


'''simple docstring'''

from __future__ import annotations

from scipy.special import comb  # type: ignore





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  list_of_points
   # Degree determines the flexibility of the curve.
   # Degree = 1 will produce a straight line.
   lowerCamelCase_      =  len(UpperCamelCase     ) - 1






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   assert 0 <= t <= 1, "Time t must be between 0 and 1."
   lowerCamelCase_      =  []
   for i in range(len(self.list_of_points     )     ):
    # basis function for each i
    output_values.append(
        comb(self.degree			,  UpperCamelCase     ) * ((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(UpperCamelCase     )			,  5     ) == 1
   return output_values






  def   snake_case		(    self			,  UpperCamelCase     ):







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

   lowerCamelCase_      =  self.basis_function(UpperCamelCase     )
   lowerCamelCase_      =  0.0
   lowerCamelCase_      =  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   snake_case		(    self			,  UpperCamelCase = 0.01     ):







   """simple docstring"""
   from matplotlib import pyplot as plt  # type: ignore

   lowerCamelCase_      =  []  # x coordinates of points to plot
   lowerCamelCase_      =  []  # y coordinates of points to plot

   lowerCamelCase_      =  0.0
   while t <= 1:
    lowerCamelCase_      =  self.bezier_curve_function(UpperCamelCase     )
    to_plot_x.append(value[0]     )
    to_plot_y.append(value[1]     )
    t += step_size

   lowerCamelCase_      =  [i[0] for i in self.list_of_points]
   lowerCamelCase_      =  [i[1] for i in self.list_of_points]

   plt.plot(
       UpperCamelCase			,  UpperCamelCase			,  color="blue"			,  label="Curve of Degree " + str(self.degree     )			,  )
   plt.scatter(UpperCamelCase			,  UpperCamelCase			,  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





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 			__A( a  ,   unittest.TestCase			):
				# FIXME: add fast tests
				pass







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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

								__a										=    '''A red cat sitting on a park bench'''

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=10		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    LMSDiscreteScheduler.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     subfolder='''scheduler'''		,     revision='''onnx'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     scheduler=_snake_case		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

								__a										=    '''A red cat sitting on a park bench'''

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=20		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]				)

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