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

SCREAMING_SNAKE_CASE_			    =  32




def 			lowercase__  (    lowerCAmelCase :    List[Any]       , lowerCAmelCase :    Optional[int] = 16       )       ->					str:






    """simple docstring"""




    UpperCAmelCase  	=    AutoTokenizer.from_pretrained('bert-base-cased'       )
    UpperCAmelCase  	=    load_dataset('glue'       , 'mrpc'       )

    def tokenize_function(lowerCAmelCase :    List[str]       ):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase  	=    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  	=    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  	=    tokenized_datasets.rename_column('label'       , 'labels'       )

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

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

    # Instantiate dataloaders.
    UpperCAmelCase  	=    DataLoader(
        tokenized_datasets['train']       , shuffle=_lowercase       , collate_fn=_lowercase       , batch_size=_lowercase       )
    UpperCAmelCase  	=    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

   SCREAMING_SNAKE_CASE_			    =  mocked_dataloaders  # noqa: F811




def 			lowercase__  (    lowerCAmelCase :    List[Any]       , lowerCAmelCase :    List[str]       )       ->					Optional[int]:






    """simple docstring"""




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

    UpperCAmelCase  	=    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(lowerCAmelCase :    Dict       ):
        # 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  	=    observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        UpperCAmelCase  	=    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  	=    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  	=    model.to(accelerator.device       )

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

        # Instantiate scheduler
        UpperCAmelCase  	=    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  	=    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  	=    model(**_lowercase       )
                    UpperCAmelCase  	=    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  	=    model(**_lowercase       )
                UpperCAmelCase  	=    outputs.logits.argmax(dim=-1       )
                UpperCAmelCase  	=    accelerator.gather_for_metrics((predictions, batch['labels'])       )
                metric.add_batch(
                    predictions=_lowercase       , references=_lowercase       , )

            UpperCAmelCase  	=    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 			lowercase__  (    )       ->					List[str]:






    """simple docstring"""




    UpperCAmelCase  	=    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  	=    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  	=    {'''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)}")



