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


SCREAMING_SNAKE_CASE__		:					Tuple						=					16
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					32







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Tuple = 16 ):







     '''simple docstring'''


     __A				:				int			=		AutoTokenizer.from_pretrained('bert-base-cased' )
     __A				:				Dict			=		load_dataset('glue'     ,  'mrpc' )

     def tokenize_function(lowercase__  :			Dict ):
          # max_length=None => use the model max length (it's actually the default)
          __A				:				Union[str, Any]			=		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():
          __A				:				Dict			=		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
     __A				:				str			=		tokenized_datasets.rename_column('label'     ,  'labels' )

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

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

     # Instantiate dataloaders.
     __A				:				Optional[Any]			=		DataLoader(
         tokenized_datasets['train']     ,  shuffle=__snake_case     ,  collate_fn=__snake_case     ,  batch_size=__snake_case )
     __A				:				str			=		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

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







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Any ):







     '''simple docstring'''


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

     __A				:				Union[str, Any]			=		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(lowercase__  :			Optional[int] ):
          # 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)
          __A				:				Optional[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).
          __A				:				Optional[int]			=		model.to(accelerator.device )

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

          # Instantiate scheduler
          __A				:				int			=		get_linear_schedule_with_warmup(
              optimizer=__snake_case     ,  num_warmup_steps=1_00     ,  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.
          __A			,							__A			,							__A			,							__A			,							__A				:				Union[str, Any]			=		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 )
                    __A				:				Tuple			=		model(**__snake_case )
                    __A				:				int			=		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():
                         __A				:				Optional[int]			=		model(**__snake_case )
                    __A				:				Dict			=		outputs.logits.argmax(dim=-1 )
                    __A			,							__A				:				Optional[int]			=		accelerator.gather_for_metrics((predictions, batch['labels']) )
                    metric.add_batch(
                        predictions=__snake_case     ,  references=__snake_case     ,  )

               __A				:				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     _a				(      ):







     '''simple docstring'''


     __A				:				List[Any]			=		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.' )
     __A				:				Any			=		parser.parse_args()
     __A				:				int			=		{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(__snake_case     ,  __snake_case )


if __name__ == "__main__":
      main()




import math







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     assert isinstance(lowercase__     ,  lowercase__ ) and (
         number >= 0
     ), "'number' must been an int and positive"

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or not number % 2:
          # Negatives, 0, 1 and all even numbers are not primes
          return False

     SCREAMING_SNAKE_CASE__				:				Tuple			=		range(3     ,  int(math.sqrt(lowercase__ ) + 1 )     ,  2 )
     return not any(not number % i for i in odd_numbers )







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Any=1     ,  **lowercase__  :			Any ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		factor * value
     SCREAMING_SNAKE_CASE__				:				Dict			=		value

     while not is_prime(lowercase__ ):
          value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

     if value == first_value_val:
          return next_prime(value + 1     ,  **lowercase__ )
     return value



