





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 	__snake_case					(      pl.LightningModule ):






    '''simple docstring'''





    def __init__( self    :		Union[str, Any]      ,    snake_case    :		Union[str, Any]      ,    snake_case    :		str=None      ,    snake_case    :		List[Any]="base"      ,    snake_case    :		int=None      ,    snake_case    :		List[Any]=None      ,    snake_case    :		Optional[Any]=None      ,    **snake_case    :		Optional[int]      ,    ):

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

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





    def 						A		( self    :		List[str]      ,    *snake_case    :		List[str]      ,    **snake_case    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					self.model_type.from_pretrained(*__UpperCamelCase      ,    **__UpperCamelCase				)





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





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





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					self.model
      __lowerCAmelCase								=					['''bias''', '''LayerNorm.weight''']
      __lowerCAmelCase								=					[
          {
              '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)],
              '''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								=					Adafactor(
            __UpperCamelCase      ,    lr=self.hparams.learning_rate      ,    scale_parameter=__UpperCamelCase      ,    relative_step=__UpperCamelCase				)

      else:
        __lowerCAmelCase								=					AdamW(
            __UpperCamelCase      ,    lr=self.hparams.learning_rate      ,    eps=self.hparams.adam_epsilon				)
      __lowerCAmelCase								=					optimizer

      __lowerCAmelCase								=					self.get_lr_scheduler()

      return [optimizer], [scheduler]





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		Dict      ,    snake_case    :		Any				):

      """simple docstring"""





      return self.validation_step(__UpperCamelCase      ,    __UpperCamelCase				)





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		Union[str, Any]				):

      """simple docstring"""





      return self.validation_end(__UpperCamelCase				)





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					max(1      ,    self.hparams.gpus				)  # TODO: consider num_tpu_cores
      __lowerCAmelCase								=					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    :		List[Any]      ,    snake_case    :		List[str]				):

      """simple docstring"""





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





    def 						A		( self    :		Union[str, Any]      ,    snake_case    :		Any      ,    snake_case    :		int      ,    snake_case    :		Optional[Any] = False				):

      """simple docstring"""





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





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      return self.train_loader





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





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





    def 						A		( self    :		Dict				):

      """simple docstring"""





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





    def 						A		( self    :		List[str]      ,    snake_case    :		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 						A		( self    :		Tuple      ,    snake_case    :		Union[str, Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					self.output_dir.joinpath('''best_tfmr'''				)
      __lowerCAmelCase								=					self.step_count
      self.model.save_pretrained(__UpperCamelCase				)
      self.tokenizer.save_pretrained(__UpperCamelCase				)





    @staticmethod
    def 						A		( snake_case    :		List[str]      ,    snake_case    :		Optional[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=''''''      ,    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 	__snake_case					(      pl.Callback ):






    '''simple docstring'''





    def 						A		( self    :		Dict      ,    snake_case    :		Union[str, Any]      ,    snake_case    :		Tuple				):

      """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 	__snake_case					(      pl.Callback ):






    '''simple docstring'''





    def 						A		( self    :		Any      ,    snake_case    :		Optional[int]      ,    snake_case    :		Any				):

      """simple docstring"""





      __lowerCAmelCase								=					trainer.lr_schedulers[0]['''scheduler''']
      __lowerCAmelCase								=					{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]      ,    snake_case    :		List[str]      ,    snake_case    :		Union[str, Any]				):

      """simple docstring"""





      rank_zero_info('''***** Validation results *****'''				)
      __lowerCAmelCase								=					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    :		Any      ,    snake_case    :		Union[str, Any]      ,    snake_case    :		str				):

      """simple docstring"""





      rank_zero_info('''***** Test results *****'''				)
      __lowerCAmelCase								=					trainer.callback_metrics
      # Log and save results to file
      __lowerCAmelCase								=					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        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any  ,				__lowercase:		List[str]):



  '''simple docstring'''






  parser.add_argument(
      '''--output_dir'''  ,				default=a__  ,				type=a__  ,				required=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=a__  ,				type=a__  ,				required=a__  ,				help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''  ,				)




def        __SCREAMING_SNAKE_CASE	(				__lowercase:		BaseTransformer  ,				__lowercase:		argparse.Namespace  ,				__lowercase:		List[str]=None  ,				__lowercase:		int=True  ,				__lowercase:		Any=None  ,				__lowercase:		Optional[Any]=[]  ,				__lowercase:		Union[str, Any]=None  ,				__lowercase:		Tuple=None  ,				**__lowercase:		Any  ,				):



  '''simple docstring'''






  pl.seed_everything(args.seed)

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

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

  __lowerCAmelCase								=					{}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    __lowerCAmelCase								=					16
    # train_params["amp_level"] = args.fp16_opt_level

  if args.gpus > 1:
    __lowerCAmelCase								=					'''auto'''  # "ddp"
    __lowerCAmelCase								=					'''ddp'''

  __lowerCAmelCase								=					args.accumulate_grad_batches
  __lowerCAmelCase								=					None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  __lowerCAmelCase								=					'''auto'''

  __lowerCAmelCase								=					pl.Trainer.from_argparse_args(
      a__  ,				weights_summary=a__  ,				callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]  ,				logger=a__  ,				**a__  ,				)

  if args.do_train:
    trainer.fit(a__)

  return trainer


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A							=       logging.get_logger(__name__)

_A							=       {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					Dict    =     """opt"""
 A__    :					int    =     ["""past_key_values"""]

 def __init__(   self  ,						__UpperCamelCase=5_0_2_7_2  ,						__UpperCamelCase=7_6_8  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase=3_0_7_2  ,						__UpperCamelCase=2_0_4_8  ,						__UpperCamelCase=True  ,						__UpperCamelCase=False  ,						__UpperCamelCase=None  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase="relu"  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=0.02  ,						__UpperCamelCase=True  ,						__UpperCamelCase=1  ,						__UpperCamelCase=2  ,						__UpperCamelCase=2  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            pad_token_id=__UpperCamelCase  ,						bos_token_id=__UpperCamelCase  ,						eos_token_id=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							vocab_size
        UpperCamelCase_											=							max_position_embeddings
        UpperCamelCase_											=							num_attention_heads
        UpperCamelCase_											=							word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        UpperCamelCase_											=							ffn_dim
        UpperCamelCase_											=							hidden_size
        UpperCamelCase_											=							num_hidden_layers
        UpperCamelCase_											=							dropout
        UpperCamelCase_											=							attention_dropout
        UpperCamelCase_											=							activation_function
        UpperCamelCase_											=							init_std
        UpperCamelCase_											=							layerdrop
        UpperCamelCase_											=							use_cache
        UpperCamelCase_											=							do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        UpperCamelCase_											=							enable_bias
        UpperCamelCase_											=							layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        UpperCamelCase_											=							_remove_final_layer_norm



