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					=				16
UpperCAmelCase					=				32


def SCREAMING_SNAKE_CASE				(     lowercase_:	Accelerator		,						lowercase_:	int = 16				):
       _UpperCamelCase     :       int  						=  AutoTokenizer.from_pretrained('bert-base-cased'				)
       _UpperCamelCase     :       Tuple  						=  load_dataset('glue'		,						'mrpc'				)

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

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

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

       # Instantiate dataloaders.
       _UpperCamelCase     :       Tuple  						=  DataLoader(
           tokenized_datasets['train']		,						shuffle=lowercase_		,						collate_fn=lowercase_		,						batch_size=lowercase_				)
       _UpperCamelCase     :       str  						=  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 SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	str				):
       # For testing only
       if os.environ.get('TESTING_MOCKED_DATALOADERS'		,						lowercase_				) == "1":
              _UpperCamelCase     :       str  						=  2
       # Initialize accelerator
       _UpperCamelCase     :       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     :       Dict  						=  config['lr']
       _UpperCamelCase     :       str  						=  int(config['num_epochs']				)
       _UpperCamelCase     :       int  						=  int(config['seed']				)
       _UpperCamelCase     :       Dict  						=  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":
                     _UpperCamelCase     :       Union[str, Any]  						=  args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     _UpperCamelCase     :       Tuple  						=  int(args.checkpointing_steps				)
              else:
                     raise ValueError(
                         F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."				)
       else:
              _UpperCamelCase     :       Union[str, Any]  						=  None

       set_seed(lowercase_				)

       _UpperCamelCase						,      _UpperCamelCase     :       List[Any]  						=  get_dataloaders(lowercase_		,						lowercase_				)
       _UpperCamelCase     :       Union[str, Any]  						=  evaluate.load('glue'		,						'mrpc'				)

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

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _UpperCamelCase     :       List[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     :       int  						=  model.to(accelerator.device				)

       # Instantiate optimizer
       _UpperCamelCase     :       int  						=  AdamW(params=model.parameters()		,						lr=lowercase_				)

       # Instantiate scheduler
       _UpperCamelCase     :       Optional[int]  						=  get_linear_schedule_with_warmup(
           optimizer=lowercase_		,						num_warmup_steps=100		,						num_training_steps=(len(lowercase_				) * 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.
       _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       List[Any]  						=  accelerator.prepare(
           lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_				)

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       _UpperCamelCase     :       Optional[int]  						=  0
       # We also need to keep track of the stating epoch so files are named properly
       _UpperCamelCase     :       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				)
                     _UpperCamelCase     :       Optional[Any]  						=  os.path.basename(args.resume_from_checkpoint				)
              else:
                     # Get the most recent checkpoint
                     _UpperCamelCase     :       str  						=  [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
                     dirs.sort(key=os.path.getctime				)
                     _UpperCamelCase     :       Any  						=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              _UpperCamelCase     :       str  						=  os.path.splitext(lowercase_				)[0]

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

    # Now we train the model
       for epoch in range(lowercase_		,						lowercase_				):
              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
                     _UpperCamelCase     :       Optional[Any]  						=  accelerator.skip_first_batches(lowercase_		,						lowercase_				)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     _UpperCamelCase     :       List[Any]  						=  train_dataloader
              for step, batch in enumerate(lowercase_				):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device				)
                     _UpperCamelCase     :       Optional[Any]  						=  model(**lowercase_				)
                     _UpperCamelCase     :       Tuple  						=  outputs.loss
                     _UpperCamelCase     :       Tuple  						=  loss / gradient_accumulation_steps
                     accelerator.backward(lowercase_				)
                     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(lowercase_		,						lowercase_				):
                            _UpperCamelCase     :       Optional[Any]  						=  F"step_{overall_step}"
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          _UpperCamelCase     :       int  						=  os.path.join(args.output_dir		,						lowercase_				)
                                   accelerator.save_state(lowercase_				)

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

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

              # 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":
                     _UpperCamelCase     :       int  						=  F"epoch_{epoch}"
                     if args.output_dir is not None:
                            _UpperCamelCase     :       Optional[Any]  						=  os.path.join(args.output_dir		,						lowercase_				)
                     accelerator.save_state(lowercase_				)


def SCREAMING_SNAKE_CASE				(     ):
       _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.'				)
       parser.add_argument(
           '--checkpointing_steps'		,						type=lowercase_		,						default=lowercase_		,						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=lowercase_		,						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=lowercase_		,						default=lowercase_		,						help='If the training should continue from a checkpoint folder.'		,						)
       _UpperCamelCase     :       List[str]  						=  parser.parse_args()
       _UpperCamelCase     :       List[Any]  						=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(lowercase_		,						lowercase_				)


if __name__ == "__main__":
 main()




UpperCAmelCase					=				[chr(i) for i in range(32, 1_26)]
UpperCAmelCase					=				list(range(len(alphabets)))
UpperCAmelCase					=				list(range(len(alphabets)))
UpperCAmelCase					=				list(range(len(alphabets)))
UpperCAmelCase					=				list(reversed(range(len(alphabets))))
UpperCAmelCase					=				[]
UpperCAmelCase					=				UpperCAmelCase					=				UpperCAmelCase					=				0


def SCREAMING_SNAKE_CASE				(     ):
       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       _UpperCamelCase     :       Any  						=  gear_one[0]
       gear_one.append(lowercase_				)
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(lowercase_				)				) == 0:
              _UpperCamelCase     :       str  						=  gear_two[0]
              gear_two.append(lowercase_				)
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(lowercase_				)				) == 0:
                     _UpperCamelCase     :       int  						=  gear_three[0]
                     gear_three.append(lowercase_				)
                     del gear_three[0]
                     gear_three_pos += 1


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
       _UpperCamelCase     :       Dict  						=  alphabets.index(lowercase_				)
       _UpperCamelCase     :       Union[str, Any]  						=  gear_one[target]
       _UpperCamelCase     :       Any  						=  gear_two[target]
       _UpperCamelCase     :       Tuple  						=  gear_three[target]
       _UpperCamelCase     :       Tuple  						=  reflector[target]
       _UpperCamelCase     :       Any  						=  gear_three.index(lowercase_				)
       _UpperCamelCase     :       Any  						=  gear_two.index(lowercase_				)
       _UpperCamelCase     :       str  						=  gear_one.index(lowercase_				)
       code.append(alphabets[target]				)
       rotator()


if __name__ == "__main__":
 UpperCAmelCase					=				list(input('''Type your message:\n'''))
 while True:
  try:
   UpperCAmelCase					=				int(input('''Please set token:(must be only digits)\n'''))
   break
  except Exception as error:
   print(error)
 for _ in range(token):
  rotator()
 for j in decode:
  engine(j)
 print('''\n''' + ''''''.join(code))
 print(
     f'''\nYour Token is {token} please write it down.\nIf you want to decode '''
     '''this message again you should input same digits as token!'''
 )



