






'''simple docstring'''
import argparse

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
#
# 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_					= 1_6
a_					= 3_2





def  _a(  UpperCamelCase__				:						Accelerator,	UpperCamelCase__				:						int = 1_6							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[Any]									=AutoTokenizer.from_pretrained('''bert-base-cased'''							)
      SCREAMING_SNAKE_CASE__     :				int									=load_dataset('''glue''',	'''mrpc'''							)

      def tokenize_function(UpperCamelCase__				:						Optional[Any]							):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE__     :				List[Any]									=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():
            SCREAMING_SNAKE_CASE__     :				Dict									=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
      SCREAMING_SNAKE_CASE__     :				List[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.
            SCREAMING_SNAKE_CASE__     :				str									=1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE__     :				str									=1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=8
            else:
                  SCREAMING_SNAKE_CASE__     :				Dict									=None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=DataLoader(
          tokenized_datasets['''train'''],	shuffle=UpperCamelCase__,	collate_fn=UpperCamelCase__,	batch_size=UpperCamelCase__,	drop_last=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				List[Any]									=DataLoader(
          tokenized_datasets['''validation'''],	shuffle=UpperCamelCase__,	collate_fn=UpperCamelCase__,	batch_size=UpperCamelCase__,	drop_last=(accelerator.mixed_precision == '''fp8'''),	)

      return train_dataloader, eval_dataloader





def  _a(  UpperCamelCase__				:						int,	UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Accelerator(cpu=args.cpu,	mixed_precision=args.mixed_precision							)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE__     :				int									=config['''lr''']
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=int(config['''num_epochs''']							)
      SCREAMING_SNAKE_CASE__     :				Any									=int(config['''seed''']							)
      SCREAMING_SNAKE_CASE__     :				Any									=int(config['''batch_size''']							)

      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=evaluate.load('''glue''',	'''mrpc'''							)

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

      set_seed(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=get_dataloaders(UpperCamelCase__,	UpperCamelCase__							)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE__     :				Any									=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).
      SCREAMING_SNAKE_CASE__     :				List[str]									=model.to(accelerator.device							)
      # Instantiate optimizer
      SCREAMING_SNAKE_CASE__     :				List[str]									=AdamW(params=model.parameters(),	lr=UpperCamelCase__							)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE__     :				int									=get_linear_schedule_with_warmup(
          optimizer=UpperCamelCase__,	num_warmup_steps=1_0_0,	num_training_steps=(len(UpperCamelCase__							) * 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.

      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[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							)
                  SCREAMING_SNAKE_CASE__     :				List[Any]									=model(**UpperCamelCase__							)
                  SCREAMING_SNAKE_CASE__     :				int									=outputs.loss
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=loss / gradient_accumulation_steps
                  accelerator.backward(UpperCamelCase__							)
                  if step % gradient_accumulation_steps == 0:
                        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():
                        SCREAMING_SNAKE_CASE__     :				List[str]									=model(**UpperCamelCase__							)
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=outputs.logits.argmax(dim=-1							)
                  SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
                  metric.add_batch(
                      predictions=UpperCamelCase__,	references=UpperCamelCase__,	)

            SCREAMING_SNAKE_CASE__     :				int									=metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:",	UpperCamelCase__							)





def  _a(  ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									=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.''',	)
      parser.add_argument('''--cpu''',	action='''store_true''',	help='''If passed, will train on the CPU.'''							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=parser.parse_args()
      SCREAMING_SNAKE_CASE__     :				Tuple									={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
      training_function(UpperCamelCase__,	UpperCamelCase__							)


if __name__ == "__main__":
  main()







'''simple docstring'''
from math import isqrt





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=[True] * max_number
      for i in range(2,	isqrt(max_number - 1							) + 1							):
            if is_prime[i]:
                  for j in range(i**2,	UpperCamelCase__,	UpperCamelCase__							):
                        SCREAMING_SNAKE_CASE__     :				Any									=False

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





def  _a(  UpperCamelCase__				:						int = 1_0**8							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=calculate_prime_numbers(max_number // 2							)

      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							) - 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() = }''')