




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


_lowerCamelCase      :  str      						=						1_6
_lowerCamelCase      :  Union[str, Any]      						=						3_2




def 							_UpperCAmelCase  (UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : str = 16       ):
				'''simple docstring'''
				_lowerCAmelCase    :    str										=      AutoTokenizer.from_pretrained("""bert-base-cased"""       )
				_lowerCAmelCase    :    Optional[int]										=      load_dataset("""glue"""      ,    """mrpc"""       )

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

				def collate_fn(UpperCamelCase_ : Any       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_lowerCAmelCase    :    str										=      128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												_lowerCAmelCase    :    Optional[int]										=      16
								elif accelerator.mixed_precision != "no":
												_lowerCAmelCase    :    Dict										=      8
								else:
												_lowerCAmelCase    :    List[Any]										=      None

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

				# Instantiate dataloaders.
				_lowerCAmelCase    :    Dict										=      DataLoader(
				    tokenized_datasets["""train"""]      ,    shuffle=__snake_case      ,    collate_fn=__snake_case      ,    batch_size=__snake_case       )
				_lowerCAmelCase    :    Any										=      DataLoader(
				    tokenized_datasets["""validation"""]      ,    shuffle=__snake_case      ,    collate_fn=__snake_case      ,    batch_size=__snake_case       )

				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

	_lowerCamelCase      :  Tuple      						=						mocked_dataloaders  # noqa: F811




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

				_lowerCAmelCase    :    str										=      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=__snake_case       )
				def inner_training_loop(UpperCamelCase_ : Dict       ):
								# 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(__snake_case       )

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

								# 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).
								_lowerCAmelCase    :    Any										=      model.to(accelerator.device       )

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

								# Instantiate scheduler
								_lowerCAmelCase    :    int										=      get_linear_schedule_with_warmup(
								    optimizer=__snake_case      ,    num_warmup_steps=100      ,    num_training_steps=(len(__snake_case       ) * 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.
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      accelerator.prepare(
								    __snake_case      ,    __snake_case      ,    __snake_case      ,    __snake_case      ,    __snake_case       )

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

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

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

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


if __name__ == "__main__":
	main()







from __future__ import annotations
class        __snake_case  :



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		int = 0      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      key



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int      )  ->    list[str]:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[Any]										=      key or self.__key or 1

								# make sure key is an appropriate size
								key %= 255

								return [chr(ord(_UpperCAmelCase      ) ^ key      ) for ch in content]



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int      )  ->    list[str]:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    List[Any]										=      key or self.__key or 1

								# make sure key is an appropriate size
								key %= 255

								return [chr(ord(_UpperCAmelCase      ) ^ key      ) for ch in content]



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int = 0      )  ->    str:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Union[str, Any]										=      key or self.__key or 1

								# make sure key can be any size
								while key > 255:
												key -= 255

								# This will be returned
								_lowerCAmelCase    :    List[str]										=      """"""

								for ch in content:
												ans += chr(ord(_UpperCAmelCase      ) ^ key      )

								return ans



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int = 0      )  ->    str:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Any										=      key or self.__key or 1

								# make sure key can be any size
								while key > 255:
												key -= 255

								# This will be returned
								_lowerCAmelCase    :    List[Any]										=      """"""

								for ch in content:
												ans += chr(ord(_UpperCAmelCase      ) ^ key      )

								return ans



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int = 0      )  ->    bool:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								try:
												with open(_UpperCAmelCase      ) as fin, open("""encrypt.out"""		,	"""w+"""      ) as fout:
																# actual encrypt-process
																for line in fin:
																				fout.write(self.encrypt_string(_UpperCAmelCase		,	_UpperCAmelCase      )      )

								except OSError:
												return False

								return True



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int      )  ->    bool:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								try:
												with open(_UpperCAmelCase      ) as fin, open("""decrypt.out"""		,	"""w+"""      ) as fout:
																# actual encrypt-process
																for line in fin:
																				fout.write(self.decrypt_string(_UpperCAmelCase		,	_UpperCAmelCase      )      )

								except OSError:
												return False

								return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")

