
import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase__		:     Union[str, Any]     			=      16
UpperCAmelCase__		:     Dict     			=      32






def   lowerCamelCase__							(					a		,		a = 16      )      ->   Tuple:
   _A:  Dict					=       AutoTokenizer.from_pretrained('''bert-base-cased'''      )
   _A:  Union[str, Any]					=       load_dataset('''glue'''		,		'''mrpc'''      )

   def tokenize_function(a      ):
      # max_length=None => use the model max length (it's actually the default)
      _A:  Tuple					=       tokenizer(examples['''sentence1''']		,		examples['''sentence2''']		,		truncation=a		,		max_length=a      )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      _A:  List[str]					=       datasets.map(
          a		,		batched=a		,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,		)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   _A:  Optional[int]					=       tokenized_datasets.rename_column('''label'''		,		'''labels'''      )

   def collate_fn(a      ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _A:  List[Any]					=       1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _A:  str					=       16
      elif accelerator.mixed_precision != "no":
         _A:  int					=       8
      else:
         _A:  Optional[int]					=       None

      return tokenizer.pad(
          a		,		padding='''longest'''		,		max_length=a		,		pad_to_multiple_of=a		,		return_tensors='''pt'''		,		)

   # Instantiate dataloaders.
   _A:  Optional[int]					=       DataLoader(
       tokenized_datasets['''train''']		,		shuffle=a		,		collate_fn=a		,		batch_size=a      )
   _A:  str					=       DataLoader(
       tokenized_datasets['''validation''']		,		shuffle=a		,		collate_fn=a		,		batch_size=a      )

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   UpperCAmelCase__		:     int     			=      mocked_dataloaders  # noqa: F811






def   lowerCamelCase__							(					a		,		a      )      ->   Tuple:
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,		a      ) == "1":
      _A:  List[Any]					=       2
   # Initialize accelerator
   _A:  Dict					=       Accelerator(cpu=args.cpu		,		mixed_precision=args.mixed_precision      )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _A:  Optional[Any]					=       config['''lr''']
   _A:  Dict					=       int(config['''num_epochs''']      )
   _A:  Optional[Any]					=       int(config['''seed''']      )
   _A:  List[Any]					=       int(config['''batch_size''']      )

   _A:  Dict					=       evaluate.load('''glue'''		,		'''mrpc'''      )

   # New Code #
   # We now can define an inner training loop function. It should take a batch size as the only parameter,
   # and build the dataloaders in there.
   # It also gets our decorator
   @find_executable_batch_size(starting_batch_size=a      )
   def inner_training_loop(a      ):
      # And now just move everything below under this function
      # We need to bring in the Accelerator object from earlier
      nonlocal accelerator
      # And reset all of its attributes that could hold onto any memory:
      accelerator.free_memory()

      # Then we can declare the model, optimizer, and everything else:
      set_seed(a      )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _A:  List[str]					=       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,		return_dict=a      )

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      _A:  Any					=       model.to(accelerator.device      )

      # Instantiate optimizer
      _A:  Dict					=       AdamW(params=model.parameters()		,		lr=a      )
      _A			,  _A:  Optional[int]					=       get_dataloaders(a		,		a      )

      # Instantiate scheduler
      _A:  Optional[int]					=       get_linear_schedule_with_warmup(
          optimizer=a		,		num_warmup_steps=1_00		,		num_training_steps=(len(a      ) * num_epochs)		,		)

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      _A			,  _A			,  _A			,  _A			,  _A:  Dict					=       accelerator.prepare(
          a		,		a		,		a		,		a		,		a      )

      # Now we train the model
      for epoch in range(a      ):
         model.train()
         for step, batch in enumerate(a      ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device      )
            _A:  Any					=       model(**a      )
            _A:  Any					=       outputs.loss
            accelerator.backward(a      )
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         model.eval()
         for step, batch in enumerate(a      ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device      )
            with torch.no_grad():
               _A:  List[str]					=       model(**a      )
            _A:  int					=       outputs.logits.argmax(dim=-1      )
            _A			,  _A:  Optional[int]					=       accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
            metric.add_batch(
                predictions=a		,		references=a		,		)

         _A:  List[Any]					=       metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(f"""epoch {epoch}:"""		,		a      )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
   inner_training_loop()






def   lowerCamelCase__							(					)      ->   Union[str, Any]:
   _A:  int					=       argparse.ArgumentParser(description='''Simple example of training script.'''      )
   parser.add_argument(
       '''--mixed_precision'''		,		type=a		,		default=a		,		choices=['''no''', '''fp16''', '''bf16''', '''fp8''']		,		help='''Whether to use mixed precision. Choose'''
       '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
       '''and an Nvidia Ampere GPU.'''		,		)
   parser.add_argument('''--cpu'''		,		action='''store_true'''		,		help='''If passed, will train on the CPU.'''      )
   _A:  Any					=       parser.parse_args()
   _A:  Any					=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(a		,		a      )


if __name__ == "__main__":
   main()





import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCAmelCase__		:     Any     			=      3






def   lowerCamelCase__							(					a      )      ->   int:
   print('''Generating primitive root of p'''      )
   while True:
      _A:  Union[str, Any]					=       random.randrange(3		,		a      )
      if pow(a		,		2		,		a      ) == 1:
         continue
      if pow(a		,		a		,		a      ) == 1:
         continue
      return g






def   lowerCamelCase__							(					a      )      ->   tuple[tuple[int, int, int, int], tuple[int, int]]:
   print('''Generating prime p...'''      )
   _A:  Dict					=       rabin_miller.generate_large_prime(a      )  # select large prime number.
   _A:  Any					=       primitive_root(a      )  # one primitive root on modulo p.
   _A:  Optional[Any]					=       random.randrange(3		,		a      )  # private_key -> have to be greater than 2 for safety.
   _A:  Dict					=       cryptomath.find_mod_inverse(pow(a		,		a		,		a      )		,		a      )

   _A:  Union[str, Any]					=       (key_size, e_a, e_a, p)
   _A:  Union[str, Any]					=       (key_size, d)

   return public_key, private_key






def   lowerCamelCase__							(					a		,		a      )      ->   None:
   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:  Any					=       generate_key(a      )
   print(f"""\nWriting public key to file {name}_pubkey.txt..."""      )
   with open(f"""{name}_pubkey.txt"""		,		'''w'''      ) as fo:
      fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""      )

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






def   lowerCamelCase__							(					)      ->   None:
   print('''Making key files...'''      )
   make_key_files('''elgamal'''		,		20_48      )
   print('''Key files generation successful'''      )


if __name__ == "__main__":
   main()



