



'''simple docstring'''




import argparse
import os

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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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__	:     Optional[Any]    =			16
UpperCamelCase__	:     int    =			32


def     lowerCAmelCase_       (     _lowerCamelCase:       Accelerator	,				_lowerCamelCase:       int = 16 ):
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		AutoTokenizer.from_pretrained("""bert-base-cased""" )
		__SCREAMING_SNAKE_CASE	:		int   	=		load_dataset("""glue"""	,				"""mrpc""" )

		def tokenize_function(_lowerCamelCase:       int ):
				# max_length=None => use the model max length (it's actually the default)
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		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():
				__SCREAMING_SNAKE_CASE	:		Dict   	=		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
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		tokenized_datasets.rename_column("""label"""	,				"""labels""" )

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

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

		# Instantiate dataloaders.
		__SCREAMING_SNAKE_CASE	:		Dict   	=		DataLoader(
		    tokenized_datasets["""train"""]	,				shuffle=_lowerCamelCase	,				collate_fn=_lowerCamelCase	,				batch_size=_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		Dict   	=		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

						UpperCamelCase__	:     Optional[Any]    =			mocked_dataloaders  # noqa: F811


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

		# New Code #
		# Parse out whether we are saving every epoch or after a certain number of batches
		if hasattr(args.checkpointing_steps	,				"""isdigit""" ):
				if args.checkpointing_steps == "epoch":
						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						__SCREAMING_SNAKE_CASE	:		int   	=		int(args.checkpointing_steps )
				else:
						raise ValueError(
						    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed." )
		else:
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		None

		set_seed(_lowerCamelCase )

		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		get_dataloaders(_lowerCamelCase	,				_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		evaluate.load("""glue"""	,				"""mrpc""" )

		# If the batch size is too big we use gradient accumulation
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		batch_size // MAX_GPU_BATCH_SIZE
				__SCREAMING_SNAKE_CASE	:		Any   	=		MAX_GPU_BATCH_SIZE

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		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).
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		model.to(accelerator.device )

		# Instantiate optimizer
		__SCREAMING_SNAKE_CASE	:		int   	=		AdamW(params=model.parameters()	,				lr=_lowerCamelCase )

		# Instantiate scheduler
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		get_linear_schedule_with_warmup(
		    optimizer=_lowerCamelCase	,				num_warmup_steps=1_00	,				num_training_steps=(len(_lowerCamelCase ) * num_epochs) // gradient_accumulation_steps	,				)

		# 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.
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		accelerator.prepare(
		    _lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )

		# New Code #
		# We need to keep track of how many total steps we have iterated over
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		0
		# We also need to keep track of the stating epoch so files are named properly
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		0

		# We need to load the checkpoint back in before training here with `load_state`
		# The total number of epochs is adjusted based on where the state is being loaded from,
		# as we assume continuation of the same training script
		if args.resume_from_checkpoint:
				if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
						accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}" )
						accelerator.load_state(args.resume_from_checkpoint )
						__SCREAMING_SNAKE_CASE	:		List[Any]   	=		os.path.basename(args.resume_from_checkpoint )
				else:
						# Get the most recent checkpoint
						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
						dirs.sort(key=os.path.getctime )
						__SCREAMING_SNAKE_CASE	:		Dict   	=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				__SCREAMING_SNAKE_CASE	:		Dict   	=		os.path.splitext(_lowerCamelCase )[0]

				if "epoch" in training_difference:
						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		int(training_difference.replace("""epoch_"""	,				"""""" ) ) + 1
						__SCREAMING_SNAKE_CASE	:		List[Any]   	=		None
				else:
						__SCREAMING_SNAKE_CASE	:		int   	=		int(training_difference.replace("""step_"""	,				"""""" ) )
						__SCREAMING_SNAKE_CASE	:		Dict   	=		resume_step // len(_lowerCamelCase )
						resume_step -= starting_epoch * len(_lowerCamelCase )

    # Now we train the model
		for epoch in range(_lowerCamelCase	,				_lowerCamelCase ):
				model.train()
				# New Code #
				if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
						# We need to skip steps until we reach the resumed step
						__SCREAMING_SNAKE_CASE	:		Any   	=		accelerator.skip_first_batches(_lowerCamelCase	,				_lowerCamelCase )
						overall_step += resume_step
				else:
						# After the first iteration though, we need to go back to the original dataloader
						__SCREAMING_SNAKE_CASE	:		Dict   	=		train_dataloader
				for step, batch in enumerate(_lowerCamelCase ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device )
						__SCREAMING_SNAKE_CASE	:		Any   	=		model(**_lowerCamelCase )
						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		outputs.loss
						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		loss / gradient_accumulation_steps
						accelerator.backward(_lowerCamelCase )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
						# New Code #
						overall_step += 1

						# New Code #
						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
						# These are saved to folders named `step_{overall_step}`
						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
						# If mixed precision was used, will also save a "scalar.bin" file
						if isinstance(_lowerCamelCase	,				_lowerCamelCase ):
								__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		F"step_{overall_step}"
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		os.path.join(args.output_dir	,				_lowerCamelCase )
										accelerator.save_state(_lowerCamelCase )

