


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 A__    ( a__  ,		unittest.TestCase ):



 """simple docstring"""







 pass





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



 """simple docstring"""







 @property
 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




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

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    ort.SessionOptions()
      _lowerCAmelCase   :					str        =    False
      return options

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




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

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

      _lowerCAmelCase   :					Tuple        =    np.random.RandomState(0       )
      _lowerCAmelCase   :					Dict        =    pipe(
          prompt=lowercase__     ,						image=lowercase__     ,						mask_image=lowercase__     ,						guidance_scale=7.5     ,						num_inference_steps=1_0     ,						generator=lowercase__     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					Union[str, Any]        =    output.images
      _lowerCAmelCase   :					List[str]        =    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)
      _lowerCAmelCase   :					List[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    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




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

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

      _lowerCAmelCase   :					int        =    np.random.RandomState(0       )
      _lowerCAmelCase   :					int        =    pipe(
          prompt=lowercase__     ,						image=lowercase__     ,						mask_image=lowercase__     ,						guidance_scale=7.5     ,						num_inference_steps=2_0     ,						generator=lowercase__     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					List[Any]        =    output.images
      _lowerCAmelCase   :					Dict        =    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)
      _lowerCAmelCase   :					Optional[int]        =    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









import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						*A_ :	Optional[int]     ,						**A_ :	int       ):






      '''simple docstring'''




      warnings.warn(
          "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use ChineseCLIPImageProcessor instead."     ,						A_     ,						)
      super().__init__(*A_     ,						**A_       )






