






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 		lowercase_					(	pl.LightningModule   ):






      '''simple docstring'''




      def __init__(      self      :						str ,					__UpperCAmelCase      :						argparse.Namespace ,					__UpperCAmelCase      :						str=None ,					__UpperCAmelCase      :						List[str]="base" ,					__UpperCAmelCase      :						str=None ,					__UpperCAmelCase      :						int=None ,					__UpperCAmelCase      :						Optional[Any]=None ,					**__UpperCAmelCase      :						Optional[int] ,					)    ->Tuple:




       """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  )
       a		  =      0
       a		  =      Path(self.hparams.output_dir  )
       a		  =      self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
        a		  =      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:
        a		  =      config

       a		  =      ('''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:
        a		  =      AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,					cache_dir=__UpperCAmelCase ,					)
       else:
        a		  =      tokenizer
       a		  =      MODEL_MODES[mode]
       if model is None:
        a		  =      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:
        a		  =      model







      def       __lowerCAmelCase					(      self      :						Any ,					*__UpperCAmelCase      :						List[str] ,					**__UpperCAmelCase      :						str  )    ->str:




       """simple docstring"""


       a		  =      self.model_type.from_pretrained(*__UpperCAmelCase ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Optional[Any]:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.model
       a		  =      ['''bias''', '''LayerNorm.weight''']
       a		  =      [
           {
               '''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:
        a		  =      Adafactor(
            __UpperCAmelCase ,					lr=self.hparams.learning_rate ,					scale_parameter=__UpperCAmelCase ,					relative_step=__UpperCAmelCase  )

       else:
        a		  =      AdamW(
            __UpperCAmelCase ,					lr=self.hparams.learning_rate ,					eps=self.hparams.adam_epsilon  )
       a		  =      optimizer

       a		  =      self.get_lr_scheduler()

       return [optimizer], [scheduler]







      def       __lowerCAmelCase					(      self      :						Union[str, Any] ,					__UpperCAmelCase      :						Union[str, Any] ,					__UpperCAmelCase      :						Optional[Any]  )    ->Dict:




       """simple docstring"""


       return self.validation_step(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       return self.validation_end(__UpperCAmelCase  )







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




       """simple docstring"""


       a		  =      max(1 ,					self.hparams.gpus  )  # TODO: consider num_tpu_cores
       a		  =      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      :						List[Any] ,					__UpperCAmelCase      :						Dict  )    ->Optional[int]:




       """simple docstring"""


       if stage == "test":
        a		  =      len(self.test_dataloader().dataset  )
       else:
        a		  =      self.get_dataloader('''train''' ,					self.hparams.train_batch_size ,					shuffle=__UpperCAmelCase  )
        a		  =      len(self.train_dataloader().dataset  )







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						bool = False  )    ->str:




       """simple docstring"""


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







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




       """simple docstring"""


       return self.train_loader







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




       """simple docstring"""


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







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




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						Optional[int]  )    ->Optional[Any]:




       """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      :						Dict ,					__UpperCAmelCase      :						Dict[str, Any]  )    ->None:




       """simple docstring"""


       a		  =      self.output_dir.joinpath('''best_tfmr'''  )
       a		  =      self.step_count
       self.model.save_pretrained(__UpperCAmelCase  )
       self.tokenizer.save_pretrained(__UpperCAmelCase  )







      @staticmethod
      def       __lowerCAmelCase					(      __UpperCAmelCase      :						Dict ,					__UpperCAmelCase      :						int  )    ->int:




       """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 		lowercase_					(	pl.Callback   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						Optional[int]  )    ->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 		lowercase_					(	pl.Callback   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						Union[str, Any] ,					__UpperCAmelCase      :						List[Any]  )    ->Union[str, Any]:




       """simple docstring"""


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






class 		lowercase_					(	pl.Callback   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						int ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						Dict  )    ->int:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						pl.Trainer ,					__UpperCAmelCase      :						pl.LightningModule  )    ->Union[str, Any]:




       """simple docstring"""


       rank_zero_info('''***** Validation results *****'''  )
       a		  =      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      :						int ,					__UpperCAmelCase      :						pl.Trainer ,					__UpperCAmelCase      :						pl.LightningModule  )    ->Optional[int]:




       """simple docstring"""


       rank_zero_info('''***** Test results *****'''  )
       a		  =      trainer.callback_metrics
       # Log and save results to file
       a		  =      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 						_a    (      a :Union[str, Any]	,							a :int )							->			None:
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 parser.add_argument(
     '''--output_dir'''	,							default=str(Path(a ).parent / '''test_run''' / '''model_checkpoints''' )	,							type=a	,							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=a	,							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=a )
 parser.add_argument('''--max_grad_norm'''	,							dest='''gradient_clip_val'''	,							default=1.0	,							type=a	,							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=a	,							default=1	,							help='''Number of updates steps to accumulate before performing a backward/update pass.'''	,							)
 parser.add_argument('''--seed'''	,							type=a	,							default=42	,							help='''random seed for initialization''' )
 parser.add_argument(
     '''--data_dir'''	,							default=str(Path(a ).parent / '''test_run''' / '''dummy-train-data''' )	,							type=a	,							help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''	,							)

def 						_a    (      a :BaseTransformer	,							a :argparse.Namespace	,							a :Tuple=None	,							a :Any=True	,							a :List[str]=[]	,							a :List[Any]=None	,							a :Union[str, Any]=None	,							**a :Optional[Any]	,							)							->			List[str]:
 pl.seed_everything(args.seed )

 # init model
 a		  =      Path(model.hparams.output_dir )
 odir.mkdir(exist_ok=a )

 # add custom checkpoints
 if checkpoint_callback is None:
  a		  =      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(a )
 if logging_callback is None:
  a		  =      LoggingCallback()

 a		  =      {}

 if args.fpaa:
  a		  =      16

 if args.gpus > 1:
  a		  =      '''auto'''
  a		  =      '''ddp'''

 a		  =      args.accumulate_grad_batches
 a		  =      None
 a		  =      '''auto'''

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

 if args.do_train:
  trainer.fit(a )

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :       List[Any]									=  {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase      :       Dict									=  [
        """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

    lowercase      :       List[Any]									=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


