



import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort



class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       # TODO: is there an appropriate internal test set?
       UpperCAmelCase__    : Tuple       					=  "ssube/stable-diffusion-x4-upscaler-onnx"
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_=0	)					->		int:
          UpperCamelCase   :  Tuple						     =     floats_tensor((1, 3, 128, 128),      rng=random.Random(SCREAMING_SNAKE_CASE_	)	)
          UpperCamelCase   :  str						     =     torch.manual_seed(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     {
              'prompt': 'A painting of a squirrel eating a burger',
              'image': image,
              'generator': generator,
              'num_inference_steps': 3,
              'guidance_scale': 7.5,
              'output_type': 'numpy',
          }
          return inputs
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Tuple						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Union[str, Any]						     =     self.get_dummy_inputs()
          UpperCamelCase   :  Optional[int]						     =     pipe(**SCREAMING_SNAKE_CASE_	).images
          UpperCamelCase   :  Optional[int]						     =     image[0, -3:, -3:, -1].flatten()

          # started as 128, should now be 512
          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]	)
          assert np.abs(image_slice - expected_slice	).max() < 1e-1
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  List[Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  Any						     =     PNDMScheduler.from_config(pipe.scheduler.config,      skip_prk_steps=SCREAMING_SNAKE_CASE_	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     self.get_dummy_inputs()
          UpperCamelCase   :  int						     =     pipe(**SCREAMING_SNAKE_CASE_	).images
          UpperCamelCase   :  Optional[Any]						     =     image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Tuple						     =     np.array(
              [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64]	)
          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Union[str, Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  int						     =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     self.get_dummy_inputs()
          UpperCamelCase   :  Dict						     =     pipe(**SCREAMING_SNAKE_CASE_	).images
          UpperCamelCase   :  Any						     =     image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  List[Any]						     =     np.array(
              [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15]	)

          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Dict						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  str						     =     EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     self.get_dummy_inputs()
          UpperCamelCase   :  List[str]						     =     pipe(**SCREAMING_SNAKE_CASE_	).images
          UpperCamelCase   :  Any						     =     image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]	)
          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1






       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Union[str, Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  Optional[int]						     =     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     self.get_dummy_inputs()
          UpperCamelCase   :  List[str]						     =     pipe(**SCREAMING_SNAKE_CASE_	).images
          UpperCamelCase   :  str						     =     image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43]	)

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






@nightly
@require_onnxruntime
@require_torch_gpu
class     lowerCAmelCase_		(  unittest.TestCase							):
       @property
       def  snake_case_	(      self	)					->		Tuple:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )
       @property
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  str						     =     ort.SessionOptions()
          UpperCamelCase   :  str						     =     False
          return options
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  List[str]						     =     load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/img2img/sketch-mountains-input.jpg'	)
          UpperCamelCase   :  Any						     =     init_image.resize((128, 128)	)
          # using the PNDM scheduler by default
          UpperCamelCase   :  Any						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(
              'ssube/stable-diffusion-x4-upscaler-onnx',      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     'A fantasy landscape, trending on artstation'

          UpperCamelCase   :  int						     =     torch.manual_seed(0	)
          UpperCamelCase   :  Optional[int]						     =     pipe(
              prompt=SCREAMING_SNAKE_CASE_,      image=SCREAMING_SNAKE_CASE_,      guidance_scale=7.5,      num_inference_steps=10,      generator=SCREAMING_SNAKE_CASE_,      output_type='np',      )
          UpperCamelCase   :  Union[str, Any]						     =     output.images
          UpperCamelCase   :  Tuple						     =     images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 512, 3)
          UpperCamelCase   :  str						     =     np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72]	)
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  int						     =     load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/img2img/sketch-mountains-input.jpg'	)
          UpperCamelCase   :  List[str]						     =     init_image.resize((128, 128)	)
          UpperCamelCase   :  List[Any]						     =     LMSDiscreteScheduler.from_pretrained(
              'ssube/stable-diffusion-x4-upscaler-onnx',      subfolder='scheduler'	)
          UpperCamelCase   :  List[Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(
              'ssube/stable-diffusion-x4-upscaler-onnx',      scheduler=SCREAMING_SNAKE_CASE_,      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     'A fantasy landscape, trending on artstation'

