


# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = True ,						__lowerCamelCase = False)			->				Dict:
            _A      :       Dict				    = scheduler
            _A      :       Union[str, Any]				    = optimizers if isinstance(__lowerCamelCase ,						(list, tuple)) else [optimizers]
            _A      :       int				    = split_batches
            _A      :       int				    = step_with_optimizer
            _A      :       Any				    = GradientState()


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            if not self.step_with_optimizer:
                   # No link between scheduler and optimizer -> just step
                   self.scheduler.step(*__lowerCamelCase ,						**__lowerCamelCase)
                   return

            # Otherwise, first make sure the optimizer was stepped.
            if not self.gradient_state.sync_gradients:
                   if self.gradient_state.adjust_scheduler:
                          self.scheduler._step_count += 1
                   return

            for opt in self.optimizers:
                   if opt.step_was_skipped:
                          return
            if self.split_batches:
                   # Split batches -> the training dataloader batch size is not changed so one step per training step
                   self.scheduler.step(*__lowerCamelCase ,						**__lowerCamelCase)
            else:
                   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
                   # num_processes steps per training step
                   _A      :       List[Any]				    = AcceleratorState().num_processes
                   for _ in range(__lowerCamelCase):
                          # Special case when using OneCycle and `drop_last` was not used
                          if hasattr(self.scheduler ,						"total_steps"):
                                 if self.scheduler._step_count <= self.scheduler.total_steps:
                                        self.scheduler.step(*__lowerCamelCase ,						**__lowerCamelCase)
                          else:
                                 self.scheduler.step(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            return self.scheduler.get_last_lr()


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            return self.scheduler.state_dict()


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, Any]:
            self.scheduler.load_state_dict(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Tuple:
            return self.scheduler.get_lr()


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            return self.scheduler.print_lr(*__lowerCamelCase ,						**__lowerCamelCase)


import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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 			__A( a  ,   unittest.TestCase			):
				# FIXME: add fast tests
				pass







@nightly
@require_onnxruntime
@require_torch_gpu
class 			__A( unittest.TestCase			):




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    ort.SessionOptions()
								__a										=    False
								return options




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

								__a										=    '''A red cat sitting on a park bench'''

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=10		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    LMSDiscreteScheduler.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     subfolder='''scheduler'''		,     revision='''onnx'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     scheduler=_snake_case		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

								__a										=    '''A red cat sitting on a park bench'''

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=20		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3