





# 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    __snake_case	:





       '''simple docstring'''







       def __init__(  self			:   List[Any]							,    A			:   List[str]							,    A			:   Any							,    A			:   bool = True							,    A			:   bool = False		):
         __snake_case:						Dict			  =						scheduler
         __snake_case:						Tuple			  =						optimizers if isinstance(A							,    (list, tuple)		) else [optimizers]
         __snake_case:						str			  =						split_batches
         __snake_case:						str			  =						step_with_optimizer
         __snake_case:						Dict			  =						GradientState()




       def 							UpperCAmelCase__			(  self			:   List[Any]							,    *A			:   Tuple							,    **A			:   int		):
         if not self.step_with_optimizer:
           # No link between scheduler and optimizer -> just step
           self.scheduler.step(*A							,    **A		)
           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(*A							,    **A		)
         else:
           # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
           # num_processes steps per training step
           __snake_case:						int			  =						AcceleratorState().num_processes
           for _ in range(A		):
             # 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(*A							,    **A		)
             else:
               self.scheduler.step(*A							,    **A		)




       def 							UpperCAmelCase__			(  self			:   Tuple		):
         return self.scheduler.get_last_lr()




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         return self.scheduler.state_dict()




       def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   Union[str, Any]		):
         self.scheduler.load_state_dict(A		)




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         return self.scheduler.get_lr()






       def 							UpperCAmelCase__			(  self			:   List[str]							,    *A			:   Any							,    **A			:   Tuple		):
         return self.scheduler.print_lr(*A							,    **A		)













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    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







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





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						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 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						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






