"""simple docstring"""







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
#
########################################################################


_a  :  Dict=				16
_a  :  Optional[int]=				32
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Accelerator    ,							UpperCAmelCase_					:       int = 16						)							->    Dict:

 '''simple docstring'''





 __snake_case    :							Union[str, Any]   	=				AutoTokenizer.from_pretrained('bert-base-cased'						)
 __snake_case    :							List[str]   	=				load_dataset('glue'    ,							'mrpc'						)

 def tokenize_function(UpperCAmelCase_					:       List[Any]						):
  # max_length=None => use the model max length (it's actually the default)
  __snake_case    :							Union[str, Any]   	=				tokenizer(examples['sentence1']    ,							examples['sentence2']    ,							truncation=_lowerCamelCase    ,							max_length=_lowerCamelCase						)
  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():
  __snake_case    :							List[str]   	=				datasets.map(
      _lowerCamelCase    ,							batched=_lowerCamelCase    ,							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
 __snake_case    :							Optional[int]   	=				tokenized_datasets.rename_column('label'    ,							'labels'						)

 def collate_fn(UpperCAmelCase_					:       List[str]						):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __snake_case    :							List[str]   	=				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":
   __snake_case    :							List[Any]   	=				16
  elif accelerator.mixed_precision != "no":
   __snake_case    :							int   	=				8
  else:
   __snake_case    :							Dict   	=				None

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

 # Instantiate dataloaders.
 __snake_case    :							List[Any]   	=				DataLoader(
     tokenized_datasets['train']    ,							shuffle=_lowerCamelCase    ,							collate_fn=_lowerCamelCase    ,							batch_size=_lowerCamelCase						)
 __snake_case    :							int   	=				DataLoader(
     tokenized_datasets['validation']    ,							shuffle=_lowerCamelCase    ,							collate_fn=_lowerCamelCase    ,							batch_size=_lowerCamelCase						)

 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

 _a  :  Optional[int]=				mocked_dataloaders  # noqa: F811
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Dict						)							->    List[str]:

 '''simple docstring'''





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

 __snake_case    :							Optional[Any]   	=				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=_lowerCamelCase						)
 def inner_training_loop(UpperCAmelCase_					:       Optional[Any]						):
  # 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(_lowerCamelCase						)

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

  # 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).
  __snake_case    :							str   	=				model.to(accelerator.device						)

  # Instantiate optimizer
  __snake_case    :							Optional[Any]   	=				AdamW(params=model.parameters()    ,							lr=_lowerCamelCase						)
  __snake_case    :							List[Any]   	=				get_dataloaders(_lowerCamelCase    ,							_lowerCamelCase						)

  # Instantiate scheduler
  __snake_case    :							Dict   	=				get_linear_schedule_with_warmup(
      optimizer=_lowerCamelCase    ,							num_warmup_steps=1_00    ,							num_training_steps=(len(_lowerCamelCase						) * 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.
  __snake_case    :							Optional[Any]   	=				accelerator.prepare(
      _lowerCamelCase    ,							_lowerCamelCase    ,							_lowerCamelCase    ,							_lowerCamelCase    ,							_lowerCamelCase						)

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

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

   __snake_case    :							int   	=				metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(F"epoch {epoch}:"    ,							_lowerCamelCase						)

    # 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 						__UpperCAmelCase       (					)							->    str:

 '''simple docstring'''





 __snake_case    :							Optional[int]   	=				argparse.ArgumentParser(description='Simple example of training script.'						)
 parser.add_argument(
     '--mixed_precision'    ,							type=_lowerCamelCase    ,							default=_lowerCamelCase    ,							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.'						)
 __snake_case    :							Optional[Any]   	=				parser.parse_args()
 __snake_case    :							Any   	=				{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(_lowerCamelCase    ,							_lowerCamelCase						)


if __name__ == "__main__":
 main()






"""simple docstring"""







import string

import numpy
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	return b if a == 0 else greatest_common_divisor(b % a    ,							UpperCAmelCase_						)

class 	UpperCamelCase    :
				UpperCAmelCase       :   Any					=	string.ascii_uppercase + string.digits
				# This cipher takes alphanumerics into account
				# i.e. a total of 36 characters

				# take x and return x % len(key_string)
				UpperCAmelCase       :   List[Any]					=	numpy.vectorize(lambda lowercase		: x % 36		)

