





"""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
#
########################################################################

UpperCamelCase_           =32





def        a_				(							_lowercase       ,						_lowercase = 16   ):
						_UpperCamelCase			:					str     =    AutoTokenizer.from_pretrained('''bert-base-cased'''   )
						_UpperCamelCase			:					Optional[Any]     =    load_dataset('''glue'''       ,						'''mrpc'''   )

						def tokenize_function(_lowercase   ):
												# max_length=None => use the model max length (it's actually the default)
												_UpperCamelCase			:					str     =    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():
												_UpperCamelCase			:					Any     =    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
						_UpperCamelCase			:					Any     =    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":
																		_UpperCamelCase			:					Optional[Any]     =    16
												elif accelerator.mixed_precision != "no":
																		_UpperCamelCase			:					Any     =    8
												else:
																		_UpperCamelCase			:					Any     =    None

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

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

						UpperCamelCase_           =mocked_dataloaders  # noqa: F811





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

						_UpperCamelCase			:					Tuple     =    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
												_UpperCamelCase			:					Optional[Any]     =    observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												_UpperCamelCase			:					Optional[int]     =    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)
												_UpperCamelCase			:					str     =    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).
												_UpperCamelCase			:					Optional[Any]     =    model.to(accelerator.device   )

												# Instantiate optimizer
												_UpperCamelCase			:					Dict     =    AdamW(params=model.parameters()       ,						lr=_lowercase   )
												_UpperCamelCase   ,							_UpperCamelCase			:					List[Any]     =    get_dataloaders(_lowercase       ,						_lowercase   )

												# Instantiate scheduler
												_UpperCamelCase			:					Any     =    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.
												_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase			:					int     =    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   )
																														_UpperCamelCase			:					Optional[int]     =    model(**_lowercase   )
																														_UpperCamelCase			:					Tuple     =    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():
																														_UpperCamelCase			:					List[Any]     =    model(**_lowercase   )
																								_UpperCamelCase			:					Union[str, Any]     =    outputs.logits.argmax(dim=-1   )
																								_UpperCamelCase   ,							_UpperCamelCase			:					Any     =    accelerator.gather_for_metrics((predictions, batch['''labels'''])   )
																								metric.add_batch(
																								    predictions=_lowercase       ,						references=_lowercase       ,						)

																		_UpperCamelCase			:					Optional[Any]     =    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_				(							):
						_UpperCamelCase			:					List[Any]     =    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.'''   )
						_UpperCamelCase			:					List[str]     =    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
						_UpperCamelCase			:					Optional[Any]     =    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
						training_function(_lowercase       ,						_lowercase   )


if __name__ == "__main__":
						main()










"""simple docstring"""




UpperCamelCase_           ={
    """A""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """B""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """C""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """D""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """E""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """F""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """G""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """H""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """I""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """J""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """K""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """L""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """M""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """N""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """O""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """P""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """Q""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """R""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """S""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """T""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """U""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """V""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """W""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """X""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """Y""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
    """Z""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
}





def        a_				(							_lowercase   ):
						return [alphabet[char] for char in key.upper()]





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Optional[Any]     =    ''''''
						_UpperCamelCase			:					Tuple     =    0
						_UpperCamelCase			:					Optional[int]     =    generate_table(_lowercase   )
						for char in words.upper():
												cipher += get_opponent(table[count]       ,						_lowercase   )
												_UpperCamelCase			:					Optional[int]     =    (count + 1) % len(_lowercase   )
						return cipher





def        a_				(							_lowercase       ,						_lowercase   ):
						return encrypt(_lowercase       ,						_lowercase   )





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Dict     =    0 if char in table[0] else 1
						_UpperCamelCase			:					str     =    table[row].index(_lowercase   )
						return row, col





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    get_position(_lowercase       ,						char.upper()   )
						if row == 1:
												return table[0][col]
						else:
												return table[1][col] if row == 0 else char


if __name__ == "__main__":
						import doctest

						doctest.testmod()  # Fist ensure that all our tests are passing...
						UpperCamelCase_           =input("""Enter key: """).strip()
						UpperCamelCase_           =input("""Enter text to encrypt: """).strip()
						UpperCamelCase_           =encrypt(key, text)

						print(F"Encrypted: {cipher_text}")
						print(F"Decrypted with key: {decrypt(key, cipher_text)}")



