





"""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
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
#
########################################################################

lowerCamelCase_          =	3_2



def   __lowerCamelCase     (							a_  :	str				,     a_  :	List[Any] = 16  )    ->   Union[str, Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						AutoTokenizer.from_pretrained('''bert-base-cased'''  )
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						load_dataset('''glue'''				,     '''mrpc'''  )

      def tokenize_function(a_  :	Optional[int]  ):
            # max_length=None => use the model max length (it's actually the default)
            __SCREAMING_SNAKE_CASE     :Dict	       =						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     :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     :List[str]	       =						tokenized_datasets.rename_column('''label'''				,     '''labels'''  )

      def collate_fn(a_  :	Optional[int]  ):
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  __SCREAMING_SNAKE_CASE     :Optional[int]	       =						16
            elif accelerator.mixed_precision != "no":
                  __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						8
            else:
                  __SCREAMING_SNAKE_CASE     :Dict	       =						None

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

      # Instantiate dataloaders.
      __SCREAMING_SNAKE_CASE     :List[str]	       =						DataLoader(
          tokenized_datasets['''train''']				,     shuffle=UpperCAmelCase_				,     collate_fn=UpperCAmelCase_				,     batch_size=UpperCAmelCase_  )
      __SCREAMING_SNAKE_CASE     :int	       =						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

  lowerCamelCase_          =	mocked_dataloaders  # noqa: F811



def   __lowerCamelCase     (							a_  :	Optional[int]				,     a_  :	Union[str, Any]  )    ->   Dict:






      if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     UpperCAmelCase_  ) == "1":
            __SCREAMING_SNAKE_CASE     :int	       =						2
      # Initialize accelerator
      __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     :Optional[int]	       =						config['''lr''']
      __SCREAMING_SNAKE_CASE     :Tuple	       =						int(config['''num_epochs''']  )
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						int(config['''seed''']  )
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						int(config['''batch_size''']  )

      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						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(UpperCAmelCase_  )  )
      def inner_training_loop(a_  :	Union[str, Any]  ):
            # 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
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						observed_batch_size // batch_size

            # And then set it in the Accelerator directly:
            __SCREAMING_SNAKE_CASE     :str	       =						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(UpperCAmelCase_  )

            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            __SCREAMING_SNAKE_CASE     :List[str]	       =						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     :Union[str, Any]	       =						model.to(accelerator.device  )

            # Instantiate optimizer
            __SCREAMING_SNAKE_CASE     :Dict	       =						AdamW(params=model.parameters()				,     lr=UpperCAmelCase_  )
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						get_dataloaders(UpperCAmelCase_				,     UpperCAmelCase_  )

            # Instantiate scheduler
            __SCREAMING_SNAKE_CASE     :Dict	       =						get_linear_schedule_with_warmup(
                optimizer=UpperCAmelCase_				,     num_warmup_steps=1_00				,     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     :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_  ):
                        # And perform gradient accumulation
                        with accelerator.accumulate(UpperCAmelCase_  ):
                              # We could avoid this line since we set the accelerator with `device_placement=True`.
                              batch.to(accelerator.device  )
                              __SCREAMING_SNAKE_CASE     :Tuple	       =						model(**UpperCAmelCase_  )
                              __SCREAMING_SNAKE_CASE     :Optional[int]	       =						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     :List[str]	       =						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     :Optional[int]	       =						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   __lowerCamelCase     (							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						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     :List[Any]	       =						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
      __SCREAMING_SNAKE_CASE     :Tuple	       =						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 2_56}
      training_function(UpperCAmelCase_				,     UpperCAmelCase_  )


if __name__ == "__main__":
  main()






"""simple docstring"""
import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


lowerCamelCase_          =	(3_0, 4_8_7)
lowerCamelCase_          =	(3_6, 1_0_2_4)
lowerCamelCase_          =	(1_0_0, 1_0_0)
lowerCamelCase_          =	1_0_0

lowerCamelCase_          =	datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

lowerCamelCase_    ,  lowerCamelCase_          =	os.path.split(__file__)
lowerCamelCase_          =	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def   __lowerCamelCase     (							a_  :	Optional[Any]				,     a_  :	List[str]				,     a_  :	List[str]							)    ->   List[str]:
      with ArrowWriter(features=a_				,     path=os.path.join(a_				,     '''beta.arrow'''							)							) as writer:
            for key, record in dummy_data:
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						my_features.encode_example(a_							)
                  writer.write(a_							)
            __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						writer.finalize()



@get_duration
def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[Any]							)    ->   Dict:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for _ in dataset:
            pass



@get_duration
def   __lowerCamelCase     (							a_  :	List[str]				,     a_  :	List[str]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for _ in dataset:
            pass



@get_duration
def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[int]							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						10
      __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for i in range(0				,     len(a_							)				,     a_							):
            __SCREAMING_SNAKE_CASE     :Dict	       =						dataset[i : i + batch_size]



@get_duration
def   __lowerCamelCase     (							a_  :	Any				,     a_  :	Optional[Any]							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						10
      __SCREAMING_SNAKE_CASE     :List[str]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for i in range(0				,     len(a_							)				,     a_							):
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						dataset[i : i + batch_size]



@get_duration
def   __lowerCamelCase     (							a_  :	List[Any]				,     a_  :	Optional[Any]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :Tuple	       =						dataset[col]



@get_duration
def   __lowerCamelCase     (							a_  :	int				,     a_  :	List[str]							)    ->   str:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						dataset[col]



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						{}
      __SCREAMING_SNAKE_CASE     :int	       =						(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Features({'''image''': ArrayaD(a_				,     dtype='''float32'''							)}							)
            __SCREAMING_SNAKE_CASE     :Any	       =						generate_examples(features=a_				,     num_examples=a_							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :str	       =						read_func(a_				,     a_							)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''							)							)							)}							)
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						generate_examples(
                features=a_				,     num_examples=a_				,     seq_shapes={'''image''': SPEED_TEST_SHAPE}							)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :Tuple	       =						read_func(a_				,     a_							)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''							)							)}							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						generate_examples(
                features=a_				,     num_examples=a_				,     seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}				,     )
            __SCREAMING_SNAKE_CASE     :Any	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :str	       =						read_func(a_				,     a_							)

      with open(a_				,     '''wb'''							) as f:
            f.write(json.dumps(a_							).encode('''utf-8'''							)							)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_array_xd()