






"""simple docstring"""




import sys

sys.set_int_max_str_digits(0)  # type: ignore






def     lowerCamelCase__				(   __lowerCamelCase      :					int		):
  __UpperCAmelCase							:     Optional[Any]									=  [0] * 11
  __UpperCAmelCase							:     Tuple									=  [0] * 11

  # mark last 9 numbers
  for _ in range(9		):
    __UpperCAmelCase							:     Any									=  1
    __UpperCAmelCase							:     str									=  number // 10
  # flag
  __UpperCAmelCase							:     List[Any]									=  True

  # check last 9 numbers for pandigitality

  for x in range(9		):
    if not check_last[x + 1]:
      __UpperCAmelCase							:     Any									=  False
  if not f:
    return f

  # mark first 9 numbers
  __UpperCAmelCase							:     Optional[int]									=  int(str(_lowerCamelCase		)[:9]		)

  for _ in range(9		):
    __UpperCAmelCase							:     Optional[int]									=  1
    __UpperCAmelCase							:     int									=  number // 10

  # check first 9 numbers for pandigitality

  for x in range(9		):
    if not check_front[x + 1]:
      __UpperCAmelCase							:     Dict									=  False
  return f






def     lowerCamelCase__				(   __lowerCamelCase      :					int		):
  __UpperCAmelCase							:     List[str]									=  [0] * 11

  # mark last 9 numbers
  for _ in range(9		):
    __UpperCAmelCase							:     Optional[Any]									=  1
    __UpperCAmelCase							:     List[Any]									=  number // 10
  # flag
  __UpperCAmelCase							:     Optional[Any]									=  True

  # check last 9 numbers for pandigitality

  for x in range(9		):
    if not check_last[x + 1]:
      __UpperCAmelCase							:     int									=  False
  return f






def     lowerCamelCase__				(   ):
  __UpperCAmelCase							:     Dict									=  1
  __UpperCAmelCase							:     Dict									=  1
  __UpperCAmelCase							:     Union[str, Any]									=  2
  # temporary Fibonacci numbers

  __UpperCAmelCase							:     int									=  1
  __UpperCAmelCase							:     int									=  1
  __UpperCAmelCase							:     int									=  2
  # temporary Fibonacci numbers mod 1e9

  # mod m=1e9, done for fast optimisation
  __UpperCAmelCase							:     Optional[Any]									=  [0] * 1000000
  __UpperCAmelCase							:     int									=  1000000000

  for x in range(1000000		):
    __UpperCAmelCase							:     Tuple									=  (aa + ba) % m
    __UpperCAmelCase							:     Union[str, Any]									=  ba % m
    __UpperCAmelCase							:     List[str]									=  ca % m
    if checka(_lowerCamelCase		):
      __UpperCAmelCase							:     Tuple									=  1

  for x in range(1000000		):
    __UpperCAmelCase							:     str									=  a + b
    __UpperCAmelCase							:     int									=  b
    __UpperCAmelCase							:     List[str]									=  c
    # perform check only if in tocheck
    if tocheck[x + 3] and check(_lowerCamelCase		):
      return x + 3  # first 2 already done
  return -1


if __name__ == "__main__":
       print(f"""{solution() = }""")










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		:	str      					=			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

a		:	Any      					=			{
    "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		:	List[str]      					=			{
    "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		:	int      					=			sorted(arg_to_scheduler.keys())
a		:	Dict      					=			"{" + ", ".join(arg_to_scheduler_choices) + "}"
class    a   (			pl.LightningModule       ):



      """simple docstring"""


