






"""simple docstring"""


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 		snake_case						(						unittest.TestCase ):





    @property
    def        lowerCamelCase__   (							self			:      Optional[int]    ):


      '''simple docstring'''




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





    @property
    def        lowerCamelCase__   (							self			:      str    ):


      '''simple docstring'''




      a  :    Optional[Any]       =     ort.SessionOptions()
      a  :    List[str]       =     False
      return options





    def        lowerCamelCase__   (							self			:      Dict    ):


      '''simple docstring'''




      a  :    Optional[int]       =     load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/in_paint/overture-creations-5sI6fQgYIuo.png'    )
      a  :    Optional[Any]       =     load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'    )
      a  :    int       =     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
      a  :    List[str]       =     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
          'CompVis/stable-diffusion-v1-4'       ,	revision='onnx'       ,	safety_checker=A       ,	feature_extractor=A       ,	provider=self.gpu_provider       ,	sess_options=self.gpu_options       ,	)
      pipe.set_progress_bar_config(disable=A    )

      a  :    str       =     'A red cat sitting on a park bench'

      a  :    int       =     np.random.RandomState(0    )
      a  :    Any       =     pipe(
          prompt=A       ,	image=A       ,	mask_image=A       ,	strength=0.75       ,	guidance_scale=7.5       ,	num_inference_steps=1_5       ,	generator=A       ,	output_type='np'       ,	)

      a  :    List[Any]       =     output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)
      assert np.abs(expected_image - image    ).max() < 1E-2












"""simple docstring"""


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase       :					Optional[Any]									=		{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					Tuple									=		[
       'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'InformerForPrediction',
       'InformerModel',
       'InformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

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




