


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

a_       :Union[str, Any]				  =				32
def 	lowercase_							(A			:			Accelerator		, A			:			int = 1_6				):
       snake_case__							:     Optional[Any]            =     AutoTokenizer.from_pretrained('bert-base-cased'				)
       snake_case__							:     List[Any]            =     load_dataset('glue'		, 'mrpc'				)

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

       def collate_fn(A			:			Optional[Any]				):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     snake_case__							:     Union[str, Any]            =     1_6
              elif accelerator.mixed_precision != "no":
                     snake_case__							:     int            =     8
              else:
                     snake_case__							:     List[Any]            =     None

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

       # Instantiate dataloaders.
       snake_case__							:     List[str]            =     DataLoader(
           tokenized_datasets['train']		, shuffle=A		, collate_fn=A		, batch_size=A				)
       snake_case__							:     List[str]            =     DataLoader(
           tokenized_datasets['validation']		, shuffle=A		, collate_fn=A		, batch_size=A				)

       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[Any]				  =				mocked_dataloaders  # noqa: F811
def 	lowercase_							(A			:			Union[str, Any]		, A			:			Dict				):
       # For testing only
       if os.environ.get('TESTING_MOCKED_DATALOADERS'		, A				) == "1":
              snake_case__							:     Optional[Any]            =     2
       # Initialize accelerator
       snake_case__							:     List[Any]            =     Accelerator(cpu=args.cpu		, mixed_precision=args.mixed_precision				)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       snake_case__							:     List[str]            =     config['lr']
       snake_case__							:     List[Any]            =     int(config['num_epochs']				)
       snake_case__							:     Optional[int]            =     int(config['seed']				)
       snake_case__							:     int            =     int(config['batch_size']				)

       snake_case__							:     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(A				)				)
       def inner_training_loop(A			:			Optional[int]				):
              # 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
              snake_case__							:     str            =     observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              snake_case__							:     Dict            =     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(A				)

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              snake_case__							:     Dict            =     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		, return_dict=A				)

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

              # Instantiate optimizer
              snake_case__							:     Optional[Any]            =     AdamW(params=model.parameters()		, lr=A				)
              snake_case__							:     List[str]            =     get_dataloaders(A		, A				)

              # Instantiate scheduler
              snake_case__							:     Optional[int]            =     get_linear_schedule_with_warmup(
                  optimizer=A		, num_warmup_steps=1_0_0		, num_training_steps=(len(A				) * 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.
              snake_case__							:     int            =     accelerator.prepare(
                  A		, A		, A		, A		, A				)

              # Now we train the model
              for epoch in range(A				):
                     model.train()
                     for step, batch in enumerate(A				):
                            # And perform gradient accumulation
                            with accelerator.accumulate(A				):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device				)
                                   snake_case__							:     Optional[Any]            =     model(**A				)
                                   snake_case__							:     Optional[int]            =     outputs.loss
                                   accelerator.backward(A				)
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

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

                     snake_case__							:     Dict            =     metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(F'''epoch {epoch}:'''		, A				)

    # 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_							():
       snake_case__							:     Union[str, Any]            =     argparse.ArgumentParser(description='Simple example of training script.'				)
       parser.add_argument(
           '--mixed_precision'		, type=A		, default=A		, 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.'				)
       snake_case__							:     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
       snake_case__							:     Optional[Any]            =     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 2_5_6}
       training_function(A		, A				)


if __name__ == "__main__":
 main()











import math
def 	lowercase_							(A			:			int				):

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

       # All primes number are in format of 6k +/- 1
       for i in range(5		, int(math.sqrt(A				) + 1				)		, 6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True
def 	lowercase_							(A			:			int = 1_0_0_0		, A			:			int = 1_0_0_0				):
       snake_case__							:     str            =     [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1		, A				):
              for b in range(2		, A				):
                     if is_prime(A				):
                            snake_case__							:     Optional[Any]            =     0
                            snake_case__							:     Optional[int]            =     0
                            while is_prime((n**2) + (a * n) + b				):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   snake_case__							:     Union[str, Any]            =     [count, a, b]
       snake_case__							:     List[str]            =     longest[1] * longest[2]
       return ans


if __name__ == "__main__":
 print(solution(1_000, 1_000))






