





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase      							=   False







class       __A							(   unittest.TestCase	):




      """simple docstring"""







      pass







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




      """simple docstring"""







      def 					__snake_case      (		self):



             """simple docstring"""





             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,				torch_dtype=torch.floataa)
             pipe.to(a__)
             pipe.set_progress_bar_config(disable=a__)

             _lowerCamelCase				:  Union[str, Any]						=		load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''')

             _lowerCamelCase				:  str						=		torch.manual_seed(0)
             _lowerCamelCase				:  List[Any]						=		pipe.dual_guided(
                 prompt='''first prompt'''   ,				image=a__   ,				text_to_image_strength=0.75   ,				generator=a__   ,				guidance_scale=7.5   ,				num_inference_steps=2   ,				output_type='''numpy'''   ,				).images

             with tempfile.TemporaryDirectory() as tmpdirname:
                    pipe.save_pretrained(a__)
                    _lowerCamelCase				:  Union[str, Any]						=		VersatileDiffusionPipeline.from_pretrained(a__   ,				torch_dtype=torch.floataa)
             pipe.to(a__)
             pipe.set_progress_bar_config(disable=a__)

             _lowerCamelCase				:  List[str]						=		generator.manual_seed(0)
             _lowerCamelCase				:  Tuple						=		pipe.dual_guided(
                 prompt='''first prompt'''   ,				image=a__   ,				text_to_image_strength=0.75   ,				generator=a__   ,				guidance_scale=7.5   ,				num_inference_steps=2   ,				output_type='''numpy'''   ,				).images

             assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass"



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,				torch_dtype=torch.floataa)
             pipe.to(a__)
             pipe.set_progress_bar_config(disable=a__)

             _lowerCamelCase				:  str						=		'''cyberpunk 2077'''
             _lowerCamelCase				:  str						=		load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''')
             _lowerCamelCase				:  int						=		torch.manual_seed(0)
             _lowerCamelCase				:  Union[str, Any]						=		pipe.dual_guided(
                 prompt=a__   ,				image=a__   ,				text_to_image_strength=0.75   ,				generator=a__   ,				guidance_scale=7.5   ,				num_inference_steps=50   ,				output_type='''numpy'''   ,				).images

             _lowerCamelCase				:  Optional[int]						=		image[0, 253:256, 253:256, -1]

             assert image.shape == (1, 512, 512, 3)
             _lowerCamelCase				:  Dict						=		np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001])

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

             _lowerCamelCase				:  Optional[int]						=		'''A painting of a squirrel eating a burger '''
             _lowerCamelCase				:  List[str]						=		torch.manual_seed(0)
             _lowerCamelCase				:  List[Any]						=		pipe.text_to_image(
                 prompt=a__   ,				generator=a__   ,				guidance_scale=7.5   ,				num_inference_steps=50   ,				output_type='''numpy''').images

             _lowerCamelCase				:  Any						=		image[0, 253:256, 253:256, -1]

             assert image.shape == (1, 512, 512, 3)
             _lowerCamelCase				:  List[str]						=		np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778])

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

             _lowerCamelCase				:  Any						=		pipe.image_variation(a__   ,				generator=a__   ,				output_type='''numpy''').images

             _lowerCamelCase				:  Optional[Any]						=		image[0, 253:256, 253:256, -1]

             assert image.shape == (1, 512, 512, 3)
             _lowerCamelCase				:  Optional[int]						=		np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456])

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








import mpmath  # for roots of unity
import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__=None   ,				a__=None):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		list(poly_a or [0])[:]
             _lowerCamelCase				:  Union[str, Any]						=		list(poly_b or [0])[:]

             # Remove leading zero coefficients
             while self.polyA[-1] == 0:
                    self.polyA.pop()
             _lowerCamelCase				:  Optional[int]						=		len(self.polyA)

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

             # Add 0 to make lengths equal a power of 2
             _lowerCamelCase				:  Dict						=		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
             _lowerCamelCase				:  int						=		complex(mpmath.root(x=1   ,				n=self.c_max_length   ,				k=1))

             # The product
             _lowerCamelCase				:  Optional[int]						=		self.__multiply()


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
             # Corner case
             if len(a__) <= 1:
                    return dft[0]
             #
             _lowerCamelCase				:  Optional[Any]						=		self.c_max_length // 2
             while next_ncol > 0:
                    _lowerCamelCase				:  Optional[Any]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  List[str]						=		self.root**next_ncol

                    # First half of next step
                    _lowerCamelCase				:  Tuple						=		1
                    for j in range(self.c_max_length // (next_ncol * 2)):
                           for i in range(a__):
                                  new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
                           current_root *= root
                    # Second half of next step
                    _lowerCamelCase				:  Tuple						=		1
                    for j in range(self.c_max_length // (next_ncol * 2)):
                           for i in range(a__):
                                  new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
                           current_root *= root
                    # Update
                    _lowerCamelCase				:  Tuple						=		new_dft
                    _lowerCamelCase				:  Union[str, Any]						=		next_ncol // 2
             return dft[0]


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.__dft('''A''')
             _lowerCamelCase				:  Optional[Any]						=		self.__dft('''B''')
             _lowerCamelCase				:  List[str]						=		[[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
             _lowerCamelCase				:  str						=		2
             while next_ncol <= self.c_max_length:
                    _lowerCamelCase				:  Optional[int]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  Dict						=		self.root ** (next_ncol // 2)
                    _lowerCamelCase				:  List[str]						=		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
                    _lowerCamelCase				:  Any						=		new_inverse_c
                    next_ncol *= 2
             # Unpack
             _lowerCamelCase				:  Optional[int]						=		[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):



             """simple docstring"""





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

