




"""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


_A													=      logging.getLogger(__name__)

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

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


class _lowerCamelCase    (					pl.LightningModule						):






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

         lowerCAmelCase__      :			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:
               lowerCAmelCase__      :			List[Any]   =	AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,     cache_dir=UpperCamelCase   ,     )
         else:
               lowerCAmelCase__      :			PreTrainedTokenizer   =	tokenizer
         lowerCAmelCase__      :			str   =	MODEL_MODES[mode]
         if model is None:
               lowerCAmelCase__      :			Optional[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:
               lowerCAmelCase__      :			int   =	model






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     *UpperCamelCase       :		str   ,     **UpperCamelCase       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	self.model_type.from_pretrained(*UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Dict:

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






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.model
         lowerCAmelCase__      :			Union[str, Any]   =	["""bias""", """LayerNorm.weight"""]
         lowerCAmelCase__      :			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:
               lowerCAmelCase__      :			Any   =	Adafactor(
                   UpperCamelCase   ,     lr=self.hparams.learning_rate   ,     scale_parameter=UpperCamelCase   ,     relative_step=UpperCamelCase   )

         else:
               lowerCAmelCase__      :			str   =	AdamW(
                   UpperCamelCase   ,     lr=self.hparams.learning_rate   ,     eps=self.hparams.adam_epsilon   )
         lowerCAmelCase__      :			Tuple   =	optimizer

         lowerCAmelCase__      :			Optional[int]   =	self.get_lr_scheduler()

         return [optimizer], [scheduler]






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Optional[Any]   )			->						Union[str, Any]:

         """simple docstring"""
         return self.validation_step(UpperCamelCase   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		Union[str, Any]   )			->						int:

         """simple docstring"""
         return self.validation_end(UpperCamelCase   )






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

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	max(1   ,     self.hparams.gpus   )  # TODO: consider num_tpu_cores
         lowerCAmelCase__      :			Union[str, 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   ,     UpperCamelCase       :		str   )			->						Union[str, Any]:

         """simple docstring"""
         if stage == "test":
               lowerCAmelCase__      :			int   =	len(self.test_dataloader().dataset   )
         else:
               lowerCAmelCase__      :			int   =	self.get_dataloader("""train"""   ,     self.hparams.train_batch_size   ,     shuffle=UpperCamelCase   )
               lowerCAmelCase__      :			Union[str, Any]   =	len(self.train_dataloader().dataset   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		bool = False   )			->						List[Any]:

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






   def 	_lowerCAmelCase   (		self       :		str   )			->						str:

         """simple docstring"""
         return self.train_loader






   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         return self.get_dataloader("""dev"""   ,     self.hparams.eval_batch_size   ,     shuffle=UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						List[str]:

         """simple docstring"""
         return self.get_dataloader("""test"""   ,     self.hparams.eval_batch_size   ,     shuffle=UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Optional[int]   )			->						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       :		Dict   ,     UpperCamelCase       :		Dict[str, Any]   )			->						None:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.output_dir.joinpath("""best_tfmr"""   )
         lowerCAmelCase__      :			List[Any]   =	self.step_count
         self.model.save_pretrained(UpperCamelCase   )
         self.tokenizer.save_pretrained(UpperCamelCase   )






   @staticmethod
   def 	_lowerCAmelCase   (		UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Any   )			->						Union[str, 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       :		Dict   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Optional[Any]   )			->						Any:

         """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       :		List[Any]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Dict   )			->						Tuple:

         """simple docstring"""
         # print(pl_module.model.rag)
         for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                     print(UpperCamelCase   )






class _lowerCamelCase    (					pl.Callback						):






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		Any   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	trainer.lr_schedulers[0]["""scheduler"""]
         lowerCAmelCase__      :			List[Any]   =	{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
         pl_module.logger.log_metrics(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		pl.Trainer   ,     UpperCamelCase       :		pl.LightningModule   )			->						Optional[int]:

         """simple docstring"""
         rank_zero_info("""***** Validation results *****"""   )
         lowerCAmelCase__      :			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 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		pl.Trainer   ,     UpperCamelCase       :		pl.LightningModule   )			->						Dict:

         """simple docstring"""
         rank_zero_info("""***** Test results *****"""   )
         lowerCAmelCase__      :			List[Any]   =	trainer.callback_metrics
         # Log and save results to file
         lowerCAmelCase__      :			Dict   =	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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    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(__UpperCAmelCase				).parent / """test_run""" / """model_checkpoints"""				)    ,		type=__UpperCAmelCase    ,		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=__UpperCAmelCase    ,		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=__UpperCAmelCase				)
      parser.add_argument("""--max_grad_norm"""    ,		dest="""gradient_clip_val"""    ,		default=1.0    ,		type=__UpperCAmelCase    ,		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=__UpperCAmelCase    ,		default=1    ,		help="""Number of updates steps to accumulate before performing a backward/update pass."""    ,		)
      parser.add_argument("""--seed"""    ,		type=__UpperCAmelCase    ,		default=42    ,		help="""random seed for initialization"""				)
      parser.add_argument(
          """--data_dir"""    ,		default=str(Path(__UpperCAmelCase				).parent / """test_run""" / """dummy-train-data"""				)    ,		type=__UpperCAmelCase    ,		help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""    ,		)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=None    ,		__UpperCAmelCase=True    ,		__UpperCAmelCase=[]    ,		__UpperCAmelCase=None    ,		__UpperCAmelCase=None    ,		**__UpperCAmelCase    ,		)						->    int:
      pl.seed_everything(args.seed				)

      # init model
      lowerCAmelCase__      :			Dict   =	Path(model.hparams.output_dir				)
      odir.mkdir(exist_ok=__UpperCAmelCase				)

      # add custom checkpoints
      if checkpoint_callback is None:
            lowerCAmelCase__      :			Tuple   =	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(__UpperCAmelCase				)
      if logging_callback is None:
            lowerCAmelCase__      :			int   =	LoggingCallback()

      lowerCAmelCase__      :			Union[str, Any]   =	{}

      if args.fpaa:
            lowerCAmelCase__      :			Optional[int]   =	16

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

      lowerCAmelCase__      :			Union[str, Any]   =	args.accumulate_grad_batches
      lowerCAmelCase__      :			Dict   =	None
      lowerCAmelCase__      :			Any   =	"""auto"""

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

      if args.do_train:
            trainer.fit(__UpperCAmelCase				)

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











"""simple docstring"""


from argparse import ArgumentParser

from . import BaseTransformersCLICommand



def 	lowercase_				(			__UpperCAmelCase				)						->    List[str]:
      return DownloadCommand(args.model    ,		args.cache_dir    ,		args.force    ,		args.trust_remote_code				)


class _lowerCamelCase    (					a_						):






   @staticmethod
   def 	_lowerCAmelCase   (		UpperCamelCase       :		ArgumentParser   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	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       :		Optional[int]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		bool   ,     UpperCamelCase       :		bool   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	model
         lowerCAmelCase__      :			Union[str, Any]   =	cache
         lowerCAmelCase__      :			Optional[int]   =	force
         lowerCAmelCase__      :			Dict   =	trust_remote_code






   def 	_lowerCAmelCase   (		self       :		int   )			->						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   )





