






'''simple docstring'''
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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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_					=	1_6
a_					=	3_2





def 	_a(					UpperCamelCase__			:  Accelerator,		UpperCamelCase__			:  int = 1_6							):
      SCREAMING_SNAKE_CASE__       :			Any													=AutoTokenizer.from_pretrained('''bert-base-cased'''							)
      SCREAMING_SNAKE_CASE__       :			int													=load_dataset('''glue''',		'''mrpc'''							)

      def tokenize_function(UpperCamelCase__			:  int							):
            # 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__       :			Optional[int]													=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__       :			Any													=tokenized_datasets.rename_column('''label''',		'''labels'''							)

      def collate_fn(UpperCamelCase__			:  int							):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=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__       :			List[str]													=1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=8
            else:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__       :			List[Any]													=DataLoader(
          tokenized_datasets['''train'''],		shuffle=UpperCAmelCase__,		collate_fn=UpperCAmelCase__,		batch_size=UpperCAmelCase__							)
      SCREAMING_SNAKE_CASE__       :			Tuple													=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_					=	mocked_dataloaders  # noqa: F811





def 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  Optional[int]							):
      # For testing only
      if os.environ.get('''TESTING_MOCKED_DATALOADERS''',		UpperCAmelCase__							) == "1":
            SCREAMING_SNAKE_CASE__       :			List[str]													=2
      # Initialize accelerator
      SCREAMING_SNAKE_CASE__       :			Tuple													=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__       :			List[Any]													=config["""lr"""]
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=int(config['''num_epochs''']							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=int(config['''seed''']							)
      SCREAMING_SNAKE_CASE__       :			Any													=int(config['''batch_size''']							)

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

      # New Code #
      # We now can define an inner training loop function. It should take a batch size as the only parameter,
      # and build the dataloaders in there.
      # It also gets our decorator
      @find_executable_batch_size(starting_batch_size=UpperCAmelCase__							)
      def inner_training_loop(UpperCamelCase__			:  Union[str, Any]							):
            # And now just move everything below under this function
            # We need to bring in the Accelerator object from earlier
            nonlocal accelerator
            # And reset all of its attributes that could hold onto any memory:
            accelerator.free_memory()

            # Then we can declare the model, optimizer, and everything else:
            set_seed(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__       :			Optional[int]													=model.to(accelerator.device							)

            # Instantiate optimizer
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=AdamW(params=model.parameters(),		lr=UpperCAmelCase__							)
            SCREAMING_SNAKE_CASE__       :			int													=get_dataloaders(UpperCAmelCase__,		UpperCAmelCase__							)

            # Instantiate scheduler
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=get_linear_schedule_with_warmup(
                optimizer=UpperCAmelCase__,		num_warmup_steps=1_0_0,		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.
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=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__       :			str													=model(**UpperCAmelCase__							)
                        SCREAMING_SNAKE_CASE__       :			Tuple													=outputs.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():
                              SCREAMING_SNAKE_CASE__       :			Tuple													=model(**UpperCAmelCase__							)
                        SCREAMING_SNAKE_CASE__       :			int													=outputs.logits.argmax(dim=-1							)
                        SCREAMING_SNAKE_CASE__       :			List[str]													=accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
                        metric.add_batch(
                            predictions=UpperCAmelCase__,		references=UpperCAmelCase__,		)

                  SCREAMING_SNAKE_CASE__       :			List[Any]													=metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f"epoch {epoch}:",		UpperCAmelCase__							)

    # 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 	_a(					):
      SCREAMING_SNAKE_CASE__       :			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.''',		)
      parser.add_argument('''--cpu''',		action='''store_true''',		help='''If passed, will train on the CPU.'''							)
      SCREAMING_SNAKE_CASE__       :			int													=parser.parse_args()
      SCREAMING_SNAKE_CASE__       :			Dict													={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
      training_function(UpperCAmelCase__,		UpperCAmelCase__							)


if __name__ == "__main__":
  main()







'''simple docstring'''
import math





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      return math.sqrt(UpperCamelCase__							) * math.sqrt(UpperCamelCase__							) == num





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[Any]													=0
      SCREAMING_SNAKE_CASE__       :			Any													=n
      while left <= right:
            SCREAMING_SNAKE_CASE__       :			str													=(left + right) // 2
            if mid**2 == n:
                  return True
            elif mid**2 > n:
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=mid - 1
            else:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=mid + 1
      return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()