


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
#
########################################################################


SCREAMING_SNAKE_CASE							    =							1_6
SCREAMING_SNAKE_CASE							    =							3_2




def  a   (lowerCAmelCase__		,				lowerCAmelCase__ = 16   ):
   __a					=  AutoTokenizer.from_pretrained("""bert-base-cased"""   )
   __a					=  load_dataset("""glue"""		,				"""mrpc"""   )

   def tokenize_function(lowerCAmelCase__   ):
      # max_length=None => use the model max length (it's actually the default)
      __a					=  tokenizer(examples["""sentence1"""]		,				examples["""sentence2"""]		,				truncation=lowerCAmelCase__		,				max_length=lowerCAmelCase__   )
      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					=  datasets.map(
          lowerCAmelCase__		,				batched=lowerCAmelCase__		,				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					=  tokenized_datasets.rename_column("""label"""		,				"""labels"""   )

   def collate_fn(lowerCAmelCase__   ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      __a					=  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					=  16
      elif accelerator.mixed_precision != "no":
         __a					=  8
      else:
         __a					=  None

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

   # Instantiate dataloaders.
   __a					=  DataLoader(
       tokenized_datasets["""train"""]		,				shuffle=lowerCAmelCase__		,				collate_fn=lowerCAmelCase__		,				batch_size=lowerCAmelCase__   )
   __a					=  DataLoader(
       tokenized_datasets["""validation"""]		,				shuffle=lowerCAmelCase__		,				collate_fn=lowerCAmelCase__		,				batch_size=lowerCAmelCase__   )

   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

  SCREAMING_SNAKE_CASE							    =							mocked_dataloaders  # noqa: F811




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   # For testing only
   if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,				lowerCAmelCase__   ) == "1":
      __a					=  2
   # New Code #
   __a					=  int(args.gradient_accumulation_steps   )
   __a					=  int(args.local_sgd_steps   )
   # Initialize accelerator
   __a					=  Accelerator(
       cpu=args.cpu		,				mixed_precision=args.mixed_precision		,				gradient_accumulation_steps=lowerCAmelCase__   )
   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
   __a					=  config["""lr"""]
   __a					=  int(config["""num_epochs"""]   )
   __a					=  int(config["""seed"""]   )
   __a					=  int(config["""batch_size"""]   )

   __a					=  evaluate.load("""glue"""		,				"""mrpc"""   )

   set_seed(lowerCAmelCase__   )
   __a	,    __a					=  get_dataloaders(lowerCAmelCase__		,				lowerCAmelCase__   )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __a					=  AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,				return_dict=lowerCAmelCase__   )

   # 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					=  model.to(accelerator.device   )

   # Instantiate optimizer
   __a					=  AdamW(params=model.parameters()		,				lr=lowerCAmelCase__   )

   # Instantiate scheduler
   __a					=  get_linear_schedule_with_warmup(
       optimizer=lowerCAmelCase__		,				num_warmup_steps=100		,				num_training_steps=(len(lowerCAmelCase__   ) * 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.
   __a	,    __a	,    __a	,    __a	,    __a					=  accelerator.prepare(
       lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )

   # Now we train the model
   for epoch in range(lowerCAmelCase__   ):
      model.train()
      with LocalSGD(
          accelerator=lowerCAmelCase__		,				model=lowerCAmelCase__		,				local_sgd_steps=lowerCAmelCase__		,				enabled=local_sgd_steps is not None   ) as local_sgd:
         for step, batch in enumerate(lowerCAmelCase__   ):
            # 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(lowerCAmelCase__   ):
               __a					=  model(**lowerCAmelCase__   )
               __a					=  output.loss
               accelerator.backward(lowerCAmelCase__   )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

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

      __a					=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''		,				lowerCAmelCase__   )




def  a   ():
   __a					=  argparse.ArgumentParser(description="""Simple example of training script."""   )
   parser.add_argument(
       """--mixed_precision"""		,				type=lowerCAmelCase__		,				default=lowerCAmelCase__		,				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=lowerCAmelCase__		,				default=1		,				help="""The number of minibatches to be ran before gradients are accumulated."""		,				)
   parser.add_argument(
       """--local_sgd_steps"""		,				type=lowerCAmelCase__		,				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."""   )
   __a					=  parser.parse_args()
   __a					=  {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(lowerCAmelCase__		,				lowerCAmelCase__   )


if __name__ == "__main__":
  main()











'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .multicontrolnet import MultiControlNetModel
      from .pipeline_controlnet import StableDiffusionControlNetPipeline
      from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
      from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
      from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline






