



"""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      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      pass







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



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



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



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[str]        =   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      :     List[Any]        =   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						)

           _UpperCAmelCase      :     Union[str, Any]        =   "A red cat sitting on a park bench"

           _UpperCAmelCase      :     List[str]        =   np.random.RandomState(0						)
           _UpperCAmelCase      :     Tuple        =   pipe(
               prompt=_a					,     image=_a					,     mask_image=_a					,     guidance_scale=7.5					,     num_inference_steps=1_0					,     generator=_a					,     output_type="np"					,     )
           _UpperCAmelCase      :     Optional[Any]        =   output.images
           _UpperCAmelCase      :     Optional[int]        =   images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

           assert images.shape == (1, 5_1_2, 5_1_2, 3)
           _UpperCAmelCase      :     str        =   np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]						)

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



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     Dict        =   load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
           _UpperCAmelCase      :     Any        =   load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
           _UpperCAmelCase      :     Tuple        =   LMSDiscreteScheduler.from_pretrained(
               "runwayml/stable-diffusion-inpainting"					,     subfolder="scheduler"					,     revision="onnx"						)
           _UpperCAmelCase      :     Tuple        =   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						)

           _UpperCAmelCase      :     List[Any]        =   "A red cat sitting on a park bench"

           _UpperCAmelCase      :     List[Any]        =   np.random.RandomState(0						)
           _UpperCAmelCase      :     Optional[int]        =   pipe(
               prompt=_a					,     image=_a					,     mask_image=_a					,     guidance_scale=7.5					,     num_inference_steps=2_0					,     generator=_a					,     output_type="np"					,     )
           _UpperCAmelCase      :     int        =   output.images
           _UpperCAmelCase      :     Tuple        =   images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

           assert images.shape == (1, 5_1_2, 5_1_2, 3)
           _UpperCAmelCase      :     int        =   np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]						)

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










"""simple docstring"""




import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 						__snake_case						(				*SCREAMING_SNAKE_CASE__     :						List[Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[Union[Dict, Any]] = None						,  SCREAMING_SNAKE_CASE__     :						int=True						,  SCREAMING_SNAKE_CASE__     :						Tuple=2    )  ->       Optional[Any]:





     '''simple docstring'''

     from .. import __version__

     _UpperCAmelCase      :     Tuple        =   take_from
     _UpperCAmelCase      :     Optional[int]        =   ()
     if not isinstance(args[0]						,  SCREAMING_SNAKE_CASE__    ):
          _UpperCAmelCase      :     int        =   (args,)

     for attribute, version_name, message in args:
          if version.parse(version.parse(SCREAMING_SNAKE_CASE__    ).base_version    ) >= version.parse(SCREAMING_SNAKE_CASE__    ):
               raise ValueError(
                   f'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
                   f' version {__version__} is >= {version_name}'    )

          _UpperCAmelCase      :     Any        =   None
          if isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ) and attribute in deprecated_kwargs:
               values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE__    ),)
               _UpperCAmelCase      :     Tuple        =   f'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
          elif hasattr(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):
               values += (getattr(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ),)
               _UpperCAmelCase      :     str        =   f'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
          elif deprecated_kwargs is None:
               _UpperCAmelCase      :     Tuple        =   f'`{attribute}` is deprecated and will be removed in version {version_name}.'

          if warning is not None:
               _UpperCAmelCase      :     Optional[int]        =   warning + " " if standard_warn else ""
               warnings.warn(warning + message						,  SCREAMING_SNAKE_CASE__						,  stacklevel=SCREAMING_SNAKE_CASE__    )

     if isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ) and len(SCREAMING_SNAKE_CASE__    ) > 0:
          _UpperCAmelCase      :     List[Any]        =   inspect.getouterframes(inspect.currentframe()    )[1]
          _UpperCAmelCase      :     Optional[int]        =   call_frame.filename
          _UpperCAmelCase      :     Dict        =   call_frame.lineno
          _UpperCAmelCase      :     List[Any]        =   call_frame.function
          _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   next(iter(deprecated_kwargs.items()    )    )
          raise TypeError(f'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'    )

     if len(SCREAMING_SNAKE_CASE__    ) == 0:
          return
     elif len(SCREAMING_SNAKE_CASE__    ) == 1:
          return values[0]
     return values
