import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
      import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 		_lowerCamelCase		( unittest.TestCase     ):
    @property
    def     UpperCamelCase_					(	self )							->       List[str]:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )
    @property
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Dict=   ort.SessionOptions()
     SCREAMING_SNAKE_CASE__: List[str]=   False
     return options



    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: Dict=   load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
     SCREAMING_SNAKE_CASE__: int=   load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
     SCREAMING_SNAKE_CASE__: Tuple=   load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy''' )

     # using the PNDM scheduler by default
     SCREAMING_SNAKE_CASE__: Tuple=   OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         '''CompVis/stable-diffusion-v1-4'''			,			revision='''onnx'''			,			safety_checker=lowerCAmelCase			,			feature_extractor=lowerCAmelCase			,			provider=self.gpu_provider			,			sess_options=self.gpu_options			,			)
     pipe.set_progress_bar_config(disable=lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Dict=   '''A red cat sitting on a park bench'''

     SCREAMING_SNAKE_CASE__: Optional[Any]=   np.random.RandomState(0 )
     SCREAMING_SNAKE_CASE__: Any=   pipe(
         prompt=lowerCAmelCase			,			image=lowerCAmelCase			,			mask_image=lowerCAmelCase			,			strength=0.75			,			guidance_scale=7.5			,			num_inference_steps=15			,			generator=lowerCAmelCase			,			output_type='''np'''			,			)

     SCREAMING_SNAKE_CASE__: Any=   output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image ).max() < 1e-2







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase   :			Optional[int]          = {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase   :			int          = [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase   :			List[str]          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

