





'''simple docstring'''

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    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.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
  from fairscale.optim import OSS


__lowerCAmelCase							:     Optional[int]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Dict		     ={
    "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,
    "constant": get_constant_schedule,
    "constant_w_warmup": get_constant_schedule_with_warmup,
}






class        UpperCAmelCase     (	__lowerCAmelCase    ):
 def __init__(    self				:Dict						,	lowercase_				:str=None						,	lowercase_				:List[str]=None						,	*lowercase_				:Union[str, Any]						,	**lowercase_				:Tuple							)->							Optional[Any]:
     super().__init__(*lowerCamelCase__						,	**lowerCamelCase__							)

     if config is None:
         assert isinstance(self.model						,	lowerCamelCase__							), (
             "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
             F" {self.model.__class__}"
         )
         A__    =      self.model.config
     else:
         A__    =      config

     A__    =      data_args
     A__    =      self.config.tgt_vocab_size if isinstance(self.config						,	lowerCamelCase__							) else self.config.vocab_size

     if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
         assert self.config.pad_token_id is not None, (
             "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
             " calculation or doing label smoothing."
         )

     if self.config.pad_token_id is None and self.config.eos_token_id is not None:
         logger.warning(
             F"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
             " padding.."							)

     if self.args.label_smoothing == 0:
         A__    =      torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id							)
     else:
         # dynamically import label_smoothed_nll_loss
         from utils import label_smoothed_nll_loss

         A__    =      label_smoothed_nll_loss
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:int							)->							Optional[Any]:






     if self.optimizer is None:
         A__    =      ['''bias''', '''LayerNorm.weight''']
         A__    =      [
             {
                 '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay							)],
                 '''weight_decay''': self.args.weight_decay,
             },
             {
                 '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay							)],
                 '''weight_decay''': 0.0,
             },
         ]
         A__    =      Adafactor if self.args.adafactor else AdamW
         if self.args.adafactor:
             A__    =      Adafactor
             A__    =      {'''scale_parameter''': False, '''relative_step''': False}
         else:
             A__    =      AdamW
             A__    =      {
                 '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
                 '''eps''': self.args.adam_epsilon,
             }
         A__    =      self.args.learning_rate
         if self.sharded_ddp:
             A__    =      OSS(
                 params=lowerCamelCase__						,	optim=lowerCamelCase__						,	**lowerCamelCase__						,	)
         else:
             A__    =      optimizer_cls(lowerCamelCase__						,	**lowerCamelCase__							)

     if self.lr_scheduler is None:
         A__    =      self._get_lr_scheduler(lowerCamelCase__							)
     else:  # ignoring --lr_scheduler
         logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."							)
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:Union[str, Any]							)->							str:
     A__    =      arg_to_scheduler[self.args.lr_scheduler]
     if self.args.lr_scheduler == "constant":
         A__    =      schedule_func(self.optimizer							)
     elif self.args.lr_scheduler == "constant_w_warmup":
         A__    =      schedule_func(self.optimizer						,	num_warmup_steps=self.args.warmup_steps							)
     else:
         A__    =      schedule_func(
             self.optimizer						,	num_warmup_steps=self.args.warmup_steps						,	num_training_steps=lowerCamelCase__							)
     return scheduler
 def 					UpperCAmelCase_							(    self				:Any							)->							Optional[torch.utils.data.Sampler]:






     if isinstance(self.train_dataset						,	torch.utils.data.IterableDataset							):
         return None
     elif is_torch_tpu_available():
         return get_tpu_sampler(self.train_dataset							)
     else:
         if self.args.sortish_sampler:
             self.train_dataset.make_sortish_sampler(
                 self.args.per_device_train_batch_size						,	distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)						,	)

         return (
             RandomSampler(self.train_dataset							)
             if self.args.local_rank == -1
             else DistributedSampler(self.train_dataset							)
         )
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:str						,	lowercase_				:List[str]						,	lowercase_				:int							)->							int:






