





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

_UpperCAmelCase             =							16
_UpperCAmelCase             =							32






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Accelerator   ,   SCREAMING_SNAKE_CASE		:int = 16   )      ->   Dict:
 __lowerCAmelCase					:	List[Any]      =       AutoTokenizer.from_pretrained("""bert-base-cased"""   )
 __lowerCAmelCase					:	Dict      =       load_dataset("""glue"""   ,   """mrpc"""   )

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

 def collate_fn(SCREAMING_SNAKE_CASE		:Tuple   ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __lowerCAmelCase					:	Optional[int]      =       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":
   __lowerCAmelCase					:	Dict      =       16
  elif accelerator.mixed_precision != "no":
   __lowerCAmelCase					:	Tuple      =       8
  else:
   __lowerCAmelCase					:	List[str]      =       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.
 __lowerCAmelCase					:	List[Any]      =       DataLoader(
     tokenized_datasets["""train"""]   ,   shuffle=SCREAMING_SNAKE_CASE   ,   collate_fn=SCREAMING_SNAKE_CASE   ,   batch_size=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	int      =       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

      _UpperCAmelCase             =							mocked_dataloaders  # noqa: F811






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:List[Any]   )      ->   int:
 # For testing only
 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,   SCREAMING_SNAKE_CASE   ) == "1":
  __lowerCAmelCase					:	List[Any]      =       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:
  __lowerCAmelCase					:	Any      =       Accelerator(
      cpu=args.cpu   ,   mixed_precision=args.mixed_precision   ,   log_with="""all"""   ,   project_dir=args.project_dir   )
 else:
  __lowerCAmelCase					:	str      =       Accelerator(cpu=args.cpu   ,   mixed_precision=args.mixed_precision   )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowerCAmelCase					:	Any      =       config["""lr"""]
 __lowerCAmelCase					:	List[Any]      =       int(config["""num_epochs"""]   )
 __lowerCAmelCase					:	Union[str, Any]      =       int(config["""seed"""]   )
 __lowerCAmelCase					:	int      =       int(config["""batch_size"""]   )
 set_seed(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Any      =       get_dataloaders(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[Any]      =       evaluate.load("""glue"""   ,   """mrpc"""   )

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

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

 # Instantiate optimizer
 __lowerCAmelCase					:	Any      =       AdamW(params=model.parameters()   ,   lr=SCREAMING_SNAKE_CASE   )

 # Instantiate scheduler
 __lowerCAmelCase					:	List[Any]      =       get_linear_schedule_with_warmup(
     optimizer=SCREAMING_SNAKE_CASE   ,   num_warmup_steps=100   ,   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.
 __lowerCAmelCase					:	Tuple      =       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:
  __lowerCAmelCase					:	Dict      =       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:
   __lowerCAmelCase					:	Optional[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   )
   __lowerCAmelCase					:	str      =       model(**SCREAMING_SNAKE_CASE   )
   __lowerCAmelCase					:	int      =       outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   __lowerCAmelCase					:	List[str]      =       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():
    __lowerCAmelCase					:	int      =       model(**SCREAMING_SNAKE_CASE   )
   __lowerCAmelCase					:	Optional[Any]      =       outputs.logits.argmax(dim=-1   )
   __lowerCAmelCase					:	List[Any]      =       accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
   metric.add_batch(
       predictions=SCREAMING_SNAKE_CASE   ,   references=SCREAMING_SNAKE_CASE   ,   )

  __lowerCAmelCase					:	List[str]      =       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 	_SCREAMING_SNAKE_CASE		(      )      ->   Union[str, Any]:
 __lowerCAmelCase					:	Optional[int]      =       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"""   ,   )
 __lowerCAmelCase					:	Optional[int]      =       parser.parse_args()
 __lowerCAmelCase					:	Dict      =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      main()






from math import isqrt






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   list[int]:
 __lowerCAmelCase					:	Tuple      =       [True] * max_number
 for i in range(2   ,   isqrt(max_number - 1   ) + 1   ):
  if is_prime[i]:
   for j in range(i**2   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Tuple      =       False

 return [i for i in range(2   ,   SCREAMING_SNAKE_CASE   ) if is_prime[i]]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int = 10**8   )      ->   int:
 __lowerCAmelCase					:	int      =       calculate_prime_numbers(max_number // 2   )

 __lowerCAmelCase					:	List[Any]      =       0
 __lowerCAmelCase					:	List[str]      =       0
 __lowerCAmelCase					:	List[Any]      =       len(SCREAMING_SNAKE_CASE   ) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


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