





'''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


lowerCAmelCase					:     Optional[int]												=logging.getLogger(__name__)

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

lowerCAmelCase					:     Optional[int]												={
    '''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					:     str												={
    '''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					:     List[Any]												=sorted(arg_to_scheduler.keys())
lowerCAmelCase					:     Union[str, Any]												='''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class       a_   (				pl.LightningModule							):





     def __init__( self		:					Optional[Any]	,      lowercase		:					argparse.Namespace	,      lowercase		:					Optional[int]=None	,      lowercase		:					Optional[int]="base"	,      lowercase		:					Union[str, Any]=None	,      lowercase		:					Optional[Any]=None	,      lowercase		:					List[Any]=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						)
        lowercase_			:int    			= 0
        lowercase_			:int    			= Path(self.hparams.output_dir						)
        lowercase_			:Optional[int]    			= 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=lowercase	,      **lowercase	,      )
        else:
           lowercase_			:PretrainedConfig    			= config

        lowercase_			:List[str]    			= ("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:
           lowercase_			:str    			= AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,      cache_dir=lowercase	,      )
        else:
           lowercase_			:PreTrainedTokenizer    			= tokenizer
        lowercase_			:Any    			= MODEL_MODES[mode]
        if model is None:
           lowercase_			: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=lowercase	,      )
        else:
           lowercase_			:List[str]    			= model





     def 							lowercase__      ( self		:					int	,      *lowercase		:					List[Any]	,      **lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.model_type.from_pretrained(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:str    			= self.model
        lowercase_			:Any    			= ["bias", "LayerNorm.weight"]
        lowercase_			:Tuple    			= [
            {
                "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:
           lowercase_			:List[Any]    			= Adafactor(
               lowercase	,      lr=self.hparams.learning_rate	,      scale_parameter=lowercase	,      relative_step=lowercase						)

        else:
           lowercase_			:Union[str, Any]    			= AdamW(
               lowercase	,      lr=self.hparams.learning_rate	,      eps=self.hparams.adam_epsilon						)
        lowercase_			:List[Any]    			= optimizer

        lowercase_			:Tuple    			= self.get_lr_scheduler()

        return [optimizer], [scheduler]





     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					Any	,      lowercase		:					Union[str, Any]						):





        """simple docstring"""




        return self.validation_step(lowercase	,      lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]	,      lowercase		:					List[str]						):





        """simple docstring"""




        return self.validation_end(lowercase						)





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					Dict						):





        """simple docstring"""




        if stage == "test":
           lowercase_			:Dict    			= len(self.test_dataloader().dataset						)
        else:
           lowercase_			:Optional[int]    			= self.get_dataloader("train"	,      self.hparams.train_batch_size	,      shuffle=lowercase						)
           lowercase_			:Union[str, Any]    			= len(self.train_dataloader().dataset						)





     def 							lowercase__      ( self		:					int	,      lowercase		:					str	,      lowercase		:					int	,      lowercase		:					bool = False						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        return self.train_loader





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Union[str, Any]	,      lowercase		:					str						):





        """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 							lowercase__      ( self		:					str	,      lowercase		:					Dict[str, Any]						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.output_dir.joinpath("best_tfmr"						)
        lowercase_			:Optional[int]    			= self.step_count
        self.model.save_pretrained(lowercase						)
        self.tokenizer.save_pretrained(lowercase						)







     @staticmethod
     def 							lowercase__      ( lowercase		:					Any	,      lowercase		:					Dict						):





        """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       a_   (				pl.Callback							):





     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					Dict	,      lowercase		:					str						):





        """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       a_   (				pl.Callback							):





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					Optional[Any]	,      lowercase		:					int						):





        """simple docstring"""




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







class       a_   (				pl.Callback							):





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					Optional[int]	,      lowercase		:					Any						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					pl.Trainer	,      lowercase		:					pl.LightningModule						):





        """simple docstring"""




        rank_zero_info("***** Validation results *****"						)
        lowercase_			:Tuple    			= 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 							lowercase__      ( self		:					Tuple	,      lowercase		:					pl.Trainer	,      lowercase		:					pl.LightningModule						):





        """simple docstring"""




        rank_zero_info("***** Test results *****"						)
        lowercase_			:Tuple    			= trainer.callback_metrics
        # Log and save results to file
        lowercase_			:Any    			= 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 							UpperCAmelCase_  (						__lowerCamelCase					:					str	,__lowerCamelCase					:					List[Any] ):
   #  To allow all pl args uncomment the following line
   #  parser = pl.Trainer.add_argparse_args(parser)
   parser.add_argument(
       "--output_dir"	,default=str(Path(__lowerCamelCase ).parent / "test_run" / "model_checkpoints" )	,type=__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=str(Path(__lowerCamelCase ).parent / "test_run" / "dummy-train-data" )	,type=__lowerCamelCase	,help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."	,)


def 							UpperCAmelCase_  (						__lowerCamelCase					:					BaseTransformer	,__lowerCamelCase					:					argparse.Namespace	,__lowerCamelCase					:					Optional[Any]=None	,__lowerCamelCase					:					str=True	,__lowerCamelCase					:					str=[]	,__lowerCamelCase					:					str=None	,__lowerCamelCase					:					List[str]=None	,**__lowerCamelCase					:					Any	,):
   pl.seed_everything(args.seed )

   # init model
   lowercase_			:str    			= Path(model.hparams.output_dir )
   odir.mkdir(exist_ok=__lowerCamelCase )

   # add custom checkpoints
   if checkpoint_callback is None:
      lowercase_			: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(__lowerCamelCase )
   if logging_callback is None:
      lowercase_			:Optional[Any]    			= LoggingCallback()

   lowercase_			:str    			= {}

   if args.fpaa:
      lowercase_			:Optional[Any]    			= 16

   if args.gpus > 1:
      lowercase_			:List[Any]    			= "auto"
      lowercase_			:str    			= "ddp"

   lowercase_			:Any    			= args.accumulate_grad_batches
   lowercase_			:Union[str, Any]    			= None
   lowercase_			:Optional[int]    			= "auto"

   lowercase_			:str    			= pl.Trainer.from_argparse_args(
       __lowerCamelCase	,weights_summary=__lowerCamelCase	,callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]	,logger=__lowerCamelCase	,val_check_interval=1	,num_sanity_val_steps=2	,**__lowerCamelCase	,)

   if args.do_train:
      trainer.fit(__lowerCamelCase )

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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:     Optional[Any]												={
    '''configuration_nllb_moe''': [
        '''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''NllbMoeConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Tuple												=[
     '''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NllbMoeForConditionalGeneration''',
     '''NllbMoeModel''',
     '''NllbMoePreTrainedModel''',
     '''NllbMoeTop2Router''',
     '''NllbMoeSparseMLP''',
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 lowerCAmelCase					:     Tuple												=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


