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


lowercase_ 				=			16
lowercase_ 				=			32


def 							a__					(							snake_case       ,			snake_case = 16							):





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

  def tokenize_function(snake_case							):
    # max_length=None => use the model max length (it's actually the default)
    __SCREAMING_SNAKE_CASE      :	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():
    __SCREAMING_SNAKE_CASE      :	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
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		tokenized_datasets.rename_column('''label'''       ,			'''labels'''							)

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

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

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

   lowercase_ 				=			mocked_dataloaders  # noqa: F811


def 							a__					(							snake_case       ,			snake_case							):





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

  __SCREAMING_SNAKE_CASE      :	int					=		evaluate.load('''glue'''       ,			'''mrpc'''							)

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

  # Instantiate optimizer
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		AdamW(params=model.parameters()       ,			lr=snake_case							)

  # Instantiate scheduler
  __SCREAMING_SNAKE_CASE      :	List[str]					=		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.
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	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							):
          __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		model(**snake_case							)
          __SCREAMING_SNAKE_CASE      :	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():
        __SCREAMING_SNAKE_CASE      :	Optional[int]					=		model(**snake_case							)
      __SCREAMING_SNAKE_CASE      :	List[Any]					=		outputs.logits.argmax(dim=-1							)
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[int]					=		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
      metric.add_batch(
          predictions=snake_case       ,			references=snake_case       ,			)

    __SCREAMING_SNAKE_CASE      :	Dict					=		metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}:'''       ,			snake_case							)


def 							a__					(							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	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.'''							)
  __SCREAMING_SNAKE_CASE      :	str					=		parser.parse_args()
  __SCREAMING_SNAKE_CASE      :	int					=		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(snake_case       ,			snake_case							)


if __name__ == "__main__":
   main()



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

    @property
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      1
     SCREAMING_SNAKE_CASE		    =      3
     SCREAMING_SNAKE_CASE		    =      (3_2, 3_2)

     SCREAMING_SNAKE_CASE		    =      floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0     )     ).to(snake_case__     )
     return image

    @property
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      UNetaDConditionModel(
         block_out_channels=(3_2, 6_4)				,     layers_per_block=2				,     sample_size=3_2				,     in_channels=4				,     out_channels=4				,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,     cross_attention_dim=3_2				,     )
     return model

    @property
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      AutoencoderKL(
         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=4				,     )
     return model

    @property
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      RobertaSeriesConfig(
         hidden_size=3_2				,     project_dim=3_2				,     intermediate_size=3_7				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=5_0_0_6				,     )
     return RobertaSeriesModelWithTransformation(snake_case__     )

    @property
    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     def extract(*snake_case__			:   List[Any]				,     **snake_case__			:   Union[str, Any]     ):




      class   UpperCamelCase			:

          def __init__(      self			:   str     ):

           """simple docstring"""






           SCREAMING_SNAKE_CASE		    =      torch.ones([0]     )




          def UpperCamelCase      (      self			:   Any				,     snake_case__			:   List[str]     ):

           """simple docstring"""






           self.pixel_values.to(snake_case__     )
           return self


      return Out()

     return extract

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet
     SCREAMING_SNAKE_CASE		    =      PNDMScheduler(skip_prk_steps=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'     )
     SCREAMING_SNAKE_CASE		    =      7_7

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      init_image / 2 + 0.5

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      AltDiffusionImgaImgPipeline(
         unet=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     safety_checker=snake_case__				,     feature_extractor=self.dummy_extractor				,     )
     SCREAMING_SNAKE_CASE		    =      VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,     do_normalize=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      alt_pipe.to(snake_case__     )
     alt_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      alt_pipe(
         [prompt]				,     generator=snake_case__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type='np'				,     image=snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      output.images

     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      alt_pipe(
         [prompt]				,     generator=snake_case__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type='np'				,     image=snake_case__				,     return_dict=snake_case__				,     )[0]

     SCREAMING_SNAKE_CASE		    =      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE		    =      image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 3_2, 3_2, 3)
     SCREAMING_SNAKE_CASE		    =      np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-3
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 5E-3

    @unittest.skipIf(torch_device != 'cuda'				,     'This test requires a GPU'     )
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet
     SCREAMING_SNAKE_CASE		    =      PNDMScheduler(skip_prk_steps=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'     )
     SCREAMING_SNAKE_CASE		    =      7_7

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.to(snake_case__     )

     # put models in fp16
     SCREAMING_SNAKE_CASE		    =      unet.half()
     SCREAMING_SNAKE_CASE		    =      vae.half()
     SCREAMING_SNAKE_CASE		    =      bert.half()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      AltDiffusionImgaImgPipeline(
         unet=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     safety_checker=snake_case__				,     feature_extractor=self.dummy_extractor				,     )
     SCREAMING_SNAKE_CASE		    =      VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,     do_normalize=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      alt_pipe.to(snake_case__     )
     alt_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      alt_pipe(
         [prompt]				,     generator=snake_case__				,     num_inference_steps=2				,     output_type='np'				,     image=snake_case__				,     ).images

     assert image.shape == (1, 3_2, 3_2, 3)




    @unittest.skipIf(torch_device != 'cuda'				,     'This test requires a GPU'     )
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/img2img/sketch-mountains-input.jpg'     )
     # resize to resolution that is divisible by 8 but not 16 or 32
     SCREAMING_SNAKE_CASE		    =      init_image.resize((7_6_0, 5_0_4)     )

     SCREAMING_SNAKE_CASE		    =      'BAAI/AltDiffusion'
     SCREAMING_SNAKE_CASE		    =      AltDiffusionImgaImgPipeline.from_pretrained(
         snake_case__				,     safety_checker=snake_case__				,     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE		    =      'A fantasy landscape, trending on artstation'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     strength=0.75				,     guidance_scale=7.5				,     generator=snake_case__				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images[0]

     SCREAMING_SNAKE_CASE		    =      image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert image.shape == (5_0_4, 7_6_0, 3)
     SCREAMING_SNAKE_CASE		    =      np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2




@slow
@require_torch_gpu
class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/img2img/sketch-mountains-input.jpg'     )
     SCREAMING_SNAKE_CASE		    =      init_image.resize((7_6_8, 5_1_2)     )
     SCREAMING_SNAKE_CASE		    =      load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'     )

     SCREAMING_SNAKE_CASE		    =      'BAAI/AltDiffusion'
     SCREAMING_SNAKE_CASE		    =      AltDiffusionImgaImgPipeline.from_pretrained(
         snake_case__				,     safety_checker=snake_case__				,     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE		    =      'A fantasy landscape, trending on artstation'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     strength=0.75				,     guidance_scale=7.5				,     generator=snake_case__				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images[0]

     assert image.shape == (5_1_2, 7_6_8, 3)
     # img2img is flaky across GPUs even in fp32, so using MAE here
     assert np.abs(expected_image - image     ).max() < 1E-2

