






'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor


class 							_UpperCAmelCase    (		unittest.TestCase      ):




    def __init__(      self	:							int	,  a	:							Tuple	,  a	:							Union[str, Any]=7	,  a	:							Optional[Any]=3	,  a	:							Tuple=1_8	,  a	:							str=3_0	,  a	:							Dict=4_0_0	,  a	:							List[Any]=True	,  a	:							Dict=3_2	,  a	:							List[str]=True	,  ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  parent
       lowercase_						:     Tuple							     =  batch_size
       lowercase_						:     str							     =  num_channels
       lowercase_						:     Dict							     =  image_size
       lowercase_						:     Any							     =  min_resolution
       lowercase_						:     List[str]							     =  max_resolution
       lowercase_						:     Any							     =  do_resize
       lowercase_						:     Optional[Any]							     =  size_divisor
       lowercase_						:     List[Any]							     =  do_rescale





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }


@require_torch
@require_vision
class 							_UpperCAmelCase    (		snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: int									=     GLPNImageProcessor if is_vision_available() else None




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  GLPNImageProcessingTester(self      )




    @property
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(a	,  "do_resize"      )      )
       self.assertTrue(hasattr(a	,  "size_divisor"      )      )
       self.assertTrue(hasattr(a	,  "resample"      )      )
       self.assertTrue(hasattr(a	,  "do_rescale"      )      )




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       pass




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       lowercase_						:     Union[str, Any]							     =  prepare_image_inputs(self.image_processor_tester	,  equal_resolution=a      )
       for image in image_inputs:
          self.assertIsInstance(a	,  Image.Image      )

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       lowercase_						:     str							     =  image_processing(image_inputs[0]	,  return_tensors="pt"      ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       lowercase_						:     Optional[int]							     =  prepare_image_inputs(self.image_processor_tester	,  equal_resolution=a	,  numpify=a      )
       for image in image_inputs:
          self.assertIsInstance(a	,  np.ndarray      )

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       lowercase_						:     Optional[int]							     =  image_processing(image_inputs[0]	,  return_tensors="pt"      ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )





    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     str							     =  self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       lowercase_						:     List[str]							     =  prepare_image_inputs(self.image_processor_tester	,  equal_resolution=a	,  torchify=a      )
       for image in image_inputs:
          self.assertIsInstance(a	,  torch.Tensor      )

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       lowercase_						:     Optional[int]							     =  image_processing(image_inputs[0]	,  return_tensors="pt"      ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )












'''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 							_UpperCAmelCase    (		unittest.TestCase      ):




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






       '''simple docstring'''






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




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  ort.SessionOptions()
       lowercase_						:     Optional[int]							     =  False
       return options





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
       lowercase_						:     Tuple							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
       lowercase_						:     List[str]							     =  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
       lowercase_						:     List[Any]							     =  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      )

       lowercase_						:     List[str]							     =  "A red cat sitting on a park bench"

       lowercase_						:     int							     =  np.random.RandomState(0      )
       lowercase_						:     int							     =  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"	,  )

       lowercase_						:     Tuple							     =  output.images[0]

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




