


'''simple docstring'''







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
# and perform 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)
#
# 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
#
########################################################################


A__:    List[Any]					    =       16
A__:    Tuple					    =       32


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Accelerator    ,_UpperCAmelCase	:	int = 16  )  ->			Any:
       _a    : Union[str, Any]     					=AutoTokenizer.from_pretrained("""bert-base-cased"""  )
       _a    : Tuple     					=load_dataset("""glue"""    ,"""mrpc"""  )

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

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

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

       # Instantiate dataloaders.
       _a    : str     					=DataLoader(
           tokenized_datasets["""train"""]    ,shuffle=_UpperCAmelCase    ,collate_fn=_UpperCAmelCase    ,batch_size=_UpperCAmelCase  )
       _a    : List[Any]     					=DataLoader(
           tokenized_datasets["""validation"""]    ,shuffle=_UpperCAmelCase    ,collate_fn=_UpperCAmelCase    ,batch_size=_UpperCAmelCase  )

       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

   A__:    List[str]					    =       mocked_dataloaders  # noqa: F811


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	Any  )  ->			int:
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""    ,_UpperCAmelCase  ) == "1":
              _a    : int     					=2
       # New Code #
       _a    : int     					=int(args.gradient_accumulation_steps  )
       # Initialize accelerator
       _a    : Optional[Any]     					=Accelerator(
           cpu=args.cpu    ,mixed_precision=args.mixed_precision    ,gradient_accumulation_steps=_UpperCAmelCase  )
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""  )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _a    : List[Any]     					=config["""lr"""]
       _a    : List[str]     					=int(config["""num_epochs"""]  )
       _a    : Any     					=int(config["""seed"""]  )
       _a    : Tuple     					=int(config["""batch_size"""]  )

       _a    : Any     					=evaluate.load("""glue"""    ,"""mrpc"""  )

       set_seed(_UpperCAmelCase  )
       _a					,    _a    : Tuple     					=get_dataloaders(_UpperCAmelCase    ,_UpperCAmelCase  )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _a    : List[str]     					=AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""    ,return_dict=_UpperCAmelCase  )

       # 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    : Tuple     					=model.to(accelerator.device  )

       # Instantiate optimizer
       _a    : Dict     					=AdamW(params=model.parameters()    ,lr=_UpperCAmelCase  )

       # Instantiate scheduler
       _a    : Any     					=get_linear_schedule_with_warmup(
           optimizer=_UpperCAmelCase    ,num_warmup_steps=100    ,num_training_steps=(len(_UpperCAmelCase  ) * 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    : Optional[Any]     					=accelerator.prepare(
           _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase  )

       # Now we train the model
       for epoch in range(_UpperCAmelCase  ):
              model.train()
              for step, batch in enumerate(_UpperCAmelCase  ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device  )
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(_UpperCAmelCase  ):
                            _a    : int     					=model(**_UpperCAmelCase  )
                            _a    : Union[str, Any]     					=output.loss
                            accelerator.backward(_UpperCAmelCase  )
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

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


def 	SCREAMING_SNAKE_CASE_					(  )  ->			Optional[int]:
       _a    : List[Any]     					=argparse.ArgumentParser(description="""Simple example of training script."""  )
       parser.add_argument(
           """--mixed_precision"""    ,type=_UpperCAmelCase    ,default=_UpperCAmelCase    ,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."""    ,)
       # New Code #
       parser.add_argument(
           """--gradient_accumulation_steps"""    ,type=_UpperCAmelCase    ,default=1    ,help="""The number of minibatches to be ran before gradients are accumulated."""    ,)
       parser.add_argument("""--cpu"""    ,action="""store_true"""    ,help="""If passed, will train on the CPU."""  )
       _a    : int     					=parser.parse_args()
       _a    : int     					={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(_UpperCAmelCase    ,_UpperCAmelCase  )


if __name__ == "__main__":
   main()






'''simple docstring'''







from math import isqrt


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			bool:
       return all(number % divisor != 0 for divisor in range(2    ,isqrt(_UpperCAmelCase  ) + 1  )  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10**6  )  ->			int:
       _a    : List[Any]     					=0
       _a    : str     					=1
       _a    : Optional[Any]     					=7
       while prime_candidate < max_prime:
              primes_count += is_prime(_UpperCAmelCase  )

              cube_index += 1
              prime_candidate += 6 * cube_index

       return primes_count


if __name__ == "__main__":
   print(F"{solution() = }")


