
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 		lowercase_       (       pl.LightningModule				):

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




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

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

 def lowerCamelCase_				(      self     ,	*__UpperCamelCase     ,	**__UpperCamelCase     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.model
        UpperCamelCase_							       =       ["""bias""", """LayerNorm.weight"""]
        UpperCamelCase_							       =       [
            {
                """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:
               UpperCamelCase_							       =       Adafactor(
                   __UpperCamelCase     ,	lr=self.hparams.learning_rate     ,	scale_parameter=__UpperCamelCase     ,	relative_step=__UpperCamelCase     )

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

        UpperCamelCase_							       =       self.get_lr_scheduler()

        return [optimizer], [scheduler]

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        return self.validation_step(__UpperCamelCase     ,	__UpperCamelCase     )

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        return self.validation_end(__UpperCamelCase     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase = False     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return self.train_loader

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """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 lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.output_dir.joinpath("""best_tfmr"""     )
        UpperCamelCase_							       =       self.step_count
        self.model.save_pretrained(__UpperCamelCase     )
        self.tokenizer.save_pretrained(__UpperCamelCase     )

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ,	__UpperCamelCase     ):




        """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=3_2     ,	type=__UpperCamelCase     )
        parser.add_argument("""--eval_batch_size"""     ,	default=3_2     ,	type=__UpperCamelCase     )
        parser.add_argument("""--adafactor"""     ,	action="""store_true"""     )



class 		lowercase_       (       pl.Callback				):

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """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 		lowercase_       (       pl.Callback				):

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        rank_zero_info("""***** Validation results *****"""     )
        UpperCamelCase_							       =       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 lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        rank_zero_info("""***** Test results *****"""     )
        UpperCamelCase_							       =       trainer.callback_metrics
        # Log and save results to file
        UpperCamelCase_							       =       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 				lowerCamelCase__				(						a__  :  Optional[Any]	,      a__  :  int     )		->	None:
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       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 				lowerCamelCase__				(						a__  :  BaseTransformer	,      a__  :  argparse.Namespace	,      a__  :  Optional[Any]=None	,      a__  :  Optional[int]=True	,      a__  :  str=None	,      a__  :  Dict=[]	,      a__  :  List[str]=None	,      a__  :  List[str]=None	,      **a__  :  Union[str, Any]	,      )		->	Any:
       pl.seed_everything(args.seed     )

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

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

       UpperCamelCase_							       =       {}

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

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

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

       UpperCamelCase_							       =       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





def 				lowerCamelCase__				(						a__  :  list     )		->	list:
       UpperCamelCase_							       =       1.3
       UpperCamelCase_							       =       len(a__     )
       UpperCamelCase_							       =       False

       while not completed:
              # Update the gap value for a next comb
              UpperCamelCase_							       =       int(gap / shrink_factor     )
              if gap <= 1:
                     UpperCamelCase_							       =       True

              UpperCamelCase_							       =       0
              while index + gap < len(a__     ):
                     if data[index] > data[index + gap]:
                            # Swap values
                            UpperCamelCase_       ,				UpperCamelCase_							       =       data[index + gap], data[index]
                            UpperCamelCase_							       =       False
                     index += 1

       return data


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      _A	  =					input('''Enter numbers separated by a comma:\n''').strip()
      _A	  =					[int(item) for item in user_input.split(''',''')]
      print(comb_sort(unsorted))



