
'''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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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       =      32





def 	_A						(							lowercase__						,						lowercase__ = 16		):
						lowercase__								=    AutoTokenizer.from_pretrained("""bert-base-cased"""		)
						lowercase__								=    load_dataset("""glue"""						,						"""mrpc"""		)

						def tokenize_function(lowercase__		):
												# max_length=None => use the model max length (it's actually the default)
												lowercase__								=    tokenizer(examples["""sentence1"""]						,						examples["""sentence2"""]						,						truncation=lowercase__						,						max_length=lowercase__		)
												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():
												lowercase__								=    datasets.map(
												    lowercase__						,						batched=lowercase__						,						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
						lowercase__								=    tokenized_datasets.rename_column("""label"""						,						"""labels"""		)

						def collate_fn(lowercase__		):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		lowercase__								=    16
												elif accelerator.mixed_precision != "no":
																		lowercase__								=    8
												else:
																		lowercase__								=    None

												return tokenizer.pad(
												    lowercase__						,						padding="""longest"""						,						pad_to_multiple_of=lowercase__						,						return_tensors="""pt"""						,						)

						# Instantiate dataloaders.
						lowercase__								=    DataLoader(
						    tokenized_datasets["""train"""]						,						shuffle=lowercase__						,						collate_fn=lowercase__						,						batch_size=lowercase__		)
						lowercase__								=    DataLoader(
						    tokenized_datasets["""validation"""]						,						shuffle=lowercase__						,						collate_fn=lowercase__						,						batch_size=lowercase__		)

						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       =      mocked_dataloaders  # noqa: F811





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

						lowercase__								=    evaluate.load("""glue"""						,						"""mrpc"""		)

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(lowercase__		)		)
						def inner_training_loop(lowercase__		):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												lowercase__								=    observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												lowercase__								=    num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(lowercase__		)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												lowercase__								=    AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""						,						return_dict=lowercase__		)

												# 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).
												lowercase__								=    model.to(accelerator.device		)

												# Instantiate optimizer
												lowercase__								=    AdamW(params=model.parameters()						,						lr=lowercase__		)
												lowercase__	,  lowercase__								=    get_dataloaders(lowercase__						,						lowercase__		)

												# Instantiate scheduler
												lowercase__								=    get_linear_schedule_with_warmup(
												    optimizer=lowercase__						,						num_warmup_steps=100						,						num_training_steps=(len(lowercase__		) * 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.
												lowercase__	,  lowercase__	,  lowercase__	,  lowercase__	,  lowercase__								=    accelerator.prepare(
												    lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						lowercase__		)

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

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

																		lowercase__								=    metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f'''epoch {epoch}:'''						,						lowercase__		)

    # 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 	_A						(							):
						lowercase__								=    argparse.ArgumentParser(description="""Simple example of training script."""		)
						parser.add_argument(
						    """--mixed_precision"""						,						type=lowercase__						,						default=lowercase__						,						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."""		)
						lowercase__								=    parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						lowercase__								=    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 256}
						training_function(lowercase__						,						lowercase__		)


if __name__ == "__main__":
				main()








'''simple docstring'''



import os
import sys

from . import rsa_key_generator as rkg

__A       =      128
__A       =      256





def 	_A						(							lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE		):
						lowercase__								=    message.encode("""ascii"""		)

						lowercase__								=    []
						for block_start in range(0						,						len(lowercase__		)						,						lowercase__		):
												lowercase__								=    0
												for i in range(lowercase__						,						min(block_start + block_size						,						len(lowercase__		)		)		):
																		block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
												block_ints.append(lowercase__		)
						return block_ints





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE		):
						lowercase__								=    []
						for block_int in block_ints:
												lowercase__								=    []
												for i in range(block_size - 1						,						-1						,						-1		):
																		if len(lowercase__		) + i < message_length:
																								lowercase__								=    block_int // (BYTE_SIZE**i)
																								lowercase__								=    block_int % (BYTE_SIZE**i)
																								block_message.insert(0						,						chr(lowercase__		)		)
												message.extend(lowercase__		)
						return "".join(lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE		):
						lowercase__								=    []
						lowercase__	,  lowercase__								=    key

						for block in get_blocks_from_text(lowercase__						,						lowercase__		):
												encrypted_blocks.append(pow(lowercase__						,						lowercase__						,						lowercase__		)		)
						return encrypted_blocks





