





import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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_					(			_lowerCamelCase ,      unittest.TestCase   ):
   lowerCAmelCase__        = """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Optional[Any]=0 )->					Tuple:
         __A          =				floats_tensor((1, 3, 128, 128)    ,   rng=random.Random(_UpperCamelCase ) )
         __A          =				np.random.RandomState(_UpperCamelCase )
         __A          =				{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''strength''': 0.7_5,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Optional[Any]:
         __A          =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider''' )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs()
         __A          =				pipe(**_UpperCamelCase ).images
         __A          =				image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 128, 128, 3)
         __A          =				np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7] )
         assert np.abs(image_slice - expected_slice ).max() < 1e-1




   def _lowerCAmelCase      (self	:Tuple )->					Optional[int]:
         __A          =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider''' )
         __A          =				PNDMScheduler.from_config(pipe.scheduler.config    ,   skip_prk_steps=_UpperCamelCase )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs()
         __A          =				pipe(**_UpperCamelCase ).images
         __A          =				image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __A          =				np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4] )

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




   def _lowerCAmelCase      (self	:Optional[Any] )->					Any:
         __A          =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider''' )
         __A          =				LMSDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         # warmup pass to apply optimizations
         __A          =				pipe(**self.get_dummy_inputs() )

         __A          =				self.get_dummy_inputs()
         __A          =				pipe(**_UpperCamelCase ).images
         __A          =				image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __A          =				np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3] )

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




   def _lowerCAmelCase      (self	:Union[str, Any] )->					List[str]:
         __A          =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider''' )
         __A          =				EulerDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs()
         __A          =				pipe(**_UpperCamelCase ).images
         __A          =				image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __A          =				np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )

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




   def _lowerCAmelCase      (self	:List[str] )->					int:
         __A          =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider''' )
         __A          =				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs()
         __A          =				pipe(**_UpperCamelCase ).images
         __A          =				image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __A          =				np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )

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






   def _lowerCAmelCase      (self	:Tuple )->					List[str]:
         __A          =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider''' )
         __A          =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs()
         __A          =				pipe(**_UpperCamelCase ).images
         __A          =				image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __A          =				np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2] )

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





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




   @property
   def _lowerCAmelCase      (self	:List[str] )->					Dict:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




   @property
   def _lowerCAmelCase      (self	:Optional[Any] )->					Dict:
         __A          =				ort.SessionOptions()
         __A          =				False
         return options




   def _lowerCAmelCase      (self	:int )->					Any:
         __A          =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg''' )
         __A          =				init_image.resize((768, 512) )
         # using the PNDM scheduler by default
         __A          =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''    ,   revision='''onnx'''    ,   safety_checker=_UpperCamelCase    ,   feature_extractor=_UpperCamelCase    ,   provider=self.gpu_provider    ,   sess_options=self.gpu_options    ,   )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				'''A fantasy landscape, trending on artstation'''

         __A          =				np.random.RandomState(0 )
         __A          =				pipe(
             prompt=_UpperCamelCase    ,   image=_UpperCamelCase    ,   strength=0.7_5    ,   guidance_scale=7.5    ,   num_inference_steps=10    ,   generator=_UpperCamelCase    ,   output_type='''np'''    ,   )
         __A          =				output.images
         __A          =				images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __A          =				np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9] )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2






   def _lowerCAmelCase      (self	:Optional[Any] )->					Tuple:
         __A          =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg''' )
         __A          =				init_image.resize((768, 512) )
         __A          =				LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,   subfolder='''scheduler'''    ,   revision='''onnx''' )
         __A          =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,   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 )

         __A          =				'''A fantasy landscape, trending on artstation'''

         __A          =				np.random.RandomState(0 )
         __A          =				pipe(
             prompt=_UpperCamelCase    ,   image=_UpperCamelCase    ,   strength=0.7_5    ,   guidance_scale=7.5    ,   num_inference_steps=20    ,   generator=_UpperCamelCase    ,   output_type='''np'''    ,   )
         __A          =				output.images
         __A          =				images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __A          =				np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1] )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2












from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