          UpperCamelCase   :  List[Any]						     =     torch.manual_seed(0	)
          UpperCamelCase   :  Optional[Any]						     =     pipe(
              prompt=SCREAMING_SNAKE_CASE_,      image=SCREAMING_SNAKE_CASE_,      guidance_scale=7.5,      num_inference_steps=20,      generator=SCREAMING_SNAKE_CASE_,      output_type='np',      )
          UpperCamelCase   :  Dict						     =     output.images
          UpperCamelCase   :  Dict						     =     images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66]	)
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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








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


__UpperCAmelCase	       =	16
__UpperCAmelCase	       =	32
def 		UpperCamelCase     (       snake_case__	:   Accelerator	,					snake_case__	:   int = 16     )     ->					Dict:
   UpperCamelCase   :  str						     =     AutoTokenizer.from_pretrained('bert-base-cased'     )
   UpperCamelCase   :  str						     =     load_dataset('glue'	,					'mrpc'     )

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

   def collate_fn(snake_case__	:   Tuple     ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCamelCase   :  List[str]						     =     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":
         UpperCamelCase   :  str						     =     16
      elif accelerator.mixed_precision != "no":
         UpperCamelCase   :  Dict						     =     8
      else:
         UpperCamelCase   :  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.
   UpperCamelCase   :  Optional[int]						     =     DataLoader(
       tokenized_datasets['train']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )
   UpperCamelCase   :  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

    __UpperCAmelCase	       =	mocked_dataloaders  # noqa: F811
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   List[Any]     )     ->					List[str]:
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'	,					snake_case__     ) == "1":
      UpperCamelCase   :  List[str]						     =     2
   # New Code #
   UpperCamelCase   :  Optional[int]						     =     int(args.gradient_accumulation_steps     )
   UpperCamelCase   :  List[str]						     =     int(args.local_sgd_steps     )
   # Initialize accelerator
   UpperCamelCase   :  Optional[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
   UpperCamelCase   :  Union[str, Any]						     =     config['lr']
   UpperCamelCase   :  Optional[int]						     =     int(config['num_epochs']     )
   UpperCamelCase   :  List[Any]						     =     int(config['seed']     )
   UpperCamelCase   :  List[str]						     =     int(config['batch_size']     )

   UpperCamelCase   :  Optional[Any]						     =     evaluate.load('glue'	,					'mrpc'     )

   set_seed(snake_case__     )
   UpperCamelCase     ,					UpperCamelCase   :  int						     =     get_dataloaders(snake_case__	,					snake_case__     )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCamelCase   :  Tuple						     =     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).
   UpperCamelCase   :  Dict						     =     model.to(accelerator.device     )

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

   # Instantiate scheduler
   UpperCamelCase   :  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.
   UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  str						     =     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__     ):
               UpperCamelCase   :  int						     =     model(**snake_case__     )
               UpperCamelCase   :  Union[str, 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():
            UpperCamelCase   :  Dict						     =     model(**snake_case__     )
         UpperCamelCase   :  Any						     =     outputs.logits.argmax(dim=-1     )
         UpperCamelCase     ,					UpperCamelCase   :  Any						     =     accelerator.gather_for_metrics((predictions, batch['labels'])     )
         metric.add_batch(
             predictions=snake_case__	,					references=snake_case__	,					)

      UpperCamelCase   :  List[Any]						     =     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""	,					snake_case__     )
def 		UpperCamelCase     (       )     ->					Dict:
   UpperCamelCase   :  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.'     )
   UpperCamelCase   :  str						     =     parser.parse_args()
   UpperCamelCase   :  Tuple						     =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(snake_case__	,					snake_case__     )


if __name__ == "__main__":
    main()



