



"""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


UpperCAmelCase						:		List[Any]			= logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

UpperCAmelCase						:		Any			= {
    "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						:		List[Any]			= {
    "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						:		List[Any]			= sorted(arg_to_scheduler.keys())
UpperCAmelCase						:		List[Any]			= "{" + ", ".join(arg_to_scheduler_choices) + "}"


class    SCREAMING_SNAKE_CASE__ (							pl.LightningModule   ):

    def __init__(				self			:				Dict ,  lowerCAmelCase_			:				argparse.Namespace ,  lowerCAmelCase_			:				List[Any]=None ,  lowerCAmelCase_			:				Optional[int]="base" ,  lowerCAmelCase_			:				Tuple=None ,  lowerCAmelCase_			:				Union[str, Any]=None ,  lowerCAmelCase_			:				int=None ,  **lowerCAmelCase_			:				List[str] ,  ):







      """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(lowerCAmelCase_)
      lowercase_								= 0
      lowercase_								= Path(self.hparams.output_dir)
      lowercase_								= self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
        lowercase_								= 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=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )
      else:
        lowercase_								= config

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

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

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  *lowerCAmelCase_			:				Union[str, Any] ,  **lowerCAmelCase_			:				str):







      """simple docstring"""







      lowercase_								= self.model_type.from_pretrained(*lowerCAmelCase_ ,  **lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      lowercase_								= self.model
      lowercase_								= ["""bias""", """LayerNorm.weight"""]
      lowercase_								= [
          {
              """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_								= Adafactor(
            lowerCAmelCase_ ,  lr=self.hparams.learning_rate ,  scale_parameter=lowerCAmelCase_ ,  relative_step=lowerCAmelCase_)

      else:
        lowercase_								= AdamW(
            lowerCAmelCase_ ,  lr=self.hparams.learning_rate ,  eps=self.hparams.adam_epsilon)
      lowercase_								= optimizer

      lowercase_								= self.get_lr_scheduler()

      return [optimizer], [scheduler]

    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      return self.validation_step(lowerCAmelCase_ ,  lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      return self.validation_end(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				bool = False):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return self.train_loader

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				Optional[int]):







      """simple docstring"""







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

    @pl.utilities.rank_zero_only
    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				Dict[str, Any]):







      """simple docstring"""







      lowercase_								= self.output_dir.joinpath("""best_tfmr""")
      lowercase_								= self.step_count
      self.model.save_pretrained(lowerCAmelCase_)
      self.tokenizer.save_pretrained(lowerCAmelCase_)




    @staticmethod
    def 					_UpperCAmelCase	(				lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      parser.add_argument(
          """--model_name_or_path""" ,  default=lowerCAmelCase_ ,  type=lowerCAmelCase_ ,  required=lowerCAmelCase_ ,  help="""Path to pretrained model or model identifier from huggingface.co/models""" ,  )
      parser.add_argument(
          """--config_name""" ,  default="""""" ,  type=lowerCAmelCase_ ,  help="""Pretrained config name or path if not the same as model_name""")
      parser.add_argument(
          """--tokenizer_name""" ,  default=lowerCAmelCase_ ,  type=lowerCAmelCase_ ,  help="""Pretrained tokenizer name or path if not the same as model_name""" ,  )
      parser.add_argument(
          """--cache_dir""" ,  default=str(Path(lowerCAmelCase_).parent / """test_run""" / """cache""") ,  type=lowerCAmelCase_ ,  help="""Where do you want to store the pre-trained models downloaded from huggingface.co""" ,  )
      parser.add_argument(
          """--encoder_layerdrop""" ,  type=lowerCAmelCase_ ,  help="""Encoder layer dropout probability (Optional). Goes into model.config""" ,  )
      parser.add_argument(
          """--decoder_layerdrop""" ,  type=lowerCAmelCase_ ,  help="""Decoder layer dropout probability (Optional). Goes into model.config""" ,  )
      parser.add_argument(
          """--dropout""" ,  type=lowerCAmelCase_ ,  help="""Dropout probability (Optional). Goes into model.config""" ,  )
      parser.add_argument(
          """--attention_dropout""" ,  type=lowerCAmelCase_ ,  help="""Attention dropout probability (Optional). Goes into model.config""" ,  )
      parser.add_argument("""--learning_rate""" ,  default=5E-5 ,  type=lowerCAmelCase_ ,  help="""The initial learning rate for Adam.""")
      parser.add_argument(
          """--lr_scheduler""" ,  default="""linear""" ,  choices=lowerCAmelCase_ ,  metavar=lowerCAmelCase_ ,  type=lowerCAmelCase_ ,  help="""Learning rate scheduler""" ,  )
      parser.add_argument("""--weight_decay""" ,  default=0.0 ,  type=lowerCAmelCase_ ,  help="""Weight decay if we apply some.""")
      parser.add_argument("""--adam_epsilon""" ,  default=1E-8 ,  type=lowerCAmelCase_ ,  help="""Epsilon for Adam optimizer.""")
      parser.add_argument("""--warmup_steps""" ,  default=0 ,  type=lowerCAmelCase_ ,  help="""Linear warmup over warmup_steps.""")
      parser.add_argument("""--num_workers""" ,  default=4 ,  type=lowerCAmelCase_ ,  help="""kwarg passed to DataLoader""")
      parser.add_argument("""--num_train_epochs""" ,  dest="""max_epochs""" ,  default=3 ,  type=lowerCAmelCase_)
      parser.add_argument("""--train_batch_size""" ,  default=3_2 ,  type=lowerCAmelCase_)
      parser.add_argument("""--eval_batch_size""" ,  default=3_2 ,  type=lowerCAmelCase_)
      parser.add_argument("""--adafactor""" ,  action="""store_true""")







class    SCREAMING_SNAKE_CASE__ (							pl.Callback   ):

    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				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    SCREAMING_SNAKE_CASE__ (							pl.Callback   ):

    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







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







class    SCREAMING_SNAKE_CASE__ (							pl.Callback   ):

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				str ,  lowerCAmelCase_			:				pl.Trainer ,  lowerCAmelCase_			:				pl.LightningModule):







      """simple docstring"""







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




    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				pl.Trainer ,  lowerCAmelCase_			:				pl.LightningModule):







      """simple docstring"""







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







def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						None:







  '''simple docstring'''


  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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=None ,  __lowerCAmelCase=True ,  __lowerCAmelCase=[] ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  **__lowerCAmelCase ,  ) ->						Optional[int]:







  '''simple docstring'''


  pl.seed_everything(args.seed							)

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

  # add custom checkpoints
  if checkpoint_callback is None:
    lowercase_								= 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_								= LoggingCallback()

  lowercase_								= {}

  if args.fpaa:
    lowercase_								= 16

  if args.gpus > 1:
    lowercase_								= """auto"""
    lowercase_								= """ddp"""

  lowercase_								= args.accumulate_grad_batches
  lowercase_								= None
  lowercase_								= """auto"""

  lowercase_								= 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"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						int:







  '''simple docstring'''


  while a != 0:
    lowercase_					,    lowercase_								= b % a, a
  return b


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						int:







  '''simple docstring'''


  if gcd(__lowerCAmelCase ,  __lowerCAmelCase							) != 1:
    lowercase_								= F'''mod inverse of {a!r} and {m!r} does not exist'''
    raise ValueError(__lowerCAmelCase							)
  lowercase_					,    lowercase_					,    lowercase_								= 1, 0, a
  lowercase_					,    lowercase_					,    lowercase_								= 0, 1, m
  while va != 0:
    lowercase_								= ua // va
    lowercase_					,    lowercase_					,    lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
  return ua % m

