


'''simple docstring'''



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					(     a__       ,   unittest.TestCase	):
					# FIXME: add fast tests
					pass


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




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




					@property
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     ort.SessionOptions()
								UpperCAmelCase__  :					str        =     False
								return options




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

								UpperCAmelCase__  :					Any        =     """A red cat sitting on a park bench"""

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

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

								UpperCAmelCase__  :					Optional[Any]        =     """A red cat sitting on a park bench"""

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

								assert images.shape == (1, 512, 512, 3)
								UpperCAmelCase__  :					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



'''simple docstring'''



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


__A	='scheduler_config.json'


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Union[str, Any]   =					1
					lowerCAmelCase      :Union[str, Any]   =					2
					lowerCAmelCase      :Optional[int]   =					3
					lowerCAmelCase      :Optional[int]   =					4
					lowerCAmelCase      :Any   =					5
					lowerCAmelCase      :Tuple   =					6
					lowerCAmelCase      :List[Any]   =					7
					lowerCAmelCase      :str   =					8
					lowerCAmelCase      :List[Any]   =					9
					lowerCAmelCase      :List[str]   =					10
					lowerCAmelCase      :Union[str, Any]   =					11
					lowerCAmelCase      :Optional[int]   =					12
					lowerCAmelCase      :str   =					13
					lowerCAmelCase      :Dict   =					14


@dataclass
class 		_snake_case					(     a__	):
					lowerCAmelCase      :torch.FloatTensor


class 		_snake_case					:
					lowerCAmelCase      :str   =					SCHEDULER_CONFIG_NAME
					lowerCAmelCase      :Union[str, Any]   =					[]
					lowerCAmelCase      :List[str]   =					True




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase=False							,							**_lowerCamelCase							,							):
								UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     cls.load_config(
								    pretrained_model_name_or_path=_lowerCamelCase							,							subfolder=_lowerCamelCase							,							return_unused_kwargs=_lowerCamelCase							,							return_commit_hash=_lowerCamelCase							,							**_lowerCamelCase							,							)
								return cls.from_config(_lowerCamelCase							,							return_unused_kwargs=_lowerCamelCase							,							**_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							**_lowerCamelCase):
								self.save_config(save_directory=_lowerCamelCase							,							push_to_hub=_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 				snake_case__						(      self):
								return self._get_compatibles()




					@classmethod
					def 				snake_case__						(      cls):
								UpperCAmelCase__  :					List[Any]        =     list(set([cls.__name__] + cls._compatibles))
								UpperCAmelCase__  :					int        =     importlib.import_module(__name__.split(""".""")[0])
								UpperCAmelCase__  :					Dict        =     [
								    getattr(_lowerCamelCase							,							_lowerCamelCase) for c in compatible_classes_str if hasattr(_lowerCamelCase							,							_lowerCamelCase)
								]
								return compatible_classes