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE						,						):
						lowercase__								=    []
						lowercase__	,  lowercase__								=    key
						for block in encrypted_blocks:
												decrypted_blocks.append(pow(lowercase__						,						lowercase__						,						lowercase__		)		)
						return get_text_from_blocks(lowercase__						,						lowercase__						,						lowercase__		)





def 	_A						(							lowercase__		):
						with open(lowercase__		) as fo:
												lowercase__								=    fo.read()
						lowercase__	,  lowercase__	,  lowercase__								=    content.split(""","""		)
						return (int(lowercase__		), int(lowercase__		), int(lowercase__		))





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE						,						):
						lowercase__	,  lowercase__	,  lowercase__								=    read_key_file(lowercase__		)
						if key_size < block_size * 8:
												sys.exit(
												    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
												    """requires the block size to be equal to or greater than the key size. """
												    """Either decrease the block size or use different keys.""".format(
												        block_size * 8						,						lowercase__		)		)

						lowercase__								=    [str(lowercase__		) for i in encrypt_message(lowercase__						,						(n, e)						,						lowercase__		)]

						lowercase__								=    """,""".join(lowercase__		)
						lowercase__								=    f'''{len(lowercase__		)}_{block_size}_{encrypted_content}'''
						with open(lowercase__						,						"""w"""		) as fo:
												fo.write(lowercase__		)
						return encrypted_content





def 	_A						(							lowercase__						,						lowercase__		):
						lowercase__	,  lowercase__	,  lowercase__								=    read_key_file(lowercase__		)
						with open(lowercase__		) as fo:
												lowercase__								=    fo.read()
						lowercase__	,  lowercase__	,  lowercase__								=    content.split("""_"""		)
						lowercase__								=    int(lowercase__		)
						lowercase__								=    int(lowercase__		)

						if key_size < block_size * 8:
												sys.exit(
												    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
												    """requires the block size to be equal to or greater than the key size. """
												    """Did you specify the correct key file and encrypted file?""".format(
												        block_size * 8						,						lowercase__		)		)

						lowercase__								=    []
						for block in encrypted_message.split(""","""		):
												encrypted_blocks.append(int(lowercase__		)		)

						return decrypt_message(lowercase__						,						lowercase__						,						(n, d)						,						lowercase__		)





def 	_A						(							):
						lowercase__								=    """encrypted_file.txt"""
						lowercase__								=    input(r"""Encrypt\Decrypt [e\d]: """		)

						if response.lower().startswith("""e"""		):
												lowercase__								=    """encrypt"""
						elif response.lower().startswith("""d"""		):
												lowercase__								=    """decrypt"""

						if mode == "encrypt":
												if not os.path.exists("""rsa_pubkey.txt"""		):
																		rkg.make_key_files("""rsa"""						,						1024		)

												lowercase__								=    input("""\nEnter message: """		)
												lowercase__								=    """rsa_pubkey.txt"""
												print(f'''Encrypting and writing to {filename}...'''		)
												lowercase__								=    encrypt_and_write_to_file(lowercase__						,						lowercase__						,						lowercase__		)

												print("""\nEncrypted text:"""		)
												print(lowercase__		)

						elif mode == "decrypt":
												lowercase__								=    """rsa_privkey.txt"""
												print(f'''Reading from {filename} and decrypting...'''		)
												lowercase__								=    read_from_file_and_decrypt(lowercase__						,						lowercase__		)
												print("""writing decryption to rsa_decryption.txt..."""		)
												with open("""rsa_decryption.txt"""						,						"""w"""		) as dec:
																		dec.write(lowercase__		)

												print("""\nDecryption:"""		)
												print(lowercase__		)


if __name__ == "__main__":
				main()






