



"""simple docstring"""






from __future__ import annotations

from scipy.special import comb  # type: ignore






class        __snake_case       :




  def __init__( self     :						Any						,					__lowerCAmelCase     :						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= list_of_points
         # Degree determines the flexibility of the curve.
         # Degree = 1 will produce a straight line.
         _lowerCamelCase						:				List[str]    			= len(SCREAMING_SNAKE_CASE_		) - 1




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple						,					__lowerCAmelCase     :						List[Any]		):






         """simple docstring"""






         assert 0 <= t <= 1, "Time t must be between 0 and 1."
         _lowerCamelCase						:				list[float]    			= []
         for i in range(len(self.list_of_points		)		):
                # basis function for each i
                output_values.append(
                    comb(self.degree						,					SCREAMING_SNAKE_CASE_		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
         # the basis must sum up to 1 for it to produce a valid Bezier curve.
         assert round(sum(SCREAMING_SNAKE_CASE_		)						,					5		) == 1
         return output_values




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict						,					__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






         assert 0 <= t <= 1, "Time t must be between 0 and 1."

         _lowerCamelCase						:				Union[str, Any]    			= self.basis_function(SCREAMING_SNAKE_CASE_		)
         _lowerCamelCase						:				Union[str, Any]    			= 0.0
         _lowerCamelCase						:				List[Any]    			= 0.0
         for i in range(len(self.list_of_points		)		):
                # For all points, sum up the product of i-th basis function and i-th point.
                x += basis_function[i] * self.list_of_points[i][0]
                y += basis_function[i] * self.list_of_points[i][1]
         return (x, y)




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					__lowerCAmelCase     :						Optional[Any] = 0.01		):






         """simple docstring"""






         from matplotlib import pyplot as plt  # type: ignore

         _lowerCamelCase						:				list[float]    			= []  # x coordinates of points to plot
         _lowerCamelCase						:				list[float]    			= []  # y coordinates of points to plot

         _lowerCamelCase						:				List[Any]    			= 0.0
         while t <= 1:
                _lowerCamelCase						:				Union[str, Any]    			= self.bezier_curve_function(SCREAMING_SNAKE_CASE_		)
                to_plot_x.append(value[0]		)
                to_plot_y.append(value[1]		)
                t += step_size

         _lowerCamelCase						:				Union[str, Any]    			= [i[0] for i in self.list_of_points]
         _lowerCamelCase						:				Tuple    			= [i[1] for i in self.list_of_points]

         plt.plot(
             SCREAMING_SNAKE_CASE_						,					SCREAMING_SNAKE_CASE_						,					color='''blue'''						,					label='''Curve of Degree ''' + str(self.degree		)						,					)
         plt.scatter(SCREAMING_SNAKE_CASE_						,					SCREAMING_SNAKE_CASE_						,					color='''red'''						,					label='''Control Points'''		)
         plt.legend()
         plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
   BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
   BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3






'''simple docstring'''



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    UpperCAmelCase_  (_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    pass







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





    """simple docstring"""






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







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







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Any								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Tuple								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Union[str, Any]								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Union[str, Any]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	Optional[Any]								=		output.images
         __lowerCamelCase   :	Tuple								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	List[str]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Dict								=		LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     subfolder='scheduler'			,     revision='onnx'	)
         __lowerCamelCase   :	Tuple								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     scheduler=SCREAMING_SNAKE_CASE_			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

         __lowerCamelCase   :	Tuple								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Optional[int]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	List[str]								=		output.images
         __lowerCamelCase   :	Dict								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Tuple								=		np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3






