


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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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      :    str		=					16
A      :    List[str]		=					32

def       lowercase_		(		_A			:    Optional[Any]    ,					_A			:    Tuple = 16						):

      """simple docstring"""




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

      def tokenize_function(_A			:    Optional[Any]						):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase__		:				int    =  tokenizer(examples["sentence1"]    ,					examples["sentence2"]    ,					truncation=__snake_case    ,					max_length=__snake_case						)
            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():
            lowerCamelCase__		:				Optional[int]    =  datasets.map(
                __snake_case    ,					batched=__snake_case    ,					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
      lowerCamelCase__		:				Union[str, Any]    =  tokenized_datasets.rename_column("label"    ,					"labels"						)

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

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

      # Instantiate dataloaders.
      lowerCamelCase__		:				List[str]    =  DataLoader(
          tokenized_datasets["train"]    ,					shuffle=__snake_case    ,					collate_fn=__snake_case    ,					batch_size=__snake_case						)
      lowerCamelCase__		:				List[Any]    =  DataLoader(
          tokenized_datasets["validation"]    ,					shuffle=__snake_case    ,					collate_fn=__snake_case    ,					batch_size=__snake_case						)

      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      :    Optional[int]		=					mocked_dataloaders  # noqa: F811

def       lowercase_		(		_A			:    List[Any]    ,					_A			:    Optional[int]						):

      """simple docstring"""




      if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,					__snake_case						) == "1":
            lowerCamelCase__		:				Any    =  2
      # New Code #
      lowerCamelCase__		:				Union[str, Any]    =  int(args.gradient_accumulation_steps						)
      lowerCamelCase__		:				Dict    =  int(args.local_sgd_steps						)
      # Initialize accelerator
      lowerCamelCase__		:				Tuple    =  Accelerator(
          cpu=args.cpu    ,					mixed_precision=args.mixed_precision    ,					gradient_accumulation_steps=__snake_case						)
      if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
            raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCamelCase__		:				str    =  config["lr"]
      lowerCamelCase__		:				Optional[Any]    =  int(config["num_epochs"]						)
      lowerCamelCase__		:				Tuple    =  int(config["seed"]						)
      lowerCamelCase__		:				Union[str, Any]    =  int(config["batch_size"]						)

      lowerCamelCase__		:				Tuple    =  evaluate.load("glue"    ,					"mrpc"						)

      set_seed(__snake_case						)
      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  get_dataloaders(__snake_case    ,					__snake_case						)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase__		:				List[Any]    =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    ,					return_dict=__snake_case						)

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

      # Instantiate optimizer
      lowerCamelCase__		:				List[str]    =  AdamW(params=model.parameters()    ,					lr=__snake_case						)

      # Instantiate scheduler
      lowerCamelCase__		:				Optional[Any]    =  get_linear_schedule_with_warmup(
          optimizer=__snake_case    ,					num_warmup_steps=100    ,					num_training_steps=(len(__snake_case						) * 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.
      lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				int    =  accelerator.prepare(
          __snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case						)

      # Now we train the model
      for epoch in range(__snake_case						):
            model.train()
            with LocalSGD(
                accelerator=__snake_case    ,					model=__snake_case    ,					local_sgd_steps=__snake_case    ,					enabled=local_sgd_steps is not None						) as local_sgd:
                  for step, batch in enumerate(__snake_case						):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device						)
                        # New code #
                        # We use the new `accumulate` context manager to perform gradient accumulation
                        # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                        with accelerator.accumulate(__snake_case						):
                              lowerCamelCase__		:				Dict    =  model(**__snake_case						)
                              lowerCamelCase__		:				Optional[Any]    =  output.loss
                              accelerator.backward(__snake_case						)
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                              # LocalSGD-specific line
                              local_sgd.step()

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

            lowerCamelCase__		:				Tuple    =  metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"epoch {epoch}:"    ,					__snake_case						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Union[str, Any]    =  argparse.ArgumentParser(description="Simple example of training script."						)
      parser.add_argument(
          "--mixed_precision"    ,					type=__snake_case    ,					default=__snake_case    ,					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."    ,					)
      # New Code #
      parser.add_argument(
          "--gradient_accumulation_steps"    ,					type=__snake_case    ,					default=1    ,					help="The number of minibatches to be ran before gradients are accumulated."    ,					)
      parser.add_argument(
          "--local_sgd_steps"    ,					type=__snake_case    ,					default=8    ,					help="Number of local SGD steps or None to disable local SGD"						)
      parser.add_argument("--cpu"    ,					action="store_true"    ,					help="If passed, will train on the CPU."						)
      lowerCamelCase__		:				Tuple    =  parser.parse_args()
      lowerCamelCase__		:				str    =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(__snake_case    ,					__snake_case						)


if __name__ == "__main__":
    main()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

