


"""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


__lowercase    =				logging.getLogger(__name__)

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

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






class 						_A  (  pl.LightningModule  ):







    """simple docstring"""




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

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






    def      __snake_case							( self    :   Optional[int]    ,			*__UpperCAmelCase    :   int    ,			**__UpperCAmelCase    :   Union[str, Any]):
      a						:		str											=      self.model_type.from_pretrained(*__UpperCAmelCase    ,			**__UpperCAmelCase)






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






    def      __snake_case							( self    :   Union[str, Any]):
      a						:		Optional[int]											=      self.model
      a						:		Optional[Any]											=      ["bias", "LayerNorm.weight"]
      a						:		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:
        a						:		Tuple											=      Adafactor(
            __UpperCAmelCase    ,			lr=self.hparams.learning_rate    ,			scale_parameter=__UpperCAmelCase    ,			relative_step=__UpperCAmelCase)

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

      a						:		Optional[Any]											=      self.get_lr_scheduler()

      return [optimizer], [scheduler]






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Optional[Any]):
      return self.validation_step(__UpperCAmelCase    ,			__UpperCAmelCase)






    def      __snake_case							( self    :   Any    ,			__UpperCAmelCase    :   Optional[int]):
      return self.validation_end(__UpperCAmelCase)






    def      __snake_case							( self    :   str):
      a						:		Any											=      max(1    ,			self.hparams.gpus)  # TODO: consider num_tpu_cores
      a						:		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      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   Any):
      if stage == "test":
        a						:		Optional[Any]											=      len(self.test_dataloader().dataset)
      else:
        a						:		str											=      self.get_dataloader("train"    ,			self.hparams.train_batch_size    ,			shuffle=__UpperCAmelCase)
        a						:		Dict											=      len(self.train_dataloader().dataset)






    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   bool = False):
      raise NotImplementedError("You must implement this for your task")






    def      __snake_case							( self    :   str):
      return self.train_loader






    def      __snake_case							( self    :   Optional[Any]):
      return self.get_dataloader("dev"    ,			self.hparams.eval_batch_size    ,			shuffle=__UpperCAmelCase)






    def      __snake_case							( self    :   Optional[int]):
      return self.get_dataloader("test"    ,			self.hparams.eval_batch_size    ,			shuffle=__UpperCAmelCase)






    def      __snake_case							( self    :   Optional[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      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   Dict[str, Any]):
      a						:		Union[str, Any]											=      self.output_dir.joinpath("best_tfmr")
      a						:		List[str]											=      self.step_count
      self.model.save_pretrained(__UpperCAmelCase)
      self.tokenizer.save_pretrained(__UpperCAmelCase)






    @staticmethod
    def      __snake_case							( __UpperCAmelCase    :   Optional[Any]    ,			__UpperCAmelCase    :   Union[str, Any]):
      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 						_A  (  pl.Callback  ):







    """simple docstring"""




    def      __snake_case							( self    :   Any    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   Any):
      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  ):







    """simple docstring"""




    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[Any]):
      # print(pl_module.model.rag)
      for name, param in pl_module.model.rag.named_parameters():
        if param.grad is None:
          print(__UpperCAmelCase)






class 						_A  (  pl.Callback  ):







    """simple docstring"""




    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   Optional[Any]    ,			__UpperCAmelCase    :   List[Any]):
      a						:		Optional[Any]											=      trainer.lr_schedulers[0]["scheduler"]
      a						:		Optional[int]											=      {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr())}
      pl_module.logger.log_metrics(__UpperCAmelCase)






    def      __snake_case							( self    :   int    ,			__UpperCAmelCase    :   pl.Trainer    ,			__UpperCAmelCase    :   pl.LightningModule):
      rank_zero_info("***** Validation results *****")
      a						:		List[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      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   pl.Trainer    ,			__UpperCAmelCase    :   pl.LightningModule):
      rank_zero_info("***** Test results *****")
      a						:		int											=      trainer.callback_metrics
      # Log and save results to file
      a						:		List[str]											=      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      (    A_  ,       A_	)->				None:
  '''simple docstring'''







  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 					lowercase      (    A_  ,       A_  ,       A_=None  ,       A_=True  ,       A_=[]  ,       A_=None  ,       A_=None  ,       **A_  ,       )->				List[Any]:
  '''simple docstring'''







  pl.seed_everything(args.seed	)

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

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

  a						:		Optional[Any]											=      {}

  if args.fpaa:
    a						:		Dict											=      16

  if args.gpus > 1:
    a						:		str											=      "auto"
    a						:		Any											=      "ddp"

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

  a						:		Tuple											=      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










"""simple docstring"""







import datasets


__lowercase    =				"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__lowercase    =				"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__lowercase    =				"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""


def 					lowercase      (    A_  ,       A_	)->				List[str]:
  '''simple docstring'''







  return (preds == labels).mean()







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION  )
class 						_A  (  datasets.Metric  ):







    """simple docstring"""




    def      __snake_case							( self    :   List[str]):
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,			citation=_CITATION    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
              {
                  "predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
                  "references": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
              })    ,			codebase_urls=[]    ,			reference_urls=[]    ,			format="numpy"    ,			)






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   Tuple):
      return {"accuracy": simple_accuracy(__UpperCAmelCase    ,			__UpperCAmelCase)}