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

        __UpperCAmelCase							:     Optional[int]									=  ("""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:
          __UpperCAmelCase							:     Tuple									=  AutoTokenizer.from_pretrained(
              self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,				cache_dir=__lowercase		,				)
        else:
          __UpperCAmelCase							:     PreTrainedTokenizer									=  tokenizer
        __UpperCAmelCase							:     Dict									=  MODEL_MODES[mode]
        if model is None:
          __UpperCAmelCase							:     Dict									=  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:
          __UpperCAmelCase							:     Any									=  model
      def      UpperCAmelCase				(				self :      Optional[Any]		,				*__lowercase :      Optional[Any]		,				**__lowercase :      Optional[Any] )							->				List[Any]:
        __UpperCAmelCase							:     List[Any]									=  self.model_type.from_pretrained(*__lowercase		,				**__lowercase )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Dict:
        __UpperCAmelCase							:     Union[str, Any]									=  arg_to_scheduler[self.hparams.lr_scheduler]
        __UpperCAmelCase							:     Optional[int]									=  get_schedule_func(
            self.opt		,				num_warmup_steps=self.hparams.warmup_steps		,				num_training_steps=self.total_steps() )
        __UpperCAmelCase							:     int									=  {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
        return scheduler
      def      UpperCAmelCase				(				self :      Optional[int] )							->				int:
        __UpperCAmelCase							:     List[str]									=  self.model
        __UpperCAmelCase							:     List[str]									=  ["""bias""", """LayerNorm.weight"""]
        __UpperCAmelCase							:     List[str]									=  [
            {
                """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							:     List[Any]									=  Adafactor(
              __lowercase		,				lr=self.hparams.learning_rate		,				scale_parameter=__lowercase		,				relative_step=__lowercase )

        else:
          __UpperCAmelCase							:     Optional[int]									=  AdamW(
              __lowercase		,				lr=self.hparams.learning_rate		,				eps=self.hparams.adam_epsilon )
        __UpperCAmelCase							:     Dict									=  optimizer

        __UpperCAmelCase							:     Dict									=  self.get_lr_scheduler()

        return [optimizer], [scheduler]
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      List[Any]		,				__lowercase :      Tuple )							->				List[Any]:
        return self.validation_step(__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      Optional[int] )							->				Optional[int]:
        return self.validation_end(__lowercase )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				int:
        __UpperCAmelCase							:     Optional[Any]									=  max(1		,				self.hparams.gpus )  # TODO: consider num_tpu_cores
        __UpperCAmelCase							:     int									=  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 :      Any		,				__lowercase :      Tuple )							->				Dict:
        if stage == "test":
          __UpperCAmelCase							:     str									=  len(self.test_dataloader().dataset )
        else:
          __UpperCAmelCase							:     Any									=  self.get_dataloader("""train"""		,				self.hparams.train_batch_size		,				shuffle=__lowercase )
          __UpperCAmelCase							:     List[Any]									=  len(self.train_dataloader().dataset )
      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      str		,				__lowercase :      int		,				__lowercase :      bool = False )							->				Optional[int]:
        raise NotImplementedError("""You must implement this for your task""" )
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Dict:
        return self.train_loader
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Tuple:
        return self.get_dataloader("""dev"""		,				self.hparams.eval_batch_size		,				shuffle=__lowercase )
      def      UpperCAmelCase				(				self :      Dict )							->				List[Any]:
        return self.get_dataloader("""test"""		,				self.hparams.eval_batch_size		,				shuffle=__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      Optional[int] )							->				List[Any]:
        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      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Dict[str, Any] )							->				None:
        __UpperCAmelCase							:     List[str]									=  self.output_dir.joinpath("""best_tfmr""" )
        __UpperCAmelCase							:     str									=  self.step_count
        self.model.save_pretrained(__lowercase )
        self.tokenizer.save_pretrained(__lowercase )




