


"""simple docstring"""




import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard




@slow
@require_flax
class 			__snake_case   (unittest.TestCase  ):




							def   __a   (     self:    List[str]   ):
										super().tearDown()
										gc.collect()




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/sd2-inpaint/init_image.png"""   )
										__lowerCamelCase          =      load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )

										__lowerCamelCase          =      """xvjiarui/stable-diffusion-2-inpainting"""
										__lowerCamelCase     ,__lowerCamelCase          =      FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case_     ,							safety_checker=snake_case_   )

										__lowerCamelCase          =      """Face of a yellow cat, high resolution, sitting on a park bench"""

										__lowerCamelCase          =      jax.random.PRNGKey(0   )
										__lowerCamelCase          =      50

										__lowerCamelCase          =      jax.device_count()
										__lowerCamelCase          =      num_samples * [prompt]
										__lowerCamelCase          =      num_samples * [init_image]
										__lowerCamelCase          =      num_samples * [mask_image]
										__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase          =      pipeline.prepare_inputs(snake_case_     ,							snake_case_     ,							snake_case_   )

										# shard inputs and rng
										__lowerCamelCase          =      replicate(snake_case_   )
										__lowerCamelCase          =      jax.random.split(snake_case_     ,							jax.device_count()   )
										__lowerCamelCase          =      shard(snake_case_   )
										__lowerCamelCase          =      shard(snake_case_   )
										__lowerCamelCase          =      shard(snake_case_   )

										__lowerCamelCase          =      pipeline(
										    snake_case_     ,							snake_case_     ,							snake_case_     ,							snake_case_     ,							snake_case_     ,							snake_case_     ,							jit=snake_case_   )

										__lowerCamelCase          =      output.images.reshape(snake_case_     ,							5_12     ,							5_12     ,							3   )

										__lowerCamelCase          =      images[0, 2_53:2_56, 2_53:2_56, -1]

										__lowerCamelCase          =      jnp.asarray(jax.device_get(image_slice.flatten()   )   )
										__lowerCamelCase          =      jnp.array(
										    [0.3_611_307, 0.37_649_736, 0.3_757_408, 0.38_213_953, 0.39_295_167, 0.3_841_631, 0.41_554_978, 0.4_137_475, 0.4_217_084]   )
										print(f'output_slice: {output_slice}'   )
										assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2








import doctest
from collections import deque

import numpy as np
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self    ) ->      None:







								'''simple docstring'''

								__lowercase			       = [2, 1, 2, -1]
								__lowercase			       = [1, 2, 3, 4]






					def A   (					self    ) ->      list[float]:







								'''simple docstring'''

								__lowercase			       = len(self.first_signal    )
								__lowercase			       = len(self.second_signal    )

								__lowercase			       = max(snake_case_			,							snake_case_    )

								# create a zero matrix of max_length x max_length
								__lowercase			       = [[0] * max_length for i in range(snake_case_    )]

								# fills the smaller signal with zeros to make both signals of same length
								if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
								elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
								for i in range(snake_case_    ):
											__lowercase			       = deque(self.second_signal    )
											rotated_signal.rotate(snake_case_    )
											for j, item in enumerate(snake_case_    ):
														matrix[i][j] += item

        # multiply the matrix with the first signal
								__lowercase			       = np.matmul(np.transpose(snake_case_    )			,							np.transpose(self.first_signal    )    )

								# rounding-off to two decimal places
								return [round(snake_case_			,							2    ) for i in final_signal]


if __name__ == "__main__":
	doctest.testmod()


