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
       pl_module.logger.log_metrics(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




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







def     __lowerCAmelCase		(		lowercase    :    Any		,				lowercase    :    str					)      ->		None:


    """simple docstring"""





    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    :    Any=None		,				lowercase    :    List[str]=True		,				lowercase    :    List[Any]=[]		,				lowercase    :    Any=None		,				lowercase    :    Optional[int]=None		,				**lowercase    :    List[Any]		,				)      ->		Tuple:


    """simple docstring"""





    pl.seed_everything(args.seed					)

    # init model
    snake_case       : Optional[int]    =       Path(model.hparams.output_dir					)
    odir.mkdir(exist_ok=lowercase					)

    # add custom checkpoints
    if checkpoint_callback is None:
        snake_case       : 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(lowercase					)
    if logging_callback is None:
        snake_case       : Tuple    =       LoggingCallback()

    snake_case       : str    =       {}

    if args.fpaa:
        snake_case       : Union[str, Any]    =       16

    if args.gpus > 1:
        snake_case       : List[str]    =       "auto"
        snake_case       : int    =       "ddp"

    snake_case       : Dict    =       args.accumulate_grad_batches
    snake_case       : Tuple    =       None
    snake_case       : Any    =       "auto"

    snake_case       : int    =       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