      @staticmethod
      def      UpperCAmelCase				(				__lowercase :      List[str]		,				__lowercase :      Optional[int] )							->				Optional[int]:
        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=32		,				type=__lowercase )
        parser.add_argument("""--eval_batch_size"""		,				default=32		,				type=__lowercase )
        parser.add_argument("""--adafactor"""		,				action="""store_true""" )


class    a   (			pl.Callback       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      int		,				__lowercase :      Tuple		,				__lowercase :      str )							->				Optional[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      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      int		,				__lowercase :      Union[str, Any] )							->				Optional[int]:
        __UpperCAmelCase							:     Union[str, Any]									=  trainer.lr_schedulers[0]["""scheduler"""]
        __UpperCAmelCase							:     Optional[Any]									=  {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
        pl_module.logger.log_metrics(__lowercase )
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      pl.Trainer		,				__lowercase :      pl.LightningModule )							->				Dict:
        rank_zero_info("""***** Validation results *****""" )
        __UpperCAmelCase							:     int									=  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      UpperCAmelCase				(				self :      Any		,				__lowercase :      pl.Trainer		,				__lowercase :      pl.LightningModule )							->				Any:
        rank_zero_info("""***** Test results *****""" )
        __UpperCAmelCase							:     List[str]									=  trainer.callback_metrics
        # Log and save results to file
        __UpperCAmelCase							:     Dict									=  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     lowerCamelCase__				(   __lowerCamelCase      :					Optional[Any]  ,		__lowerCamelCase      :					List[Any]		):
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      """--output_dir"""  ,		default=__lowerCamelCase  ,		type=__lowerCamelCase  ,		required=__lowerCamelCase  ,		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=__lowerCamelCase  ,		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=__lowerCamelCase		)
  parser.add_argument("""--max_grad_norm"""  ,		dest="""gradient_clip_val"""  ,		default=1.0  ,		type=__lowerCamelCase  ,		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=__lowerCamelCase  ,		default=1  ,		help="""Number of updates steps to accumulate before performing a backward/update pass."""  ,		)
  parser.add_argument("""--seed"""  ,		type=__lowerCamelCase  ,		default=42  ,		help="""random seed for initialization"""		)
  parser.add_argument(
      """--data_dir"""  ,		default=__lowerCamelCase  ,		type=__lowerCamelCase  ,		required=__lowerCamelCase  ,		help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""  ,		)







def     lowerCamelCase__				(   __lowerCamelCase      :					BaseTransformer  ,		__lowerCamelCase      :					argparse.Namespace  ,		__lowerCamelCase      :					Any=None  ,		__lowerCamelCase      :					Optional[Any]=True  ,		__lowerCamelCase      :					Tuple=None  ,		__lowerCamelCase      :					Optional[int]=[]  ,		__lowerCamelCase      :					int=None  ,		__lowerCamelCase      :					List[str]=None  ,		**__lowerCamelCase      :					List[Any]  ,		):
  pl.seed_everything(args.seed		)

  # init model
  __UpperCAmelCase							:     Optional[Any]									=  Path(model.hparams.output_dir		)
  odir.mkdir(exist_ok=__lowerCamelCase		)

  # add custom checkpoints
  if checkpoint_callback is None:
    __UpperCAmelCase							:     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(__lowerCamelCase		)
  if logging_callback is None:
    __UpperCAmelCase							:     int									=  LoggingCallback()

  __UpperCAmelCase							:     int									=  {}

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

  if args.gpus > 1:
    __UpperCAmelCase							:     List[str]									=  """auto"""  # "ddp"
    __UpperCAmelCase							:     Union[str, Any]									=  """ddp"""

  __UpperCAmelCase							:     Optional[int]									=  args.accumulate_grad_batches
  __UpperCAmelCase							:     Dict									=  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  __UpperCAmelCase							:     Optional[int]									=  """auto"""

  __UpperCAmelCase							:     Optional[Any]									=  pl.Trainer.from_argparse_args(
      __lowerCamelCase  ,		weights_summary=__lowerCamelCase  ,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]  ,		logger=__lowerCamelCase  ,		**__lowerCamelCase  ,		)

  if args.do_train:
    trainer.fit(__lowerCamelCase		)

  return trainer


