






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     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									='''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''





       def       _a   ( self  ,      _lowerCamelCase=0							):
         UpperCamelCase_: Any   				=    floats_tensor((1, 3, 1_2_8, 1_2_8)  ,      rng=random.Random(_lowerCamelCase							)							)
         UpperCamelCase_: List[Any]   				=    np.random.RandomState(_lowerCamelCase							)
         UpperCamelCase_: Union[str, Any]   				=    {
             '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       _a   ( self							):
         UpperCamelCase_: List[Any]   				=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider='CPUExecutionProvider'							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_: int   				=    self.get_dummy_inputs()
         UpperCamelCase_: Dict   				=    pipe(**_lowerCamelCase							).images
         UpperCamelCase_: Union[str, Any]   				=    image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         UpperCamelCase_: List[Any]   				=    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       _a   ( self							):
         UpperCamelCase_: Dict   				=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider='CPUExecutionProvider'							)
         UpperCamelCase_: Dict   				=    PNDMScheduler.from_config(pipe.scheduler.config  ,      skip_prk_steps=_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_: Union[str, Any]   				=    self.get_dummy_inputs()
         UpperCamelCase_: Dict   				=    pipe(**_lowerCamelCase							).images
         UpperCamelCase_: List[str]   				=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         UpperCamelCase_: Optional[Any]   				=    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       _a   ( self							):
         UpperCamelCase_: List[str]   				=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider='CPUExecutionProvider'							)
         UpperCamelCase_: str   				=    LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         # warmup pass to apply optimizations
         UpperCamelCase_: List[str]   				=    pipe(**self.get_dummy_inputs()							)

         UpperCamelCase_: Optional[Any]   				=    self.get_dummy_inputs()
         UpperCamelCase_: int   				=    pipe(**_lowerCamelCase							).images
         UpperCamelCase_: str   				=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         UpperCamelCase_: Dict   				=    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       _a   ( self							):
         UpperCamelCase_: int   				=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider='CPUExecutionProvider'							)
         UpperCamelCase_: int   				=    EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    self.get_dummy_inputs()
         UpperCamelCase_: Dict   				=    pipe(**_lowerCamelCase							).images
         UpperCamelCase_: str   				=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         UpperCamelCase_: Optional[int]   				=    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       _a   ( self							):
         UpperCamelCase_: str   				=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider='CPUExecutionProvider'							)
         UpperCamelCase_: List[Any]   				=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    self.get_dummy_inputs()
         UpperCamelCase_: List[str]   				=    pipe(**_lowerCamelCase							).images
         UpperCamelCase_: int   				=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         UpperCamelCase_: List[str]   				=    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       _a   ( self							):
         UpperCamelCase_: str   				=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider='CPUExecutionProvider'							)
         UpperCamelCase_: Any   				=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    self.get_dummy_inputs()
         UpperCamelCase_: int   				=    pipe(**_lowerCamelCase							).images
         UpperCamelCase_: int   				=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         UpperCamelCase_: Dict   				=    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     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







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





       @property
       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    ort.SessionOptions()
         UpperCamelCase_: Optional[int]   				=    False
         return options





       def       _a   ( self							):
         UpperCamelCase_: List[Any]   				=    load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg'							)
         UpperCamelCase_: str   				=    init_image.resize((7_6_8, 5_1_2)							)
         # using the PNDM scheduler by default
         UpperCamelCase_: Any   				=    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_: List[str]   				=    'A fantasy landscape, trending on artstation'

         UpperCamelCase_: Tuple   				=    np.random.RandomState(0							)
         UpperCamelCase_: List[str]   				=    pipe(
             prompt=_lowerCamelCase  ,      image=_lowerCamelCase  ,      strength=0.7_5  ,      guidance_scale=7.5  ,      num_inference_steps=1_0  ,      generator=_lowerCamelCase  ,      output_type='np'  ,      )
         UpperCamelCase_: Dict   				=    output.images
         UpperCamelCase_: int   				=    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_: Dict   				=    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       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg'							)
         UpperCamelCase_: Dict   				=    init_image.resize((7_6_8, 5_1_2)							)
         UpperCamelCase_: int   				=    LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-v1-5'  ,      subfolder='scheduler'  ,      revision='onnx'							)
         UpperCamelCase_: Any   				=    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_: List[str]   				=    'A fantasy landscape, trending on artstation'

         UpperCamelCase_: int   				=    np.random.RandomState(0							)
         UpperCamelCase_: Dict   				=    pipe(
             prompt=_lowerCamelCase  ,      image=_lowerCamelCase  ,      strength=0.7_5  ,      guidance_scale=7.5  ,      num_inference_steps=2_0  ,      generator=_lowerCamelCase  ,      output_type='np'  ,      )
         UpperCamelCase_: Optional[int]   				=    output.images
         UpperCamelCase_: Optional[int]   				=    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_: str   				=    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







import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def   snake_case (UpperCAmelCase__  )	->    tuple:
  return (data["data"], data["target"])

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.ndarray:
  UpperCamelCase_: Dict   				=    XGBRegressor(verbosity=0     ,   random_state=4_2  )
  xgb.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Predict target for test data
  UpperCamelCase_: int   				=    xgb.predict(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    predictions.reshape(len(UpperCAmelCase__  )     ,   1  )
  return predictions

def   snake_case ()	->    None:
  UpperCamelCase_: Union[str, Any]   				=    fetch_california_housing()
  UpperCamelCase_				,UpperCamelCase_: Tuple   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25     ,   random_state=1  )
  UpperCamelCase_: Union[str, Any]   				=    xgboost(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )
  print(F'''Mean Square Error  : {mean_squared_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()