






'''simple docstring'''

import mpmath  # for roots of unity
import numpy as np




class      A     :






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None			):
       UpperCamelCase_					: List[str]            =				list(poly_a or [0]			)[:]
       UpperCamelCase_					: Any            =				list(poly_b or [0]			)[:]

       # Remove leading zero coefficients
       while self.polyA[-1] == 0:
             self.polyA.pop()
       UpperCamelCase_					: List[str]            =				len(self.polyA			)

       while self.polyB[-1] == 0:
             self.polyB.pop()
       UpperCamelCase_					: List[Any]            =				len(self.polyB			)

       # Add 0 to make lengths equal a power of 2
       UpperCamelCase_					: Optional[int]            =				int(
           2 ** np.ceil(np.loga(len(self.polyA			) + len(self.polyB			) - 1			)			)			)

       while len(self.polyA			) < self.c_max_length:
             self.polyA.append(0			)
       while len(self.polyB			) < self.c_max_length:
             self.polyB.append(0			)
       # A complex root used for the fourier transform
       UpperCamelCase_					: Any            =				complex(mpmath.root(x=1      ,  n=self.c_max_length      ,  k=1			)			)

       # The product
       UpperCamelCase_					: Optional[Any]            =				self.__multiply()
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[Any]            =				[[x] for x in self.polyA] if which == "A" else [[x] for x in self.polyB]
       # Corner case
       if len(__lowerCAmelCase			) <= 1:
             return dft[0]
       #
       UpperCamelCase_					: List[str]            =				self.c_max_length // 2
       while next_ncol > 0:
             UpperCamelCase_					: Optional[Any]            =				[[] for i in range(__lowerCAmelCase			)]
             UpperCamelCase_					: Tuple            =				self.root**next_ncol

             # First half of next step
             UpperCamelCase_					: List[Any]            =				1
             for j in range(self.c_max_length // (next_ncol * 2)			):
                   for i in range(__lowerCAmelCase			):
                         new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]			)
                   current_root *= root
             # Second half of next step
             UpperCamelCase_					: Tuple            =				1
             for j in range(self.c_max_length // (next_ncol * 2)			):
                   for i in range(__lowerCAmelCase			):
                         new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]			)
                   current_root *= root
             # Update
             UpperCamelCase_					: str            =				new_dft
             UpperCamelCase_					: Tuple            =				next_ncol // 2
       return dft[0]
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				self.__dft("""A"""			)
       UpperCamelCase_					: List[str]            =				self.__dft("""B"""			)
       UpperCamelCase_					: Union[str, Any]            =				[[dft_a[i] * dft_b[i] for i in range(self.c_max_length			)]]
       del dft_a
       del dft_b

       # Corner Case
       if len(inverce_c[0]			) <= 1:
             return inverce_c[0]
       # Inverse DFT
       UpperCamelCase_					: Optional[Any]            =				2
       while next_ncol <= self.c_max_length:
             UpperCamelCase_					: Union[str, Any]            =				[[] for i in range(__lowerCAmelCase			)]
             UpperCamelCase_					: List[Any]            =				self.root ** (next_ncol // 2)
             UpperCamelCase_					: Optional[int]            =				1
             # First half of next step
             for j in range(self.c_max_length // next_ncol			):
                   for i in range(next_ncol // 2			):
                         # Even positions
                         new_inverse_c[i].append(
                             (
                                 inverce_c[i][j]
                                 + inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / 2			)
                         # Odd positions
                         new_inverse_c[i + next_ncol // 2].append(
                             (
                                 inverce_c[i][j]
                                 - inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / (2 * current_root)			)
                   current_root *= root
             # Update
             UpperCamelCase_					: Optional[Any]            =				new_inverse_c
             next_ncol *= 2
       # Unpack
       UpperCamelCase_					: List[str]            =				[round(x[0].real      ,  8			) + round(x[0].imag      ,  8			) * 1j for x in inverce_c]

       # Remove leading 0's
       while inverce_c[-1] == 0:
             inverce_c.pop()
       return inverce_c





 def __str__(				self			):
       UpperCamelCase_					: Union[str, Any]            =				"A = " + " + ".join(
           F"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A]			)			)
       UpperCamelCase_					: str            =				"B = " + " + ".join(
           F"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B]			)			)
       UpperCamelCase_					: List[str]            =				"A*B = " + " + ".join(
           F"{coef}*x^{i}" for coef, i in enumerate(self.product			)			)

       return F"{a}\n{b}\n{c}"


# Unit tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()














'''simple docstring'''
import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


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




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






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




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  ort.SessionOptions()
       lowercase_						:     Optional[int]							     =  False
       return options





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
       lowercase_						:     Tuple							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
       lowercase_						:     List[str]							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"      )

       # using the PNDM scheduler by default
       lowercase_						:     List[Any]							     =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           "CompVis/stable-diffusion-v1-4"	,  revision="onnx"	,  safety_checker=a	,  feature_extractor=a	,  provider=self.gpu_provider	,  sess_options=self.gpu_options	,  )
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     List[str]							     =  "A red cat sitting on a park bench"

       lowercase_						:     int							     =  np.random.RandomState(0      )
       lowercase_						:     int							     =  pipe(
           prompt=a	,  image=a	,  mask_image=a	,  strength=0.75	,  guidance_scale=7.5	,  num_inference_steps=1_5	,  generator=a	,  output_type="np"	,  )

       lowercase_						:     Tuple							     =  output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)
       assert np.abs(expected_image - image      ).max() < 1e-2




