"""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,
# specifically showcasing the experiment tracking 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
#
########################################################################

__SCREAMING_SNAKE_CASE								=16
__SCREAMING_SNAKE_CASE								=32



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

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

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

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

     # Instantiate dataloaders.
     lowercase_			:       str									=			DataLoader(
         tokenized_datasets['train']  ,    shuffle=__SCREAMING_SNAKE_CASE  ,    collate_fn=__SCREAMING_SNAKE_CASE  ,    batch_size=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Any									=			DataLoader(
         tokenized_datasets['validation']  ,    shuffle=__SCREAMING_SNAKE_CASE  ,    collate_fn=__SCREAMING_SNAKE_CASE  ,    batch_size=__SCREAMING_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								=mocked_dataloaders  # noqa: F811



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]   ):
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,    __SCREAMING_SNAKE_CASE   ) == "1":
          lowercase_			:       List[str]									=			2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          lowercase_			:       List[str]									=			Accelerator(
              cpu=args.cpu  ,    mixed_precision=args.mixed_precision  ,    log_with='all'  ,    project_dir=args.project_dir   )
     else:
          lowercase_			:       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
     lowercase_			:       int									=			config['lr']
     lowercase_			:       Any									=			int(config['num_epochs']   )
     lowercase_			:       Optional[Any]									=			int(config['seed']   )
     lowercase_			:       Dict									=			int(config['batch_size']   )
     set_seed(__SCREAMING_SNAKE_CASE   )

     lowercase_							,						lowercase_			:       Optional[Any]									=			get_dataloaders(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     lowercase_			:       Optional[int]									=			evaluate.load('glue'  ,    'mrpc'   )

     # If the batch size is too big we use gradient accumulation
     lowercase_			:       List[str]									=			1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          lowercase_			:       int									=			batch_size // MAX_GPU_BATCH_SIZE
          lowercase_			:       int									=			MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_			:       Dict									=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'  ,    return_dict=__SCREAMING_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).
     lowercase_			:       Tuple									=			model.to(accelerator.device   )

     # Instantiate optimizer
     lowercase_			:       Optional[int]									=			AdamW(params=model.parameters()  ,    lr=__SCREAMING_SNAKE_CASE   )

     # Instantiate scheduler
     lowercase_			:       List[Any]									=			get_linear_schedule_with_warmup(
         optimizer=__SCREAMING_SNAKE_CASE  ,    num_warmup_steps=1_00  ,    num_training_steps=(len(__SCREAMING_SNAKE_CASE   ) * 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.
     lowercase_							,						lowercase_							,						lowercase_							,						lowercase_							,						lowercase_			:       Any									=			accelerator.prepare(
         __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          lowercase_			:       List[Any]									=			os.path.split(__SCREAMING_SNAKE_CASE   )[-1].split('.'   )[0]
          accelerator.init_trackers(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     # Now we train the model
     for epoch in range(__SCREAMING_SNAKE_CASE   ):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               lowercase_			:       Union[str, Any]									=			0
          for step, batch in enumerate(__SCREAMING_SNAKE_CASE   ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device   )
               lowercase_			:       Union[str, Any]									=			model(**__SCREAMING_SNAKE_CASE   )
               lowercase_			:       Tuple									=			outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               lowercase_			:       Tuple									=			loss / gradient_accumulation_steps
               accelerator.backward(__SCREAMING_SNAKE_CASE   )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

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

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       'accuracy': eval_metric['accuracy'],
                       'f1': eval_metric['f1'],
                       'train_loss': total_loss.item() / len(__SCREAMING_SNAKE_CASE   ),
                       'epoch': epoch,
                   }  ,    step=__SCREAMING_SNAKE_CASE  ,    )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()



def     lowercase__( ):
     lowercase_			:       Any									=			argparse.ArgumentParser(description='Simple example of training script.'   )
     parser.add_argument(
         '--mixed_precision'  ,    type=__SCREAMING_SNAKE_CASE  ,    default=__SCREAMING_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.'   )
     parser.add_argument(
         '--with_tracking'  ,    action='store_true'  ,    help='Whether to load in all available experiment trackers from the environment and use them for logging.'  ,    )
     parser.add_argument(
         '--project_dir'  ,    type=__SCREAMING_SNAKE_CASE  ,    default='logs'  ,    help='Location on where to store experiment tracking logs` and relevent project information'  ,    )
     lowercase_			:       Optional[Any]									=			parser.parse_args()
     lowercase_			:       Union[str, Any]									=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       main()


"""simple docstring"""







from math import sqrt



def     lowercase__( __SCREAMING_SNAKE_CASE							:  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(sqrt(__SCREAMING_SNAKE_CASE   ) + 1   )  ,    6   ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int = 1_00_01   ):
     lowercase_			:       str									=			0
     lowercase_			:       Optional[Any]									=			1
     while count != nth and number < 3:
          number += 1
          if is_prime(__SCREAMING_SNAKE_CASE   ):
               count += 1
     while count != nth:
          number += 2
          if is_prime(__SCREAMING_SNAKE_CASE   ):
               count += 1
     return number


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

