

'''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    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()







    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/sd2-inpaint/init_image.png"""    )
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""    )

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

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

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

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

           # shard inputs and rng
           A_		   = replicate(UpperCamelCase__    )
           A_		   = jax.random.split(UpperCamelCase__						,			jax.device_count()    )
           A_		   = shard(UpperCamelCase__    )
           A_		   = shard(UpperCamelCase__    )
           A_		   = shard(UpperCamelCase__    )

           A_		   = pipeline(
               UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			jit=UpperCamelCase__    )

           A_		   = output.images.reshape(UpperCamelCase__						,			512						,			512						,			3    )

           A_		   = images[0, 253:256, 253:256, -1]

           A_		   = jnp.asarray(jax.device_get(image_slice.flatten()    )    )
           A_		   = jnp.array(
               [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]    )
           print(f'''output_slice: {output_slice}'''    )
           assert jnp.abs(output_slice - expected_slice    ).max() < 1e-2



'''simple docstring'''







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    )     ->     list[float]:
       A_   ,     A_		   = coefficient_matrix.shape
       A_   ,     A_		   = constant_matrix.shape

       if rowsa != colsa:
              A_		   = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

       if colsa != 1:
              A_		   = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

       if rowsa != rowsa:
              A_		   = (
                  """Coefficient and constant matrices dimensions must be nxn and nx1 but """
                  F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
              )
              raise ValueError(UpperCAmelCase__			)

       if len(UpperCAmelCase__			) != rowsa:
              A_		   = (
                  """Number of initial values must be equal to number of rows in coefficient """
                  F'''matrix but received {len(UpperCAmelCase__			)} and {rowsa}'''
              )
              raise ValueError(UpperCAmelCase__			)

       if iterations <= 0:
              raise ValueError("""Iterations must be at least 1"""			)

       A_		   = np.concatenate(
           (coefficient_matrix, constant_matrix),    axis=1			)

       A_   ,     A_		   = table.shape

       strictly_diagonally_dominant(UpperCAmelCase__			)

       # Iterates the whole matrix for given number of times
       for _ in range(UpperCAmelCase__			):
              A_		   = []
              for row in range(UpperCAmelCase__			):
                     A_		   = 0
                     for col in range(UpperCAmelCase__			):
                            if col == row:
                                   A_		   = table[row][col]
                            elif col == cols - 1:
                                   A_		   = table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     A_		   = (temp + val) / denom
                     new_val.append(UpperCAmelCase__			)
              A_		   = new_val

       return [float(UpperCAmelCase__			) for i in new_val]
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     bool:
       A_   ,     A_		   = table.shape

       A_		   = True

       for i in range(0,    UpperCAmelCase__			):
              A_		   = 0
              for j in range(0,    cols - 1			):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""			)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
      import doctest

      doctest.testmod()
