




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    : List[Any]		= logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_lowerCAmelCase    : List[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
_lowerCAmelCase    : 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
}
_lowerCAmelCase    : Union[str, Any]		= sorted(arg_to_scheduler.keys())
_lowerCAmelCase    : List[str]		= "{" + ", ".join(arg_to_scheduler_choices) + "}"
class       _UpperCamelCase   (					pl.LightningModule		):
      def __init__(      self :List[Any]			,   lowerCamelCase :argparse.Namespace			,   lowerCamelCase :Optional[Any]=None			,   lowerCamelCase :Tuple="base"			,   lowerCamelCase :Any=None			,   lowerCamelCase :str=None			,   lowerCamelCase :Tuple=None			,   **lowerCamelCase :Union[str, Any]			,   )  ->      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(_a   )
       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=_a			,   **_a			,   )
       else:
        UpperCAmelCase__							=    config

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

       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=_a			,   )
       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=_a			,   )
       else:
        UpperCAmelCase__							=    model
      def 						UpperCAmelCase_    (      self :Dict			,   *lowerCamelCase :Any			,   **lowerCamelCase :Optional[Any]   )  ->      Dict:
       UpperCAmelCase__							=    self.model_type.from_pretrained(*_a			,   **_a   )
      def 						UpperCAmelCase_    (      self :Any   )  ->      int:
       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 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      List[str]:
       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(
            _a			,   lr=self.hparams.learning_rate			,   scale_parameter=_a			,   relative_step=_a   )

       else:
        UpperCAmelCase__							=    AdamW(
            _a			,   lr=self.hparams.learning_rate			,   eps=self.hparams.adam_epsilon   )
       UpperCAmelCase__							=    optimizer

       UpperCAmelCase__							=    self.get_lr_scheduler()

       return [optimizer], [scheduler]
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Tuple			,   lowerCamelCase :Optional[int]   )  ->      Optional[Any]:
       return self.validation_step(_a			,   _a   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Dict   )  ->      List[Any]:
       return self.validation_end(_a   )
      def 						UpperCAmelCase_    (      self :int   )  ->      int:
       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 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :Optional[Any]   )  ->      Dict:
       if mode == "test":
        UpperCAmelCase__							=    len(self.test_dataloader().dataset   )
       else:
        UpperCAmelCase__							=    self.get_dataloader("train"			,   self.hparams.train_batch_size			,   shuffle=_a   )
        UpperCAmelCase__							=    len(self.train_dataloader().dataset   )
      def 						UpperCAmelCase_    (      self :List[str]			,   lowerCamelCase :str			,   lowerCamelCase :int			,   lowerCamelCase :bool = False   )  ->      Any:
       raise NotImplementedError("You must implement this for your task"   )
      def 						UpperCAmelCase_    (      self :Optional[int]   )  ->      List[str]:
       return self.train_loader
      def 						UpperCAmelCase_    (      self :str   )  ->      Optional[int]:
       return self.get_dataloader("dev"			,   self.hparams.eval_batch_size			,   shuffle=_a   )
      def 						UpperCAmelCase_    (      self :Optional[int]   )  ->      Any:
       return self.get_dataloader("test"			,   self.hparams.eval_batch_size			,   shuffle=_a   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :int   )  ->      Optional[int]:
       return os.path.join(
           self.hparams.data_dir			,   "cached_{}_{}_{}".format(
               _a			,   list(filter(_a			,   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]   )  ->      Optional[int]:
       UpperCAmelCase__							=    self.output_dir.joinpath("best_tfmr"   )
       UpperCAmelCase__							=    self.step_count
       self.model.save_pretrained(_a   )
       self.tokenizer.save_pretrained(_a   )







