


'''simple docstring'''






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__	       =     FlaxControlNetModel.from_pretrained(
														    '''lllyasviel/sd-controlnet-canny''' ,   from_pt=lowerCamelCase_ ,   dtype=jnp.bfloataa       )
														lowerCAmelCase__	       =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    '''runwayml/stable-diffusion-v1-5''' ,   controlnet=lowerCamelCase_ ,   from_pt=lowerCamelCase_ ,   dtype=jnp.bfloataa       )
														lowerCAmelCase__	       =     controlnet_params

														lowerCAmelCase__	       =     'bird'
														lowerCAmelCase__	       =     jax.device_count()
														lowerCAmelCase__	       =     pipe.prepare_text_inputs([prompts] * num_samples       )

														lowerCAmelCase__	       =     load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''       )
														lowerCAmelCase__	       =     pipe.prepare_image_inputs([canny_image] * num_samples       )

														lowerCAmelCase__	       =     jax.random.PRNGKey(0       )
														lowerCAmelCase__	       =     jax.random.split(lowerCamelCase_ ,   jax.device_count()       )

														lowerCAmelCase__	       =     replicate(lowerCamelCase_       )
														lowerCAmelCase__	       =     shard(lowerCamelCase_       )
														lowerCAmelCase__	       =     shard(lowerCamelCase_       )

														lowerCAmelCase__	       =     pipe(
														    prompt_ids=lowerCamelCase_ ,   image=lowerCamelCase_ ,   params=lowerCamelCase_ ,   prng_seed=lowerCamelCase_ ,   num_inference_steps=50 ,   jit=lowerCamelCase_ ,   ).images
														assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

														lowerCAmelCase__	       =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-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(
														    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]       )
														print(F"""output_slice: {output_slice}"""       )
														assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     FlaxControlNetModel.from_pretrained(
														    '''lllyasviel/sd-controlnet-openpose''' ,   from_pt=lowerCamelCase_ ,   dtype=jnp.bfloataa       )
														lowerCAmelCase__	       =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    '''runwayml/stable-diffusion-v1-5''' ,   controlnet=lowerCamelCase_ ,   from_pt=lowerCamelCase_ ,   dtype=jnp.bfloataa       )
														lowerCAmelCase__	       =     controlnet_params

														lowerCAmelCase__	       =     'Chef in the kitchen'
														lowerCAmelCase__	       =     jax.device_count()
														lowerCAmelCase__	       =     pipe.prepare_text_inputs([prompts] * num_samples       )

														lowerCAmelCase__	       =     load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''       )
														lowerCAmelCase__	       =     pipe.prepare_image_inputs([pose_image] * num_samples       )

														lowerCAmelCase__	       =     jax.random.PRNGKey(0       )
														lowerCAmelCase__	       =     jax.random.split(lowerCamelCase_ ,   jax.device_count()       )

														lowerCAmelCase__	       =     replicate(lowerCamelCase_       )
														lowerCAmelCase__	       =     shard(lowerCamelCase_       )
														lowerCAmelCase__	       =     shard(lowerCamelCase_       )

														lowerCAmelCase__	       =     pipe(
														    prompt_ids=lowerCamelCase_ ,   image=lowerCamelCase_ ,   params=lowerCamelCase_ ,   prng_seed=lowerCamelCase_ ,   num_inference_steps=50 ,   jit=lowerCamelCase_ ,   ).images
														assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

														lowerCAmelCase__	       =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-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(
														    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]       )
														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  _snake_case       (					A			,   A			,   A			,   A			,   )						->   list[float]:
							lowerCAmelCase__       , lowerCAmelCase__	       =     coefficient_matrix.shape
							lowerCAmelCase__       , lowerCAmelCase__	       =     constant_matrix.shape

							if rowsa != colsa:
														lowerCAmelCase__	       =     F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(A	)

							if colsa != 1:
														lowerCAmelCase__	       =     F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(A	)

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

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

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

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

							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							strictly_diagonally_dominant(A	)

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

							return [float(A	) for i in new_val]


def  _snake_case       (					A	)						->   bool:
							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							lowerCAmelCase__	       =     True

							for i in range(0			,   A	):
														lowerCAmelCase__	       =     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()