




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  _lowerCAmelCase     (  unittest.TestCase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=7      ,				__UpperCAmelCase=3      ,				__UpperCAmelCase=18      ,				__UpperCAmelCase=30      ,				__UpperCAmelCase=400      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=32      ,				__UpperCAmelCase=True      ,				):
        lowerCAmelCase__				:  Optional[int]		       =     parent
        lowerCAmelCase__				:  str		       =     batch_size
        lowerCAmelCase__				:  Dict		       =     num_channels
        lowerCAmelCase__				:  Dict		       =     image_size
        lowerCAmelCase__				:  Optional[int]		       =     min_resolution
        lowerCAmelCase__				:  str		       =     max_resolution
        lowerCAmelCase__				:  Optional[int]		       =     do_resize
        lowerCAmelCase__				:  Union[str, Any]		       =     size_divisor
        lowerCAmelCase__				:  List[str]		       =     do_rescale







      def 	__magic_name__(   self		):
        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }






@require_torch
@require_vision
class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      A__		     =		GLPNImageProcessor if is_vision_available() else None

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     GLPNImageProcessingTester(self		)

      @property
      def 	__magic_name__(   self		):
        return self.image_processor_tester.prepare_image_processor_dict()

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''do_resize'''		)		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''size_divisor'''		)		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''resample'''		)		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''do_rescale'''		)		)

      def 	__magic_name__(   self		):
        pass

      def 	__magic_name__(   self		):
        # Initialize image_processing
        lowerCAmelCase__				:  str		       =     self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        lowerCAmelCase__				:  Dict		       =     prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=__UpperCAmelCase		)
        for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase      ,				Image.Image		)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        lowerCAmelCase__				:  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 	__magic_name__(   self		):
        # Initialize image_processing
        lowerCAmelCase__				:  List[Any]		       =     self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        lowerCAmelCase__				:  int		       =     prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=__UpperCAmelCase      ,				numpify=__UpperCAmelCase		)
        for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase      ,				np.ndarray		)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        lowerCAmelCase__				:  Optional[Any]		       =     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 	__magic_name__(   self		):
        # Initialize image_processing
        lowerCAmelCase__				:  Tuple		       =     self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        lowerCAmelCase__				:  List[Any]		       =     prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=__UpperCAmelCase      ,				torchify=__UpperCAmelCase		)
        for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase      ,				torch.Tensor		)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        lowerCAmelCase__				:  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		)







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  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      # FIXME: add fast tests
      pass






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

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

      @property
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     ort.SessionOptions()
        lowerCAmelCase__				:  List[Any]		       =     False
        return options

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     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[Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  List[Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  int		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=10      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Any		       =     output.images
        lowerCAmelCase__				:  Any		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  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		):
        lowerCAmelCase__				:  List[str]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  str		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     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=__UpperCAmelCase      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

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

        lowerCAmelCase__				:  Union[str, Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  Optional[Any]		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=20      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Tuple		       =     output.images
        lowerCAmelCase__				:  Optional[Any]		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 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