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

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

				# New Code #
				# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
				# These are saved to folders named `epoch_{epoch}`
				# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
				# If mixed precision was used, will also save a "scalar.bin" file
				if checkpointing_steps == "epoch":
						__SCREAMING_SNAKE_CASE	:		str   	=		F"epoch_{epoch}"
						if args.output_dir is not None:
								__SCREAMING_SNAKE_CASE	:		int   	=		os.path.join(args.output_dir	,				_lowerCamelCase )
						accelerator.save_state(_lowerCamelCase )


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		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.""" )
		parser.add_argument(
		    """--checkpointing_steps"""	,				type=_lowerCamelCase	,				default=_lowerCamelCase	,				help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""	,				)
		parser.add_argument(
		    """--output_dir"""	,				type=_lowerCamelCase	,				default="""."""	,				help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,				)
		parser.add_argument(
		    """--resume_from_checkpoint"""	,				type=_lowerCamelCase	,				default=_lowerCamelCase	,				help="""If the training should continue from a checkpoint folder."""	,				)
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		parser.parse_args()
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(_lowerCamelCase	,				_lowerCamelCase )


if __name__ == "__main__":
						main()




'''simple docstring'''




import os
import sys

from . import rsa_key_generator as rkg

UpperCamelCase__	:     Any    =			1_28
UpperCamelCase__	:     Optional[int]    =			2_56


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		message.encode("""ascii""" )

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


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		list[str]   	=		[]
		for block_int in block_ints:
				__SCREAMING_SNAKE_CASE	:		list[str]   	=		[]
				for i in range(block_size - 1	,				-1	,				-1 ):
						if len(_lowerCamelCase ) + i < message_length:
								__SCREAMING_SNAKE_CASE	:		List[Any]   	=		block_int // (BYTE_SIZE**i)
								__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		block_int % (BYTE_SIZE**i)
								block_message.insert(0	,				chr(_lowerCamelCase ) )
				message.extend(_lowerCamelCase )
		return "".join(_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       tuple[int, int]	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[]
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		key

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


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       tuple[int, int]	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE	,				):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase ) )
		return get_text_from_blocks(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       str ):
		with open(_lowerCamelCase ) as fo:
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		fo.read()
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		content.split(""",""" )
		return (int(_lowerCamelCase ), int(_lowerCamelCase ), int(_lowerCamelCase ))


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       str	,				_lowerCamelCase:       str	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE	,				):
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		read_key_file(_lowerCamelCase )
		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	,				_lowerCamelCase ) )

		__SCREAMING_SNAKE_CASE	:		Dict   	=		[str(_lowerCamelCase ) for i in encrypt_message(_lowerCamelCase	,				(n, e)	,				_lowerCamelCase )]

		__SCREAMING_SNAKE_CASE	:		int   	=		""",""".join(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		F"{len(_lowerCamelCase )}_{block_size}_{encrypted_content}"
		with open(_lowerCamelCase	,				"""w""" ) as fo:
				fo.write(_lowerCamelCase )
		return encrypted_content


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       str ):
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		read_key_file(_lowerCamelCase )
		with open(_lowerCamelCase ) as fo:
				__SCREAMING_SNAKE_CASE	:		Any   	=		fo.read()
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		content.split("""_""" )
		__SCREAMING_SNAKE_CASE	:		int   	=		int(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		int   	=		int(_lowerCamelCase )

		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	,				_lowerCamelCase ) )

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

		return decrypt_message(_lowerCamelCase	,				_lowerCamelCase	,				(n, d)	,				_lowerCamelCase )


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		"""encrypted_file.txt"""
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		input(r"""Encrypt\Decrypt [e\d]: """ )

		if response.lower().startswith("""e""" ):
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""encrypt"""
		elif response.lower().startswith("""d""" ):
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		"""decrypt"""

		if mode == "encrypt":
				if not os.path.exists("""rsa_pubkey.txt""" ):
						rkg.make_key_files("""rsa"""	,				10_24 )

				__SCREAMING_SNAKE_CASE	:		int   	=		input("""\nEnter message: """ )
				__SCREAMING_SNAKE_CASE	:		str   	=		"""rsa_pubkey.txt"""
				print(F"Encrypting and writing to {filename}..." )
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		encrypt_and_write_to_file(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )

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

		elif mode == "decrypt":
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		"""rsa_privkey.txt"""
				print(F"Reading from {filename} and decrypting..." )
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		read_from_file_and_decrypt(_lowerCamelCase	,				_lowerCamelCase )
				print("""writing decryption to rsa_decryption.txt...""" )
				with open("""rsa_decryption.txt"""	,				"""w""" ) as dec:
						dec.write(_lowerCamelCase )

				print("""\nDecryption:""" )
				print(_lowerCamelCase )


if __name__ == "__main__":
						main()