






'''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
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    	=   1_6
__A    	=   3_2




def        _SCREAMING_SNAKE_CASE  (				A  :       Accelerator  ,		A  :       int = 16  )    -> Optional[int]:







   """simple docstring"""
   __snake_case				:      str     							= AutoTokenizer.from_pretrained('bert-base-cased'  )
   __snake_case				:      Optional[int]     							= load_dataset('glue'  ,		'mrpc'  )

   def tokenize_function(A  :       int  ):
      # max_length=None => use the model max length (it's actually the default)
      __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():
      __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
   __snake_case				:      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.
      __snake_case				:      Tuple     							= 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":
         __snake_case				:      Union[str, Any]     							= 16
      elif accelerator.mixed_precision != "no":
         __snake_case				:      Tuple     							= 8
      else:
         __snake_case				:      int     							= None

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

   # Instantiate dataloaders.
   __snake_case				:      str     							= DataLoader(
       tokenized_datasets['train']  ,		shuffle=A  ,		collate_fn=A  ,		batch_size=A  )
   __snake_case				:      Any     							= 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

      __A    	=   mocked_dataloaders  # noqa: F811




def        _SCREAMING_SNAKE_CASE  (				A  :       str  ,		A  :       Tuple  )    -> List[Any]:







   """simple docstring"""
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,		A  ) == "1":
      __snake_case				:      Union[str, Any]     							= 2
   # New Code #
   __snake_case				:      int     							= int(args.gradient_accumulation_steps  )
   __snake_case				:      Optional[Any]     							= int(args.local_sgd_steps  )
   # Initialize accelerator
   __snake_case				:      Optional[Any]     							= Accelerator(
       cpu=args.cpu  ,		mixed_precision=args.mixed_precision  ,		gradient_accumulation_steps=A  )
   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
   __snake_case				:      Dict     							= config['lr']
   __snake_case				:      int     							= int(config['num_epochs']  )
   __snake_case				:      List[Any]     							= int(config['seed']  )
   __snake_case				:      Optional[int]     							= int(config['batch_size']  )

   __snake_case				:      str     							= evaluate.load('glue'  ,		'mrpc'  )

   set_seed(A  )
   __snake_case							,__snake_case				:      Optional[Any]     							= get_dataloaders(A  ,		A  )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __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).
   __snake_case				:      Dict     							= model.to(accelerator.device  )

   # Instantiate optimizer
   __snake_case				:      int     							= AdamW(params=model.parameters()  ,		lr=A  )

   # Instantiate scheduler
   __snake_case				:      Tuple     							= get_linear_schedule_with_warmup(
       optimizer=A  ,		num_warmup_steps=1_00  ,		num_training_steps=(len(A  ) * 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.
   __snake_case							,__snake_case							,__snake_case							,__snake_case							,__snake_case				:      int     							= accelerator.prepare(
       A  ,		A  ,		A  ,		A  ,		A  )

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

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

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




def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:







   """simple docstring"""
   __snake_case				:      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.'  ,		)
   # New Code #
   parser.add_argument(
       '--gradient_accumulation_steps'  ,		type=A  ,		default=1  ,		help='The number of minibatches to be ran before gradients are accumulated.'  ,		)
   parser.add_argument(
       '--local_sgd_steps'  ,		type=A  ,		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.'  )
   __snake_case				:      Any     							= parser.parse_args()
   __snake_case				:      List[Any]     							= {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(A  ,		A  )


if __name__ == "__main__":
      main()







'''simple docstring'''






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class a_    (    UpperCamelCase_		,      UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							VQModel
   _snake_case        =							"""sample"""







   @property
   def     SCREAMING_SNAKE_CASE__					(self					,   __a=(3_2, 3_2))			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= 4
      __snake_case				:      Optional[int]     							= 3

      __snake_case				:      str     							= floats_tensor((batch_size, num_channels) + sizes).to(__a)

      return {"sample": image}







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= {
          'block_out_channels': [3_2, 6_4],
          'in_channels': 3,
          'out_channels': 3,
          'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
          'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
          'latent_channels': 3,
      }
      __snake_case				:      List[Any]     							= self.dummy_input
      return init_dict, inputs_dict







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case							,__snake_case				:      List[Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy'					,   output_loading_info=__a)
      self.assertIsNotNone(__a)
      self.assertEqual(len(loading_info['missing_keys'])					,   0)

      model.to(__a)
      __snake_case				:      Any     							= model(**self.dummy_input)

      assert image is not None, "Make sure output is not None"







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy')
      model.to(__a).eval()

      torch.manual_seed(0)
      if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0)

      __snake_case				:      Tuple     							= torch.randn(1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size)
      __snake_case				:      Optional[int]     							= image.to(__a)
      with torch.no_grad():
         __snake_case				:      List[Any]     							= model(__a).sample

      __snake_case				:      int     							= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      __snake_case				:      int     							= torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143])
      # fmt: on
      self.assertTrue(torch.allclose(__a					,   __a					,   atol=1E-3))