


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

__lowercase    =				16
__lowercase    =				32


def 					lowercase      (    A_  ,       A_ = 16	)->				int:
  '''simple docstring'''







  a						:		int											=      AutoTokenizer.from_pretrained("bert-base-cased"	)
  a						:		List[str]											=      load_dataset("glue"  ,       "mrpc"	)

  def tokenize_function(A_	):
    # max_length=None => use the model max length (it's actually the default)
    a						:		Optional[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():
    a						:		str											=      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
  a						:		Tuple											=      tokenized_datasets.rename_column("label"  ,       "labels"	)

  def collate_fn(A_	):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a						:		Dict											=      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":
      a						:		List[str]											=      16
    elif accelerator.mixed_precision != "no":
      a						:		int											=      8
    else:
      a						:		Tuple											=      None

    return tokenizer.pad(
        A_  ,       padding="longest"  ,       max_length=A_  ,       pad_to_multiple_of=A_  ,       return_tensors="pt"  ,       )

  # Instantiate dataloaders.
  a						:		List[Any]											=      DataLoader(
      tokenized_datasets["train"]  ,       shuffle=A_  ,       collate_fn=A_  ,       batch_size=A_	)
  a						:		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

 __lowercase    =				mocked_dataloaders  # noqa: F811


def 					lowercase      (    A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







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

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

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

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

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

  # Instantiate scheduler
  a						:		List[Any]											=      get_linear_schedule_with_warmup(
      optimizer=A_  ,       num_warmup_steps=100  ,       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.
  a							,		a							,		a							,		a							,		a						:		int											=      accelerator.prepare(
      A_  ,       A_  ,       A_  ,       A_  ,       A_	)

  # New Code #
  # We need to initialize the trackers we use. Overall configurations can also be stored
  if args.with_tracking:
    a						:		str											=      os.path.split(A_	)[-1].split("."	)[0]
    accelerator.init_trackers(A_  ,       A_	)

  # Now we train the model
  for epoch in range(A_	):
    model.train()
    # New Code #
    # For our tracking example, we will log the total loss of each epoch
    if args.with_tracking:
      a						:		Optional[Any]											=      0
    for step, batch in enumerate(A_	):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device	)
      a						:		int											=      model(**A_	)
      a						:		List[Any]											=      outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      a						:		Dict											=      loss / gradient_accumulation_steps
      accelerator.backward(A_	)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    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():
        a						:		Dict											=      model(**A_	)
      a						:		int											=      outputs.logits.argmax(dim=-1	)
      a							,		a						:		str											=      accelerator.gather_for_metrics((predictions, batch["labels"])	)
      metric.add_batch(
          predictions=A_  ,       references=A_  ,       )

    a						:		Optional[int]											=      metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}:'''  ,       A_	)

    # 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(A_	),
              "epoch": epoch,
          }  ,       step=A_  ,       )

    # 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 					lowercase      (    )->				Any:
  '''simple docstring'''







  a						:		Union[str, 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(
      "--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=A_  ,       default="logs"  ,       help="Location on where to store experiment tracking logs` and relevent project information"  ,       )
  a						:		List[Any]											=      parser.parse_args()
  a						:		str											=      {"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
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment


def 					lowercase      (    )->				Union[str, Any]:
  '''simple docstring'''







  a						:		Union[str, Any]											=      torch.nn.Linear(2  ,       4	)
  a						:		Tuple											=      torch.optim.AdamW(model.parameters()  ,       lr=1.0	)
  a						:		Union[str, Any]											=      torch.optim.lr_scheduler.OneCycleLR(A_  ,       max_lr=0.0_1  ,       steps_per_epoch=2  ,       epochs=1	)
  a						:		List[str]											=      DataLoader(TensorDataset(torch.tensor([1, 2, 3]	)	)	)
  a						:		int											=      DataLoader(TensorDataset(torch.tensor([4, 5, 6]	)	)	)

  return model, optimizer, scheduler, train_dl, valid_dl


def 					lowercase      (    A_	)->				List[Any]:
  '''simple docstring'''







  return (model.weight.abs().sum() + model.bias.abs().sum()).item()


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		Optional[int]											=      torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
  model.load_state_dict(A_	)







class 						_A  (  _a  ):







    """simple docstring"""




    @require_cuda
    def      __snake_case							( self    :   Any):
      a						:		List[str]											=      Accelerator()
      assert PartialState._shared_state["_cpu"] is False
      assert PartialState._shared_state["device"].type == "cuda"
      with self.assertRaises(__UpperCAmelCase):
        a						:		Any											=      Accelerator(cpu=__UpperCAmelCase)






    def      __snake_case							( self    :   List[Any]):
      a						:		str											=      Accelerator()
      a						:		Optional[Any]											=      GradientState()
      assert state.num_steps == 1
      a						:		Dict											=      4
      assert state.num_steps == 4

      assert state.sync_gradients is True
      a						:		Optional[int]											=      False
      assert state.sync_gradients is False
      GradientState._reset_state()






    def      __snake_case							( self    :   str):
      a						:		int											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Tuple											=      create_components()

      (
          (
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		
      )						:		Tuple											=      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      self.assertTrue(prepared_model in accelerator._models)
      self.assertTrue(prepared_optimizer in accelerator._optimizers)
      self.assertTrue(prepared_scheduler in accelerator._schedulers)
      self.assertTrue(prepared_train_dl in accelerator._dataloaders)
      self.assertTrue(prepared_valid_dl in accelerator._dataloaders)






    def      __snake_case							( self    :   Dict):
      a						:		Dict											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Any											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      accelerator.free_memory()

      self.assertTrue(len(accelerator._models) == 0)
      self.assertTrue(len(accelerator._optimizers) == 0)
      self.assertTrue(len(accelerator._schedulers) == 0)
      self.assertTrue(len(accelerator._dataloaders) == 0)






    def      __snake_case							( self    :   int):
      PartialState._reset_state()

      # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
      def noop(*__UpperCAmelCase    :   Union[str, Any]    ,			**__UpperCAmelCase    :   Any):
        pass

      with patch("torch.cuda.set_device"    ,			__UpperCAmelCase), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"):
        a						:		int											=      Accelerator()
        self.assertEqual(str(accelerator.state.device)    ,			"cuda:64")






    def      __snake_case							( self    :   List[str]):
      a						:		Tuple											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Optional[Any]											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Dict											=      get_signature(__UpperCAmelCase)

      with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(__UpperCAmelCase)

        # make sure random weights don't match
        load_random_weights(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)

        # make sure loaded weights match
        accelerator.load_state(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)






    def      __snake_case							( self    :   Optional[int]):
      a						:		str											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Dict											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Union[str, Any]											=      get_signature(__UpperCAmelCase)

      # saving hook
      def save_config(__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Union[str, Any]):
        a						:		Tuple											=      {"class_name": models[0].__class__.__name__}

        with open(os.path.join(__UpperCAmelCase    ,			"data.json")    ,			"w") as f:
          json.dump(__UpperCAmelCase    ,			__UpperCAmelCase)

        # loading hook
      def load_config(__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Union[str, Any]):
        with open(os.path.join(__UpperCAmelCase    ,			"data.json")    ,			"r") as f:
          a						:		Optional[Any]											=      json.load(__UpperCAmelCase)

        a						:		Tuple											=      config["class_name"]

      a						:		Optional[int]											=      accelerator.register_save_state_pre_hook(__UpperCAmelCase)
      a						:		Tuple											=      accelerator.register_load_state_pre_hook(__UpperCAmelCase)

      with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(__UpperCAmelCase)

        # make sure random weights don't match with hooks
        load_random_weights(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)

        # random class name to verify correct one is loaded
        a						:		int											=      "random"

        # make sure loaded weights match with hooks
        accelerator.load_state(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)

        # mode.class_name is loaded from config
        self.assertTrue(model.class_name == model.__class__.__name__)

      # remove hooks
      save_hook.remove()
      load_hook.remove()

      with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(__UpperCAmelCase)

        # make sure random weights don't match with hooks removed
        load_random_weights(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)

        # random class name to verify correct one is loaded
        a						:		Dict											=      "random"

        # make sure loaded weights match with hooks removed
        accelerator.load_state(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)

        # mode.class_name is NOT loaded from config
        self.assertTrue(model.class_name != model.__class__.__name__)






    def      __snake_case							( self    :   Optional[Any]):
      a						:		List[str]											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		int											=      create_components()
      a						:		Tuple											=      None

      # This should work
      a							,		a							,		a							,		a							,		a							,		a						:		Any											=      accelerator.prepare(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      self.assertTrue(dummy_obj is None)






    def      __snake_case							( self    :   List[str]):
      a						:		str											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		List[Any]											=      create_components()
      a						:		Union[str, Any]											=      [1, 2, 3]

      # This should work
      a							,		a							,		a							,		a							,		a							,		a						:		str											=      accelerator.prepare(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Dummy object should have `_is_accelerate_prepared` set to `True`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Model is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Optimizer is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Scheduler is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,			)






    @slow
    @require_bnb
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Optional[int]											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map={"": 0}    ,			)
      a						:		Tuple											=      Accelerator()

      # This should work
      a						:		List[Any]											=      accelerator.prepare(__UpperCAmelCase)






    @slow
    @require_bnb
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Dict											=      Accelerator()

      with init_empty_weights():
        a						:		Any											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Union[str, Any]											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      "cpu"

      a						:		int											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			device_map=__UpperCAmelCase    ,			load_in_abit=__UpperCAmelCase    ,			llm_inta_enable_fpaa_cpu_offload=__UpperCAmelCase)

      # This should not work and get value error
      with self.assertRaises(__UpperCAmelCase):
        a						:		Optional[int]											=      accelerator.prepare(__UpperCAmelCase)






    @slow
    @require_bnb
    @require_multi_gpu
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Union[str, Any]											=      {"distributed_type": DistributedType.MULTI_GPU}

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Any											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		Dict											=      1

      a						:		Any											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		int											=      Accelerator()

      # This should not work and get value error
      with self.assertRaises(__UpperCAmelCase):
        a						:		Optional[int]											=      accelerator.prepare(__UpperCAmelCase)

      PartialState._reset_state()






    @slow
    @require_bnb
    @require_multi_gpu
    def      __snake_case							( self    :   Tuple):
      from transformers import AutoModelForCausalLM

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        a						:		Tuple											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      1

      a						:		Optional[int]											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		str											=      Accelerator()

      # This should work
      a						:		Any											=      accelerator.prepare(__UpperCAmelCase)






    @require_cuda
    def      __snake_case							( self    :   List[Any]):
      a						:		Tuple											=      torch.nn.Linear(10    ,			10)
      a						:		int											=      torch.optim.SGD(model.parameters()    ,			lr=0.01)
      a						:		Optional[Any]											=      Accelerator(cpu=__UpperCAmelCase)
      a						:		List[str]											=      accelerator.prepare(__UpperCAmelCase)