      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :Optional[int]			,   lowerCamelCase :Union[str, Any]   )  ->      List[Any]:
       parser.add_argument(
           "--model_name_or_path"			,   default=_a			,   type=_a			,   required=_a			,   help="Path to pretrained model or model identifier from huggingface.co/models"			,   )
       parser.add_argument(
           "--config_name"			,   default=""			,   type=_a			,   help="Pretrained config name or path if not the same as model_name"   )
       parser.add_argument(
           "--tokenizer_name"			,   default=_a			,   type=_a			,   help="Pretrained tokenizer name or path if not the same as model_name"			,   )
       parser.add_argument(
           "--cache_dir"			,   default=""			,   type=_a			,   help="Where do you want to store the pre-trained models downloaded from huggingface.co"			,   )
       parser.add_argument(
           "--encoder_layerdrop"			,   type=_a			,   help="Encoder layer dropout probability (Optional). Goes into model.config"			,   )
       parser.add_argument(
           "--decoder_layerdrop"			,   type=_a			,   help="Decoder layer dropout probability (Optional). Goes into model.config"			,   )
       parser.add_argument(
           "--dropout"			,   type=_a			,   help="Dropout probability (Optional). Goes into model.config"			,   )
       parser.add_argument(
           "--attention_dropout"			,   type=_a			,   help="Attention dropout probability (Optional). Goes into model.config"			,   )
       parser.add_argument("--learning_rate"			,   default=5e-5			,   type=_a			,   help="The initial learning rate for Adam."   )
       parser.add_argument(
           "--lr_scheduler"			,   default="linear"			,   choices=_a			,   metavar=_a			,   type=_a			,   help="Learning rate scheduler"			,   )
       parser.add_argument("--weight_decay"			,   default=0.0			,   type=_a			,   help="Weight decay if we apply some."   )
       parser.add_argument("--adam_epsilon"			,   default=1e-8			,   type=_a			,   help="Epsilon for Adam optimizer."   )
       parser.add_argument("--warmup_steps"			,   default=0			,   type=_a			,   help="Linear warmup over warmup_steps."   )
       parser.add_argument("--num_workers"			,   default=4			,   type=_a			,   help="kwarg passed to DataLoader"   )
       parser.add_argument("--num_train_epochs"			,   dest="max_epochs"			,   default=3			,   type=_a   )
       parser.add_argument("--train_batch_size"			,   default=32			,   type=_a   )
       parser.add_argument("--eval_batch_size"			,   default=32			,   type=_a   )
       parser.add_argument("--adafactor"			,   action="store_true"   )



class       _UpperCamelCase   (					pl.Callback		):
      def 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :Any			,   lowerCamelCase :Optional[Any]   )  ->      Dict:
       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(_a   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :pl.Trainer			,   lowerCamelCase :pl.LightningModule   )  ->      Any:
       rank_zero_info("***** Validation results *****"   )
       UpperCAmelCase__							=    trainer.callback_metrics
       # Log results
       for key in sorted(_a   ):
        if key not in ["log", "progress_bar"]:
         rank_zero_info("{} = {}\n".format(_a			,   str(metrics[key]   )   )   )







      def 						UpperCAmelCase_    (      self :Union[str, Any]			,   lowerCamelCase :pl.Trainer			,   lowerCamelCase :pl.LightningModule   )  ->      Optional[int]:
       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(_a			,   "w"   ) as writer:
        for key in sorted(_a   ):
         if key not in ["log", "progress_bar"]:
          rank_zero_info("{} = {}\n".format(_a			,   str(metrics[key]   )   )   )
          writer.write("{} = {}\n".format(_a			,   str(metrics[key]   )   )   )



def     lowerCAmelCase		(		_lowerCAmelCase				: str    ,				_lowerCAmelCase				: str						):




 """simple docstring"""







 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				: Dict=None    ,				_lowerCAmelCase				: List[Any]=True    ,				_lowerCAmelCase				: str=[]    ,				_lowerCAmelCase				: int=None    ,				_lowerCAmelCase				: str=None    ,				**_lowerCAmelCase				: Optional[int]    ,				):




 """simple docstring"""







 pl.seed_everything(args.seed						)

 # init model
 UpperCAmelCase__							=    Path(model.hparams.output_dir						)
 odir.mkdir(exist_ok=_lowerCAmelCase						)

 # 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(_lowerCAmelCase						)
 if logging_callback is None:
  UpperCAmelCase__							=    LoggingCallback()

 UpperCAmelCase__							=    {}

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

 if args.gpus > 1:
  UpperCAmelCase__							=    "ddp"

 UpperCAmelCase__							=    args.accumulate_grad_batches
 UpperCAmelCase__							=    extra_train_kwargs.get("accelerator"    ,				_lowerCAmelCase						)
 UpperCAmelCase__							=    extra_train_kwargs.get("profiler"    ,				_lowerCAmelCase						)

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

 if args.do_train:
  trainer.fit(_lowerCAmelCase						)

 return trainer










from __future__ import annotations





def     __lowerCamelCase							(   __magic_name__						:	int    ):
							for r in range(1  ,			__magic_name__    ):
														a__:							List[str]									=[]
														for x in range(n - 1    ):
																					a__:							int									=pow(__magic_name__  ,			__magic_name__  ,			__magic_name__    )
																					if val in li:
																												break
																					li.append(__magic_name__    )
														else:
																					return r
							return None


if __name__ == "__main__":
		__UpperCAmelCase    	=  int(input('''Enter a prime number q: '''))
		__UpperCAmelCase    	=  find_primitive(q)
		if a is None:
				print(f"""Cannot find the primitive for the value: {a!r}""")
		else:
				__UpperCAmelCase    	=  int(input('''Enter private key of A: '''))
				__UpperCAmelCase    	=  pow(a, a_private, q)
				__UpperCAmelCase    	=  int(input('''Enter private key of B: '''))
				__UpperCAmelCase    	=  pow(a, b_private, q)

				__UpperCAmelCase    	=  pow(b_public, a_private, q)
				__UpperCAmelCase    	=  pow(a_public, b_private, q)

				print('''The key value generated by A is: ''', a_secret)
				print('''The key value generated by B is: ''', b_secret)





