


import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    A      = False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor


class     lowercase__  (					unittest.TestCase		):






  def __init__(					self      :					Optional[Any]		,						_lowercase      :					List[str]		,						_lowercase      :					int=7		,						_lowercase      :					Optional[int]=3		,						_lowercase      :					Union[str, Any]=18		,						_lowercase      :					Optional[int]=30		,						_lowercase      :					Optional[Any]=4_00		,						_lowercase      :					Optional[Any]=None		,						_lowercase      :					List[str]=True		,						_lowercase      :					int=True		,						_lowercase      :					Any=None		,						):




         """simple docstring"""







         UpperCAmelCase__										=     size if size is not None else {"height": 20, "width": 20}
         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     num_channels
         UpperCAmelCase__										=     image_size
         UpperCAmelCase__										=     min_resolution
         UpperCAmelCase__										=     max_resolution
         UpperCAmelCase__										=     size
         UpperCAmelCase__										=     do_normalize
         UpperCAmelCase__										=     do_convert_rgb
         UpperCAmelCase__										=     [5_12, 10_24, 20_48, 40_96]
         UpperCAmelCase__										=     patch_size if patch_size is not None else {"height": 16, "width": 16}






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






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




         """simple docstring"""







         UpperCAmelCase__										=     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
         UpperCAmelCase__										=     Image.open(requests.get(_lowercase		,						stream=_lowercase ).raw ).convert("RGB" )
         return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'					,							)
@require_torch
@require_vision
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  PixaStructImageProcessor if is_vision_available() else None






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     PixaStructImageProcessingTester(self )






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




         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(_lowercase		,						"do_normalize" ) )
         self.assertTrue(hasattr(_lowercase		,						"do_convert_rgb" ) )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processor_tester.prepare_dummy_image()

         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         UpperCAmelCase__										=     20_48

         UpperCAmelCase__										=     image_processor(_lowercase		,						return_tensors="pt"		,						max_patches=_lowercase )
         self.assertTrue(torch.allclose(inputs.flattened_patches.mean()		,						torch.tensor(0.0_6_0_6 )		,						atol=1E-3		,						rtol=1E-3 ) )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         UpperCAmelCase__										=     prepare_image_inputs(self.image_processor_tester		,						equal_resolution=_lowercase )
         for image in image_inputs:
                self.assertIsInstance(_lowercase		,						Image.Image )

         # Test not batched input
         UpperCAmelCase__										=     (
             (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
             * self.image_processor_tester.num_channels
         ) + 2

         for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                UpperCAmelCase__										=     image_processor(
                    image_inputs[0]		,						return_tensors="pt"		,						max_patches=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(1, max_patch, expected_hidden_dim)		,						)

                # Test batched
                UpperCAmelCase__										=     image_processor(
                    _lowercase		,						return_tensors="pt"		,						max_patches=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,						)






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         UpperCAmelCase__										=     prepare_image_inputs(self.image_processor_tester		,						equal_resolution=_lowercase )
         for image in image_inputs:
                self.assertIsInstance(_lowercase		,						Image.Image )

         # Test not batched input
         UpperCAmelCase__										=     (
             (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
             * self.image_processor_tester.num_channels
         ) + 2

         UpperCAmelCase__										=     True

         for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                with self.assertRaises(_lowercase ):
                       UpperCAmelCase__										=     image_processor(
                           image_inputs[0]		,						return_tensors="pt"		,						max_patches=_lowercase ).flattened_patches

                UpperCAmelCase__										=     "Hello"

                UpperCAmelCase__										=     image_processor(
                    image_inputs[0]		,						return_tensors="pt"		,						max_patches=_lowercase		,						header_text=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(1, max_patch, expected_hidden_dim)		,						)

                # Test batched
                UpperCAmelCase__										=     image_processor(
                    _lowercase		,						return_tensors="pt"		,						max_patches=_lowercase		,						header_text=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,						)






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




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         UpperCAmelCase__										=     prepare_image_inputs(self.image_processor_tester		,						equal_resolution=_lowercase		,						numpify=_lowercase )
         for image in image_inputs:
                self.assertIsInstance(_lowercase		,						np.ndarray )

         UpperCAmelCase__										=     (
             (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
             * self.image_processor_tester.num_channels
         ) + 2

         for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                UpperCAmelCase__										=     image_processor(
                    image_inputs[0]		,						return_tensors="pt"		,						max_patches=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(1, max_patch, expected_hidden_dim)		,						)

                # Test batched
                UpperCAmelCase__										=     image_processor(
                    _lowercase		,						return_tensors="pt"		,						max_patches=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,						)






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         UpperCAmelCase__										=     prepare_image_inputs(self.image_processor_tester		,						equal_resolution=_lowercase		,						torchify=_lowercase )
         for image in image_inputs:
                self.assertIsInstance(_lowercase		,						torch.Tensor )

         # Test not batched input
         UpperCAmelCase__										=     (
             (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
             * self.image_processor_tester.num_channels
         ) + 2

         for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                UpperCAmelCase__										=     image_processor(
                    image_inputs[0]		,						return_tensors="pt"		,						max_patches=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(1, max_patch, expected_hidden_dim)		,						)

                # Test batched
                UpperCAmelCase__										=     image_processor(
                    _lowercase		,						return_tensors="pt"		,						max_patches=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,						)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'					,							)
@require_torch
@require_vision
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  PixaStructImageProcessor if is_vision_available() else None






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     PixaStructImageProcessingTester(self		,						num_channels=4 )
         UpperCAmelCase__										=     3






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




         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(_lowercase		,						"do_normalize" ) )
         self.assertTrue(hasattr(_lowercase		,						"do_convert_rgb" ) )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         UpperCAmelCase__										=     prepare_image_inputs(self.image_processor_tester		,						equal_resolution=_lowercase )
         for image in image_inputs:
                self.assertIsInstance(_lowercase		,						Image.Image )

         # Test not batched input
         UpperCAmelCase__										=     (
             (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
             * (self.image_processor_tester.num_channels - 1)
         ) + 2

         for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                UpperCAmelCase__										=     image_processor(
                    image_inputs[0]		,						return_tensors="pt"		,						max_patches=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(1, max_patch, expected_hidden_dim)		,						)

                # Test batched
                UpperCAmelCase__										=     image_processor(
                    _lowercase		,						return_tensors="pt"		,						max_patches=_lowercase ).flattened_patches
                self.assertEqual(
                    encoded_images.shape		,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,						)









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     lowercase__  (					unittest.TestCase		):






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




         """simple docstring"""







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






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




         """simple docstring"""







         UpperCAmelCase__										=     ort.SessionOptions()
         UpperCAmelCase__										=     False
         return options






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png" )
         UpperCAmelCase__										=     load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
         UpperCAmelCase__										=     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
         UpperCAmelCase__										=     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,						revision="onnx"		,						safety_checker=_lowercase		,						feature_extractor=_lowercase		,						provider=self.gpu_provider		,						sess_options=self.gpu_options		,						)
         pipe.set_progress_bar_config(disable=_lowercase )

         UpperCAmelCase__										=     "A red cat sitting on a park bench"

         UpperCAmelCase__										=     np.random.RandomState(0 )
         UpperCAmelCase__										=     pipe(
             prompt=_lowercase		,						image=_lowercase		,						mask_image=_lowercase		,						strength=0.7_5		,						guidance_scale=7.5		,						num_inference_steps=15		,						generator=_lowercase		,						output_type="np"		,						)

         UpperCAmelCase__										=     output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image ).max() < 1E-2





