





"""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 checkpointing 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
#
########################################################################

lowerCamelCase_          =	1_6
lowerCamelCase_          =	3_2



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

      def tokenize_function(a_  :	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=a_				,     max_length=a_							)
            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[Any]	       =						datasets.map(
                a_				,     batched=a_				,     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_  :	int							):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						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":
                  __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						16
            elif accelerator.mixed_precision != "no":
                  __SCREAMING_SNAKE_CASE     :Optional[int]	       =						8
            else:
                  __SCREAMING_SNAKE_CASE     :str	       =						None

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

      # Instantiate dataloaders.
      __SCREAMING_SNAKE_CASE     :Dict	       =						DataLoader(
          tokenized_datasets['''train''']				,     shuffle=a_				,     collate_fn=a_				,     batch_size=a_							)
      __SCREAMING_SNAKE_CASE     :str	       =						DataLoader(
          tokenized_datasets['''validation''']				,     shuffle=a_				,     collate_fn=a_				,     batch_size=a_							)

      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_  :	Any				,     a_  :	List[Any]							)    ->   List[str]:
      # For testing only
      if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     a_							) == "1":
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						2
      # Initialize accelerator
      __SCREAMING_SNAKE_CASE     :List[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     :Dict	       =						config['''lr''']
      __SCREAMING_SNAKE_CASE     :int	       =						int(config['''num_epochs''']							)
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						int(config['''seed''']							)
      __SCREAMING_SNAKE_CASE     :Any	       =						int(config['''batch_size''']							)

      # New Code #
      # Parse out whether we are saving every epoch or after a certain number of batches
      if hasattr(args.checkpointing_steps				,     '''isdigit'''							):
            if args.checkpointing_steps == "epoch":
                  __SCREAMING_SNAKE_CASE     :Dict	       =						args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  __SCREAMING_SNAKE_CASE     :List[str]	       =						int(args.checkpointing_steps							)
            else:
                  raise ValueError(
                      f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
      else:
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						None

      set_seed(a_							)

      __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[str]	       =						get_dataloaders(a_				,     a_							)
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						evaluate.load('''glue'''				,     '''mrpc'''							)

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

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=a_							)

      # 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[Any]	       =						model.to(accelerator.device							)

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

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

      # New Code #
      # We need to keep track of how many total steps we have iterated over
      __SCREAMING_SNAKE_CASE     :int	       =						0
      # We also need to keep track of the stating epoch so files are named properly
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						0

      # We need to load the checkpoint back in before training here with `load_state`
      # The total number of epochs is adjusted based on where the state is being loaded from,
      # as we assume continuation of the same training script
      if args.resume_from_checkpoint:
            if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                  accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''							)
                  accelerator.load_state(args.resume_from_checkpoint							)
                  __SCREAMING_SNAKE_CASE     :Any	       =						os.path.basename(args.resume_from_checkpoint							)
            else:
                  # Get the most recent checkpoint
                  __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
                  dirs.sort(key=os.path.getctime							)
                  __SCREAMING_SNAKE_CASE     :List[Any]	       =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            __SCREAMING_SNAKE_CASE     :int	       =						os.path.splitext(a_							)[0]

            if "epoch" in training_difference:
                  __SCREAMING_SNAKE_CASE     :str	       =						int(training_difference.replace('''epoch_'''				,     ''''''							)							) + 1
                  __SCREAMING_SNAKE_CASE     :Dict	       =						None
            else:
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						int(training_difference.replace('''step_'''				,     ''''''							)							)
                  __SCREAMING_SNAKE_CASE     :Optional[int]	       =						resume_step // len(a_							)
                  resume_step -= starting_epoch * len(a_							)

    # Now we train the model
      for epoch in range(a_				,     a_							):
            model.train()
            # New Code #
            if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                  # We need to skip steps until we reach the resumed step
                  __SCREAMING_SNAKE_CASE     :Optional[int]	       =						accelerator.skip_first_batches(a_				,     a_							)
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  __SCREAMING_SNAKE_CASE     :str	       =						train_dataloader
            for step, batch in enumerate(a_							):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device							)
                  __SCREAMING_SNAKE_CASE     :List[str]	       =						model(**a_							)
                  __SCREAMING_SNAKE_CASE     :List[str]	       =						outputs.loss
                  __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						loss / gradient_accumulation_steps
                  accelerator.backward(a_							)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()
                  # New Code #
                  overall_step += 1

                  # New Code #
                  # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                  # These are saved to folders named `step_{overall_step}`
                  # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                  # If mixed precision was used, will also save a "scalar.bin" file
                  if isinstance(a_				,     a_							):
                        __SCREAMING_SNAKE_CASE     :str	       =						f'''step_{overall_step}'''
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    __SCREAMING_SNAKE_CASE     :Tuple	       =						os.path.join(args.output_dir				,     a_							)
                              accelerator.save_state(a_							)

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

            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'''epoch {epoch}:'''				,     a_							)

            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `epoch_{epoch}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if checkpointing_steps == "epoch":
                  __SCREAMING_SNAKE_CASE     :List[Any]	       =						f'''epoch_{epoch}'''
                  if args.output_dir is not None:
                        __SCREAMING_SNAKE_CASE     :Dict	       =						os.path.join(args.output_dir				,     a_							)
                  accelerator.save_state(a_							)



def   __lowerCamelCase     (							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						argparse.ArgumentParser(description='''Simple example of training script.'''							)
      parser.add_argument(
          '''--mixed_precision'''				,     type=a_				,     default=a_				,     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(
          '''--checkpointing_steps'''				,     type=a_				,     default=a_				,     help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''				,     )
      parser.add_argument(
          '''--output_dir'''				,     type=a_				,     default='''.'''				,     help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,     )
      parser.add_argument(
          '''--resume_from_checkpoint'''				,     type=a_				,     default=a_				,     help='''If the training should continue from a checkpoint folder.'''				,     )
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						parser.parse_args()
      __SCREAMING_SNAKE_CASE     :int	       =						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(a_				,     a_							)


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()