				UpperCAmelCase       :   Dict					=	numpy.vectorize(lowercase		)



				def __init__(self       :   str			,						_A       :   numpy.ndarray)					->			None:
					__snake_case    :							str   	=				self.modulus(_A)  # mod36 calc's on the encrypt key
					self.check_determinant()  # validate the determinant of the encryption key
					__snake_case    :							Optional[Any]   	=				encrypt_key.shape[0]



				def       _lowercase  (self       :   Any			,						_A       :   str)					->			int:
					return self.key_string.index(_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   int)					->			str:
					return self.key_string[round(_A)]



				def       _lowercase  (self       :   Optional[int])					->			None:
					__snake_case    :							Any   	=				round(numpy.linalg.det(self.encrypt_key))

					if det < 0:
						__snake_case    :							Any   	=				det % len(self.key_string)

					__snake_case    :							Tuple   	=				len(self.key_string)
					if greatest_common_divisor(_A			,						len(self.key_string)) != 1:
						__snake_case    :							List[str]   	=				(
						    f"determinant modular {req_l} of encryption key({det}) "
						    f"is not co prime w.r.t {req_l}.\nTry another key."
						)
						raise ValueError(_A)



				def       _lowercase  (self       :   Dict			,						_A       :   str)					->			str:
					__snake_case    :							str   	=				[char for char in text.upper() if char in self.key_string]

					__snake_case    :							int   	=				chars[-1]
					while len(_A) % self.break_key != 0:
						chars.append(_A)

					return "".join(_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			str:
					__snake_case    :							Any   	=				self.process_text(text.upper())
					__snake_case    :							Dict   	=				''

					for i in range(0			,						len(_A) - self.break_key + 1			,						self.break_key):
						__snake_case    :							Dict   	=				text[i : i + self.break_key]
						__snake_case    :							List[str]   	=				[self.replace_letters(_A) for char in batch]
						__snake_case    :							str   	=				numpy.array([vec]).T
						__snake_case    :							List[Any]   	=				self.modulus(self.encrypt_key.dot(_A)).T.tolist()[
						    0
						]
						__snake_case    :							str   	=				''.join(
						    self.replace_digits(_A) for num in batch_encrypted)
						encrypted += encrypted_batch

					return encrypted



				def       _lowercase  (self       :   Optional[int])					->			numpy.ndarray:
					__snake_case    :							List[Any]   	=				round(numpy.linalg.det(self.encrypt_key))

					if det < 0:
						__snake_case    :							int   	=				det % len(self.key_string)
					__snake_case    :							Optional[Any]   	=				None
					for i in range(len(self.key_string)):
						if (det * i) % len(self.key_string) == 1:
							__snake_case    :							Dict   	=				i
							break

					__snake_case    :							List[str]   	=				(
					    det_inv
					    * numpy.linalg.det(self.encrypt_key)
					    * numpy.linalg.inv(self.encrypt_key)
					)

					return self.to_int(self.modulus(_A))






				def       _lowercase  (self       :   int			,						_A       :   str)					->			str:
					__snake_case    :							int   	=				self.make_decrypt_key()
					__snake_case    :							List[str]   	=				self.process_text(text.upper())
					__snake_case    :							str   	=				''

					for i in range(0			,						len(_A) - self.break_key + 1			,						self.break_key):
						__snake_case    :							Optional[Any]   	=				text[i : i + self.break_key]
						__snake_case    :							Union[str, Any]   	=				[self.replace_letters(_A) for char in batch]
						__snake_case    :							Tuple   	=				numpy.array([vec]).T
						__snake_case    :							List[str]   	=				self.modulus(decrypt_key.dot(_A)).T.tolist()[0]
						__snake_case    :							str   	=				''.join(
						    self.replace_digits(_A) for num in batch_decrypted)
						decrypted += decrypted_batch

					return decrypted







def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				int(input('Enter the order of the encryption key: '						)						)
	__snake_case    :							str   	=				[]

	print('Enter each row of the encryption key with space separated integers'						)
	for _ in range(UpperCAmelCase_						):
		__snake_case    :							Union[str, Any]   	=				[int(UpperCAmelCase_						) for x in input().split()]
		hill_matrix.append(UpperCAmelCase_						)

	__snake_case    :							Dict   	=				HillCipher(numpy.array(UpperCAmelCase_						)						)

	print('Would you like to encrypt or decrypt some text? (1 or 2)'						)
	__snake_case    :							Optional[Any]   	=				input('\n1. Encrypt\n2. Decrypt\n'						)
	if option == "1":
		__snake_case    :							int   	=				input('What text would you like to encrypt?: '						)
		print('Your encrypted text is:'						)
		print(hc.encrypt(UpperCAmelCase_						)						)
	elif option == "2":
		__snake_case    :							Tuple   	=				input('What text would you like to decrypt?: '						)
		print('Your decrypted text is:'						)
		print(hc.decrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	main()