     if self.args.label_smoothing == 0:
         if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
             # force training to ignore pad token
             A__    =      model(**lowerCamelCase__						,	use_cache=lowerCamelCase__							)[0]
             A__    =      self.loss_fn(logits.view(-1						,	logits.shape[-1]							)						,	labels.view(-1							)							)
         else:
             # compute usual loss via models
             A__    =      model(**lowerCamelCase__						,	labels=lowerCamelCase__						,	use_cache=lowerCamelCase__							)[:2]
     else:
         # compute label smoothed loss
         A__    =      model(**lowerCamelCase__						,	use_cache=lowerCamelCase__							)[0]
         A__    =      torch.nn.functional.log_softmax(lowerCamelCase__						,	dim=-1							)
         A__    =      self.loss_fn(lowerCamelCase__						,	lowerCamelCase__						,	self.args.label_smoothing						,	ignore_index=self.config.pad_token_id							)
     return loss, logits
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Optional[Any]						,	lowercase_				:List[str]							)->							Dict:
     A__    =      inputs.pop("labels"							)
     A__    =      self._compute_loss(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__							)
     return loss
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:nn.Module						,	lowercase_				:Dict[str, Union[torch.Tensor, Any]]						,	lowercase_				:bool						,	lowercase_				:Optional[List[str]] = None						,	)->							Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
     A__    =      self._prepare_inputs(lowerCamelCase__							)

     A__    =      {
         '''max_length''': self.data_args.val_max_target_length
         if self.data_args is not None
         else self.config.max_length,
         '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
     }

     if self.args.predict_with_generate and not self.args.prediction_loss_only:
         A__    =      self.model.generate(
             inputs["input_ids"]						,	attention_mask=inputs["attention_mask"]						,	**lowerCamelCase__						,	)
         # in case the batch is shorter than max length, the output should be padded
         if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
             A__    =      self._pad_tensors_to_max_len(lowerCamelCase__						,	gen_kwargs["max_length"]							)

     A__    =      inputs.pop("labels"							)
     with torch.no_grad():
         # compute loss on predict data
         A__    =      self._compute_loss(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__							)

     A__    =      loss.mean().detach()
     if self.args.prediction_loss_only:
         return (loss, None, None)

     A__    =      generated_tokens if self.args.predict_with_generate else logits

     if labels.shape[-1] < gen_kwargs["max_length"]:
         A__    =      self._pad_tensors_to_max_len(lowerCamelCase__						,	gen_kwargs["max_length"]							)

     return (loss, logits, labels)



 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Tuple						,	lowercase_				:str							)->							Tuple:
     A__    =      self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

     if pad_token_id is None:
         raise ValueError(
             "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
             F" padded to `max_length`={max_length}"							)

     A__    =      pad_token_id * torch.ones(
         (tensor.shape[0], max_length)						,	dtype=tensor.dtype						,	device=tensor.device							)
     A__    =      tensor
     return padded_tensor










'''simple docstring'''

import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812





def 						UpperCamelCase    (							):
    print("Making key files..."	)
    make_key_files("rsa"						,	10_24	)
    print("Key files generation successful."	)





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    print("Generating prime p..."	)
    A__    =      rabinMiller.generate_large_prime(_lowerCamelCase	)
    print("Generating prime q..."	)
    A__    =      rabinMiller.generate_large_prime(_lowerCamelCase	)
    A__    =      p * q

    print("Generating e that is relatively prime to (p - 1) * (q - 1)..."	)
    while True:
        A__    =      random.randrange(2 ** (key_size - 1)						,	2 ** (key_size)	)
        if cryptoMath.gcd(_lowerCamelCase						,	(p - 1) * (q - 1)	) == 1:
            break

    print("Calculating d that is mod inverse of e..."	)
    A__    =      cryptoMath.find_mod_inverse(_lowerCamelCase						,	(p - 1) * (q - 1)	)

    A__    =      (n, e)
    A__    =      (n, d)
    return (public_key, private_key)





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : int	):
    if os.path.exists(F"{name}_pubkey.txt"	) or os.path.exists(F"{name}_privkey.txt"	):
        print("\nWARNING:"	)
        print(
            F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
            "Use a different name or delete these files and re-run this program."	)
        sys.exit()

    A__,     A__    =      generate_key(_lowerCamelCase	)
    print(F"\nWriting public key to file {name}_pubkey.txt..."	)
    with open(F"{name}_pubkey.txt"						,	"w"	) as out_file:
        out_file.write(F"{key_size},{public_key[0]},{public_key[1]}"	)

    print(F"Writing private key to file {name}_privkey.txt..."	)
    with open(F"{name}_privkey.txt"						,	"w"	) as out_file:
        out_file.write(F"{key_size},{private_key[0]},{private_key[1]}"	)


if __name__ == "__main__":
  main()



