

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 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: Optional[int]=0    )    ->      Dict:
   UpperCAmelCase					      =  floats_tensor((1, 3, 1_2_8, 1_2_8)			,						rng=random.Random(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  np.random.RandomState(lowerCAmelCase__    )
   UpperCAmelCase					      =  {
       "prompt": "A painting of a squirrel eating a burger",
       "image": image,
       "generator": generator,
       "num_inference_steps": 3,
       "strength": 0.75,
       "guidance_scale": 7.5,
       "output_type": "numpy",
   }
   return inputs


 def _UpperCamelCase	(						self		: List[str]    )    ->      int:
   UpperCAmelCase					      =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,						provider="CPUExecutionProvider"    )
   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

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

   assert image.shape == (1, 1_2_8, 1_2_8, 3)
   UpperCAmelCase					      =  np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]    )
   assert np.abs(image_slice - expected_slice    ).max() < 1e-1


 def _UpperCamelCase	(						self		: str    )    ->      List[str]:
   UpperCAmelCase					      =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,						provider="CPUExecutionProvider"    )
   UpperCAmelCase					      =  PNDMScheduler.from_config(pipe.scheduler.config			,						skip_prk_steps=lowerCAmelCase__    )
   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

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

   assert image.shape == (1, 1_2_8, 1_2_8, 3)
   UpperCAmelCase					      =  np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]    )

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


 def _UpperCamelCase	(						self		: Tuple    )    ->      Dict:
   UpperCAmelCase					      =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,						provider="CPUExecutionProvider"    )
   UpperCAmelCase					      =  LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

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

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

   assert image.shape == (1, 1_2_8, 1_2_8, 3)
   UpperCAmelCase					      =  np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]    )

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


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

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

   assert image.shape == (1, 1_2_8, 1_2_8, 3)
   UpperCAmelCase					      =  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]    )

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


 def _UpperCamelCase	(						self		: Dict    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,						provider="CPUExecutionProvider"    )
   UpperCAmelCase					      =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

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

   assert image.shape == (1, 1_2_8, 1_2_8, 3)
   UpperCAmelCase					      =  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]    )

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


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,						provider="CPUExecutionProvider"    )
   UpperCAmelCase					      =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

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

   assert image.shape == (1, 1_2_8, 1_2_8, 3)
   UpperCAmelCase					      =  np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]    )

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






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


 @property
 def _UpperCamelCase	(						self		: Any    )    ->      str:
   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )


 @property
 def _UpperCamelCase	(						self		: Dict    )    ->      Tuple:
   UpperCAmelCase					      =  ort.SessionOptions()
   UpperCAmelCase					      =  False
   return options


 def _UpperCamelCase	(						self		: Dict    )    ->      Any:
   UpperCAmelCase					      =  load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/img2img/sketch-mountains-input.jpg"    )
   UpperCAmelCase					      =  init_image.resize((7_6_8, 5_1_2)    )
   # using the PNDM scheduler by default
   UpperCAmelCase					      =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
       "CompVis/stable-diffusion-v1-4"			,						revision="onnx"			,						safety_checker=lowerCAmelCase__			,						feature_extractor=lowerCAmelCase__			,						provider=self.gpu_provider			,						sess_options=self.gpu_options			,						)
   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

   UpperCAmelCase					      =  "A fantasy landscape, trending on artstation"

   UpperCAmelCase					      =  np.random.RandomState(0    )
   UpperCAmelCase					      =  pipe(
       prompt=lowerCAmelCase__			,						image=lowerCAmelCase__			,						strength=0.75			,						guidance_scale=7.5			,						num_inference_steps=1_0			,						generator=lowerCAmelCase__			,						output_type="np"			,						)
   UpperCAmelCase					      =  output.images
   UpperCAmelCase					      =  images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

   assert images.shape == (1, 5_1_2, 7_6_8, 3)
   UpperCAmelCase					      =  np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]    )
   # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      List[Any]:
   UpperCAmelCase					      =  load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/img2img/sketch-mountains-input.jpg"    )
   UpperCAmelCase					      =  init_image.resize((7_6_8, 5_1_2)    )
   UpperCAmelCase					      =  LMSDiscreteScheduler.from_pretrained(
       "runwayml/stable-diffusion-v1-5"			,						subfolder="scheduler"			,						revision="onnx"    )
   UpperCAmelCase					      =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
       "runwayml/stable-diffusion-v1-5"			,						revision="onnx"			,						scheduler=lowerCAmelCase__			,						safety_checker=lowerCAmelCase__			,						feature_extractor=lowerCAmelCase__			,						provider=self.gpu_provider			,						sess_options=self.gpu_options			,						)
   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

   UpperCAmelCase					      =  "A fantasy landscape, trending on artstation"

   UpperCAmelCase					      =  np.random.RandomState(0    )
   UpperCAmelCase					      =  pipe(
       prompt=lowerCAmelCase__			,						image=lowerCAmelCase__			,						strength=0.75			,						guidance_scale=7.5			,						num_inference_steps=2_0			,						generator=lowerCAmelCase__			,						output_type="np"			,						)
   UpperCAmelCase					      =  output.images
   UpperCAmelCase					      =  images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

   assert images.shape == (1, 5_1_2, 7_6_8, 3)
   UpperCAmelCase					      =  np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]    )
   # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






import copy
import os

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






class 						__magic_name__    :


 def __init__(						self		: Optional[int]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  ""
   UpperCAmelCase					      =  ""
   UpperCAmelCase					      =  []
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  2_5_6
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: Optional[Any]    )    ->      List[str]:
   UpperCAmelCase					      =  cva.imread(lowerCAmelCase__			,						0    )
   UpperCAmelCase					      =  copy.deepcopy(self.img    )
   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  plt.hist(self.img.ravel()			,						2_5_6			,						[0, 2_5_6]			,						label="x"    )
   UpperCAmelCase					      =  np.sum(lowerCAmelCase__    )
   for i in range(len(lowerCAmelCase__    )    ):
     UpperCAmelCase					      =  x[i] / self.k
     self.sk += prk
     UpperCAmelCase					      =  (self.L - 1) * self.sk
     if self.rem != 0:
       UpperCAmelCase					      =  int(last % last    )
     UpperCAmelCase					      =  int(last + 1 if self.rem >= 0.5 else last    )
     self.last_list.append(lowerCAmelCase__    )
     UpperCAmelCase					      =  int(np.ma.count(self.img    ) / self.img[1].size    )
     UpperCAmelCase					      =  self.img[1].size
   for i in range(self.number_of_cols    ):
     for j in range(self.number_of_rows    ):
       UpperCAmelCase					      =  self.img[j][i]
       if num != self.last_list[num]:
         UpperCAmelCase					      =  self.last_list[num]
   cva.imwrite("output_data/output.jpg"			,						self.img    )


 def _UpperCamelCase	(						self		: str    )    ->      int:
   plt.hist(self.img.ravel()			,						2_5_6			,						[0, 2_5_6]    )


 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[Any]:
   cva.imshow("Output-Image"			,						self.img    )
   cva.imshow("Input-Image"			,						self.original_image    )
   cva.waitKey(5_0_0_0    )
   cva.destroyAllWindows()


if __name__ == "__main__":
      lowerCAmelCase__       = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
      lowerCAmelCase__       = ConstantStretch()
      stretcher.stretch(file_path)
      stretcher.plot_histogram()
      stretcher.show_image()



