



"""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


lowerCamelCase__			=   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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







class 					_UpperCamelCase		( pl.LightningModule):






    def __init__(self						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__="base"						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      **lowerCamelCase__						,      ):


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

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

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






    def  A    (self						,      *lowerCamelCase__						,      **lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.model_type.from_pretrained(*lowercase_						,      **lowercase_   )






    def  A    (self   ):


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






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.model
      A__											=							['''bias''', '''LayerNorm.weight''']
      A__											=							[
          {
              '''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:
        A__											=							Adafactor(
            lowercase_						,      lr=self.hparams.learning_rate						,      scale_parameter=lowercase_						,      relative_step=lowercase_   )

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

      A__											=							self.get_lr_scheduler()

      return [optimizer], [scheduler]






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


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






    def  A    (self						,      lowerCamelCase__   ):


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






    def  A    (self   ):


      """simple docstring"""
      A__											=							max(1						,      self.hparams.gpus   )  # TODO: consider num_tpu_cores
      A__											=							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						,      lowerCamelCase__   ):


      """simple docstring"""

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






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = False   ):


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






    def  A    (self   ):


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






    def  A    (self   ):


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






    def  A    (self   ):


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






    def  A    (self						,      lowerCamelCase__   ):


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






    @pl.utilities.rank_zero_only
    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.output_dir.joinpath("""best_tfmr"""   )
      A__											=							self.step_count
      self.model.save_pretrained(lowercase_   )
      self.tokenizer.save_pretrained(lowercase_   )






    @staticmethod
    def  A    (lowerCamelCase__						,      lowerCamelCase__   ):


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







class 					_UpperCamelCase		( pl.Callback):






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


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






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


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






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


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






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Tuple		,				UpperCamelCase     :	Dict    ):
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      """--output_dir"""		,				default=lowercase_		,				type=lowercase_		,				required=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=lowercase_		,				type=lowercase_		,				required=lowercase_		,				help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""		,				)






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	str		,				UpperCamelCase     :	Dict		,				UpperCamelCase     :	List[Any]=None		,				UpperCamelCase     :	Optional[Any]=True		,				UpperCamelCase     :	List[Any]=[]		,				UpperCamelCase     :	Dict=None		,				UpperCamelCase     :	Any=None		,				**UpperCamelCase     :	Any		,				):
  pl.seed_everything(args.seed    )

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

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

  A__											=							{}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    A__											=							16
    A__											=							args.fpaa_opt_level

  if args.gpus > 1:
    A__											=							'''ddp'''

  A__											=							args.accumulate_grad_batches
  A__											=							extra_train_kwargs.get("""accelerator"""		,				lowercase_    )
  A__											=							extra_train_kwargs.get("""profiler"""		,				lowercase_    )

  A__											=							pl.Trainer.from_argparse_args(
      lowercase_		,				weights_summary=lowercase_		,				callbacks=[logging_callback] + extra_callbacks		,				logger=lowercase_		,				checkpoint_callback=lowercase_		,				**lowercase_		,				)

  if args.do_train:
    trainer.fit(lowercase_    )

  return trainer












"""simple docstring"""




def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	int = 1000    ):
  if (not isinstance(UpperCamelCase		,				UpperCamelCase    )) or (digit_position <= 0):
    raise ValueError("""Digit position must be a positive integer"""    )
  elif (not isinstance(UpperCamelCase		,				UpperCamelCase    )) or (precision < 0):
    raise ValueError("""Precision must be a nonnegative integer"""    )

  # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
  # accurate
  A__											=							(
      4 * _subsum(UpperCamelCase		,				1		,				UpperCamelCase    )
      - 2 * _subsum(UpperCamelCase		,				4		,				UpperCamelCase    )
      - _subsum(UpperCamelCase		,				5		,				UpperCamelCase    )
      - _subsum(UpperCamelCase		,				6		,				UpperCamelCase    )
  )

  # return the first hex digit of the fractional part of the result
  return hex(int((sum_result % 1) * 16    )    )[2:]






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	int		,				UpperCamelCase     :	int    ):
  A__											=							0.0
  for sum_index in range(digit_pos_to_extract + precision    ):
    A__											=							8 * sum_index + denominator_addend
    if sum_index < digit_pos_to_extract:
      # if the exponential term is an integer and we mod it by the denominator
      # before dividing, only the integer part of the sum will change;
      # the fractional part will not
      A__											=							pow(
          16		,				digit_pos_to_extract - 1 - sum_index		,				UpperCamelCase    )
    else:
      A__											=							pow(16		,				digit_pos_to_extract - 1 - sum_index    )
    total += exponential_term / denominator
  return total


if __name__ == "__main__":
      import doctest

      doctest.testmod()




