






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 			SCREAMING_SNAKE_CASE      (      pl.LightningModule       ):
     '''simple docstring'''


     def __init__(					self							:						Any       ,			UpperCAmelCase_							:						argparse.Namespace       ,			UpperCAmelCase_							:						str=None       ,			UpperCAmelCase_							:						Optional[int]="base"       ,			UpperCAmelCase_							:						List[str]=None       ,			UpperCAmelCase_							:						Optional[Any]=None       ,			UpperCAmelCase_							:						List[str]=None       ,			**UpperCAmelCase_							:						List[Any]       ,			):
       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_   )
       SCREAMING_SNAKE_CASE							:   int						    =     0
       SCREAMING_SNAKE_CASE							:   List[str]						    =     Path(self.hparams.output_dir   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
         SCREAMING_SNAKE_CASE							:   Dict						    =     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:
         SCREAMING_SNAKE_CASE							:   PretrainedConfig						    =     config

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     ("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:
         SCREAMING_SNAKE_CASE							:   Any						    =     AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,			cache_dir=UpperCAmelCase_       ,			)
       else:
         SCREAMING_SNAKE_CASE							:   PreTrainedTokenizer						    =     tokenizer
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     MODEL_MODES[mode]
       if model is None:
         SCREAMING_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:
         SCREAMING_SNAKE_CASE							:   int						    =     model


     def 					_A							(					self							:						Optional[Any]       ,			*UpperCAmelCase_							:						Optional[Any]       ,			**UpperCAmelCase_							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.model_type.from_pretrained(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


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


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     ["bias", "LayerNorm.weight"]
       SCREAMING_SNAKE_CASE							:   List[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:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     Adafactor(
             UpperCAmelCase_       ,			lr=self.hparams.learning_rate       ,			scale_parameter=UpperCAmelCase_       ,			relative_step=UpperCAmelCase_   )

       else:
         SCREAMING_SNAKE_CASE							:   Dict						    =     AdamW(
             UpperCAmelCase_       ,			lr=self.hparams.learning_rate       ,			eps=self.hparams.adam_epsilon   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     optimizer

       SCREAMING_SNAKE_CASE							:   Any						    =     self.get_lr_scheduler()

       return [optimizer], [scheduler]


     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						str   ):
       return self.validation_step(UpperCAmelCase_       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						int   ):
       return self.validation_end(UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     max(1       ,			self.hparams.gpus   )  # TODO: consider num_tpu_cores
       SCREAMING_SNAKE_CASE							:   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 					_A							(					self							:						int       ,			UpperCAmelCase_							:						List[str]   ):
       if stage == "test":
         SCREAMING_SNAKE_CASE							:   Any						    =     len(self.test_dataloader().dataset   )
       else:
         SCREAMING_SNAKE_CASE							:   Any						    =     self.get_dataloader("train"       ,			self.hparams.train_batch_size       ,			shuffle=UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     len(self.train_dataloader().dataset   )


     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						bool = False   ):
       raise NotImplementedError("You must implement this for your task"   )


     def 					_A							(					self							:						List[str]   ):
       return self.train_loader


     def 					_A							(					self							:						List[Any]   ):
       return self.get_dataloader("dev"       ,			self.hparams.eval_batch_size       ,			shuffle=UpperCAmelCase_   )


     def 					_A							(					self							:						Union[str, Any]   ):
       return self.get_dataloader("test"       ,			self.hparams.eval_batch_size       ,			shuffle=UpperCAmelCase_   )


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						List[str]   ):
       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 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Dict[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.output_dir.joinpath("best_tfmr"   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.step_count
       self.model.save_pretrained(UpperCAmelCase_   )
       self.tokenizer.save_pretrained(UpperCAmelCase_   )






     @staticmethod
     def 					_A							(					UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						Dict   ):
       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 			SCREAMING_SNAKE_CASE      (      pl.Callback       ):
     '''simple docstring'''


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Optional[int]   ):
       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 			SCREAMING_SNAKE_CASE      (      pl.Callback       ):
     '''simple docstring'''


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						str   ):
       # print(pl_module.model.rag)
       for name, param in pl_module.model.rag.named_parameters():
         if param.grad is None:
           print(UpperCAmelCase_   )





class 			SCREAMING_SNAKE_CASE      (      pl.Callback       ):
     '''simple docstring'''


     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     trainer.lr_schedulers[0]["scheduler"]
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
       pl_module.logger.log_metrics(UpperCAmelCase_   )


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						pl.Trainer       ,			UpperCAmelCase_							:						pl.LightningModule   ):
       rank_zero_info("***** Validation results *****"   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     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 					_A							(					self							:						Dict       ,			UpperCAmelCase_							:						pl.Trainer       ,			UpperCAmelCase_							:						pl.LightningModule   ):
       rank_zero_info("***** Test results *****"   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     trainer.callback_metrics
       # Log and save results to file
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     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    ,  lowercase					):

  """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    ,  lowercase    ,  lowercase=None    ,  lowercase=True    ,  lowercase=[]    ,  lowercase=None    ,  lowercase=None    ,  **lowercase    ,  ):

  """simple docstring"""






  pl.seed_everything(args.seed					)

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

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

  SCREAMING_SNAKE_CASE							:   List[Any]						    =     {}

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

  if args.gpus > 1:
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     "auto"
    SCREAMING_SNAKE_CASE							:   Tuple						    =     "ddp"

  SCREAMING_SNAKE_CASE							:   str						    =     args.accumulate_grad_batches
  SCREAMING_SNAKE_CASE							:   Tuple						    =     None
  SCREAMING_SNAKE_CASE							:   str						    =     "auto"

  SCREAMING_SNAKE_CASE							:   Tuple						    =     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









from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code		)




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       @staticmethod
       def 			a		(		_UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							parser.add_parser("""download"""   )
              download_parser.add_argument(
                  """--cache-dir"""      , type=_UpperCamelCase      , default=_UpperCamelCase      , help="""Path to location to store the models"""   )
              download_parser.add_argument(
                  """--force"""      , action="""store_true"""      , help="""Force the model to be download even if already in cache-dir"""   )
              download_parser.add_argument(
                  """--trust-remote-code"""      , action="""store_true"""      , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""      , )
              download_parser.add_argument("""model"""      , type=_UpperCamelCase      , help="""Name of the model to download"""   )
              download_parser.set_defaults(func=_UpperCamelCase   )


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							model
              __snake_case       	=							cache
              __snake_case       	=							force
              __snake_case       	=							trust_remote_code






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






              """simple docstring"""


              from ..models.auto import AutoModel, AutoTokenizer

              AutoModel.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )
              AutoTokenizer.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )

