

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 						snake_case  (		pl.LightningModule					):



   '''simple docstring'''






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

    _snake_case      :					str					=   ("""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:
     _snake_case      :					Optional[int]					=   AutoTokenizer.from_pretrained(
         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,						cache_dir=lowerCAmelCase			,						)
    else:
     _snake_case      :					PreTrainedTokenizer					=   tokenizer
    _snake_case      :					int					=   MODEL_MODES[mode]
    if model is None:
     _snake_case      :					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=lowerCAmelCase			,						)
    else:
     _snake_case      :					Tuple					=   model

   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						*lowerCAmelCase							:     Union[str, Any]			,						**lowerCAmelCase							:     Dict)			->			Any:

    """simple docstring"""







    _snake_case      :					List[str]					=   self.model_type.from_pretrained(*lowerCAmelCase			,						**lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     Optional[int])			->			List[Any]:

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     Tuple)			->			List[str]:

    """simple docstring"""







    _snake_case      :					Dict					=   self.model
    _snake_case      :					int					=   ["""bias""", """LayerNorm.weight"""]
    _snake_case      :					List[Any]					=   [
        {
            """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:
     _snake_case      :					Dict					=   Adafactor(
         lowerCAmelCase			,						lr=self.hparams.learning_rate			,						scale_parameter=lowerCAmelCase			,						relative_step=lowerCAmelCase)

    else:
     _snake_case      :					int					=   AdamW(
         lowerCAmelCase			,						lr=self.hparams.learning_rate			,						eps=self.hparams.adam_epsilon)
    _snake_case      :					List[str]					=   optimizer

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

    return [optimizer], [scheduler]

   def        UpperCamelCase_					(   self							:     Optional[int]			,						lowerCAmelCase							:     Tuple			,						lowerCAmelCase							:     Optional[Any])			->			List[Any]:

    """simple docstring"""







    return self.validation_step(lowerCAmelCase			,						lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Dict)			->			int:

    """simple docstring"""







    return self.validation_end(lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     List[str])			->			int:

    """simple docstring"""







    _snake_case      :					Dict					=   max(1			,						self.hparams.gpus)  # TODO: consider num_tpu_cores
    _snake_case      :					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        UpperCamelCase_					(   self							:     List[str]			,						lowerCAmelCase							:     Optional[int])			->			Tuple:

    """simple docstring"""







    if mode == "test":
     _snake_case      :					Optional[Any]					=   len(self.test_dataloader().dataset)
    else:
     _snake_case      :					Tuple					=   self.get_dataloader("""train"""			,						self.hparams.train_batch_size			,						shuffle=lowerCAmelCase)
     _snake_case      :					Tuple					=   len(self.train_dataloader().dataset)

   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     bool = False)			->			str:

    """simple docstring"""







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

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

    """simple docstring"""







    return self.train_loader

   def        UpperCamelCase_					(   self							:     Dict)			->			Optional[int]:

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     Optional[Any])			->			Tuple:

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     Optional[Any])			->			Tuple:

    """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							:     Dict			,						lowerCAmelCase							:     Dict[str, Any])			->			None:

    """simple docstring"""







    _snake_case      :					List[Any]					=   self.output_dir.joinpath("""best_tfmr""")
    _snake_case      :					Union[str, Any]					=   self.step_count
    self.model.save_pretrained(lowerCAmelCase)
    self.tokenizer.save_pretrained(lowerCAmelCase)


   @staticmethod
   def        UpperCamelCase_					(   lowerCAmelCase							:     int			,						lowerCAmelCase							:     Any)			->			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=""""""			,						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=32			,						type=lowerCAmelCase)
    parser.add_argument("""--eval_batch_size"""			,						default=32			,						type=lowerCAmelCase)
    parser.add_argument("""--adafactor"""			,						action="""store_true""")







class 						snake_case  (		pl.Callback					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     List[str]			,						lowerCAmelCase							:     Union[str, Any]			,						lowerCAmelCase							:     Any)			->			Optional[int]:

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     List[str]			,						lowerCAmelCase							:     pl.Trainer			,						lowerCAmelCase							:     pl.LightningModule)			->			Union[str, Any]:

    """simple docstring"""







    rank_zero_info("""***** Validation results *****""")
    _snake_case      :					Any					=   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							:     str			,						lowerCAmelCase							:     pl.Trainer			,						lowerCAmelCase							:     pl.LightningModule)			->			Dict:

    """simple docstring"""







    rank_zero_info("""***** Test results *****""")
    _snake_case      :					Dict					=   trainer.callback_metrics
    # Log and save results to file
    _snake_case      :					Any					=   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  lowercase    (				SCREAMING_SNAKE_CASE__    :		str			,    SCREAMING_SNAKE_CASE__    :		Tuple			)					->       None:
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 parser.add_argument(
     """--output_dir"""			,    default=SCREAMING_SNAKE_CASE__			,    type=SCREAMING_SNAKE_CASE__			,    required=SCREAMING_SNAKE_CASE__			,    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=SCREAMING_SNAKE_CASE__			,    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=SCREAMING_SNAKE_CASE__			)
 parser.add_argument("""--max_grad_norm"""			,    dest="""gradient_clip_val"""			,    default=1.0			,    type=SCREAMING_SNAKE_CASE__			,    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=SCREAMING_SNAKE_CASE__			,    default=1			,    help="""Number of updates steps to accumulate before performing a backward/update pass."""			,    )
 parser.add_argument("""--seed"""			,    type=SCREAMING_SNAKE_CASE__			,    default=42			,    help="""random seed for initialization"""			)
 parser.add_argument(
     """--data_dir"""			,    default=SCREAMING_SNAKE_CASE__			,    type=SCREAMING_SNAKE_CASE__			,    required=SCREAMING_SNAKE_CASE__			,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""			,    )



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		BaseTransformer			,    SCREAMING_SNAKE_CASE__    :		argparse.Namespace			,    SCREAMING_SNAKE_CASE__    :		Any=None			,    SCREAMING_SNAKE_CASE__    :		Union[str, Any]=True			,    SCREAMING_SNAKE_CASE__    :		int=[]			,    SCREAMING_SNAKE_CASE__    :		List[Any]=None			,    SCREAMING_SNAKE_CASE__    :		List[Any]=None			,    **SCREAMING_SNAKE_CASE__    :		int			,    )					->       List[Any]:
 pl.seed_everything(args.seed			)

 # init model
 _snake_case      :					Optional[int]					=   Path(model.hparams.output_dir			)
 odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)

 # add custom checkpoints
 if checkpoint_callback is None:
  _snake_case      :					str					=   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(SCREAMING_SNAKE_CASE__			)
 if logging_callback is None:
  _snake_case      :					Dict					=   LoggingCallback()

 _snake_case      :					str					=   {}

 # TODO: remove with PyTorch 1.6 since pl uses native amp
 if args.fpaa:
  _snake_case      :					List[str]					=   16
  _snake_case      :					Optional[int]					=   args.fpaa_opt_level

 if args.gpus > 1:
  _snake_case      :					Optional[int]					=   """ddp"""

 _snake_case      :					Tuple					=   args.accumulate_grad_batches
 _snake_case      :					Optional[Any]					=   extra_train_kwargs.get("""accelerator"""			,    SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Optional[int]					=   extra_train_kwargs.get("""profiler"""			,    SCREAMING_SNAKE_CASE__			)

 _snake_case      :					Any					=   pl.Trainer.from_argparse_args(
     SCREAMING_SNAKE_CASE__			,    weights_summary=SCREAMING_SNAKE_CASE__			,    callbacks=[logging_callback] + extra_callbacks			,    logger=SCREAMING_SNAKE_CASE__			,    checkpoint_callback=SCREAMING_SNAKE_CASE__			,    **SCREAMING_SNAKE_CASE__			,    )

 if args.do_train:
  trainer.fit(SCREAMING_SNAKE_CASE__			)

 return trainer





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			)					->       Optional[int]:
 _snake_case      :					Any					=   [701, 301, 132, 57, 23, 10, 4, 1]
 for gap in gaps:
  for i in range(SCREAMING_SNAKE_CASE__			,    len(SCREAMING_SNAKE_CASE__			)			):
   _snake_case      :					List[Any]					=   collection[i]
   _snake_case      :					List[str]					=   i
   while j >= gap and collection[j - gap] > insert_value:
    _snake_case      :					int					=   collection[j - gap]
    j -= gap
   if j != i:
    _snake_case      :					Tuple					=   insert_value
 return collection


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		input("""Enter numbers separated by a comma:\n""").strip()
  a__      =		[int(item) for item in user_input.split(""",""")]
  print(shell_sort(unsorted))
