
'''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 	__lowercase				(  unittest.TestCase    ):

			def        UpperCAmelCase__ (self      ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()

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

								lowerCamelCase_					:      int     =		'''bird'''
								lowerCamelCase_					:      Dict     =		jax.device_count()
								lowerCamelCase_					:      Any     =		pipe.prepare_text_inputs([prompts] * num_samples      )

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

								lowerCamelCase_					:      int     =		jax.random.PRNGKey(0      )
								lowerCamelCase_					:      List[Any]     =		jax.random.split(A			,   jax.device_count()      )

								lowerCamelCase_					:      str     =		replicate(A      )
								lowerCamelCase_					:      Union[str, Any]     =		shard(A      )
								lowerCamelCase_					:      Optional[Any]     =		shard(A      )

								lowerCamelCase_					:      Any     =		pipe(
								    prompt_ids=A			,   image=A			,   params=A			,   prng_seed=A			,   num_inference_steps=5_0			,   jit=A			,   ).images
								assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

								lowerCamelCase_					:      Union[str, Any]     =		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								lowerCamelCase_					:      Tuple     =		images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

								lowerCamelCase_					:      Optional[int]     =		jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								lowerCamelCase_					:      str     =		jnp.array(
								    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]      )
								print(F"""output_slice: {output_slice}"""      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose'''			,   from_pt=A			,   dtype=jnp.bfloataa      )
								lowerCamelCase_,      lowerCamelCase_					:      List[str]     =		FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5'''			,   controlnet=A			,   from_pt=A			,   dtype=jnp.bfloataa      )
								lowerCamelCase_					:      List[str]     =		controlnet_params

								lowerCamelCase_					:      List[str]     =		'''Chef in the kitchen'''
								lowerCamelCase_					:      str     =		jax.device_count()
								lowerCamelCase_					:      Optional[int]     =		pipe.prepare_text_inputs([prompts] * num_samples      )

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

								lowerCamelCase_					:      Union[str, Any]     =		jax.random.PRNGKey(0      )
								lowerCamelCase_					:      Optional[int]     =		jax.random.split(A			,   jax.device_count()      )

								lowerCamelCase_					:      Tuple     =		replicate(A      )
								lowerCamelCase_					:      str     =		shard(A      )
								lowerCamelCase_					:      Union[str, Any]     =		shard(A      )

								lowerCamelCase_					:      Union[str, Any]     =		pipe(
								    prompt_ids=A			,   image=A			,   params=A			,   prng_seed=A			,   num_inference_steps=5_0			,   jit=A			,   ).images
								assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

								lowerCamelCase_					:      int     =		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								lowerCamelCase_					:      Optional[int]     =		images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

								lowerCamelCase_					:      Optional[int]     =		jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								lowerCamelCase_					:      Dict     =		jnp.array(
								    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]      )
								print(F"""output_slice: {output_slice}"""      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2




'''simple docstring'''


from __future__ import annotations

from decimal import Decimal

from numpy import array




def lowercase_	(   _lowercase				)     ->							list[list[float]]:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		Decimal

					# Check if the provided matrix has 2 rows and 2 columns
					# since this implementation only works for 2x2 matrices
					if len(_lowercase				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
										# Calculate the determinant of the matrix
										lowerCamelCase_					:      Union[str, Any]     =		float(
										    d(matrix[0][0]				) * d(matrix[1][1]				) - d(matrix[1][0]				) * d(matrix[0][1]				)				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creates a copy of the matrix with swapped positions of the elements
										lowerCamelCase_					:      Optional[Any]     =		[[0.0, 0.0], [0.0, 0.0]]
										lowerCamelCase_,      lowerCamelCase_					:      int     =		matrix[1][1], matrix[0][0]
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		-matrix[1][0], -matrix[0][1]

										# Calculate the inverse of the matrix
										return [
										    [(float(d(_lowercase				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
										]
					elif (
					    len(_lowercase				) == 3
					    and len(matrix[0]				) == 3
					    and len(matrix[1]				) == 3
					    and len(matrix[2]				) == 3
					):
										# Calculate the determinant of the matrix using Sarrus rule
										lowerCamelCase_					:      Tuple     =		float(
										    (
										        (d(matrix[0][0]				) * d(matrix[1][1]				) * d(matrix[2][2]				))
										        + (d(matrix[0][1]				) * d(matrix[1][2]				) * d(matrix[2][0]				))
										        + (d(matrix[0][2]				) * d(matrix[1][0]				) * d(matrix[2][1]				))
										    )
										    - (
										        (d(matrix[0][2]				) * d(matrix[1][1]				) * d(matrix[2][0]				))
										        + (d(matrix[0][1]				) * d(matrix[1][0]				) * d(matrix[2][2]				))
										        + (d(matrix[0][0]				) * d(matrix[1][2]				) * d(matrix[2][1]				))
										    )				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creating cofactor matrix
										lowerCamelCase_					:      Any     =		[
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										]
										lowerCamelCase_					:      Union[str, Any]     =		(d(matrix[1][1]				) * d(matrix[2][2]				)) - (
										    d(matrix[1][2]				) * d(matrix[2][1]				)
										)
										lowerCamelCase_					:      Any     =		-(
										    (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[1][0]				) * d(matrix[2][1]				)) - (
										    d(matrix[1][1]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      Optional[Any]     =		-(
										    (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[0][0]				) * d(matrix[2][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      List[Any]     =		-(
										    (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Any     =		(d(matrix[0][1]				) * d(matrix[1][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[1][1]				)
										)
										lowerCamelCase_					:      Tuple     =		-(
										    (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
										)
										lowerCamelCase_					:      str     =		(d(matrix[0][0]				) * d(matrix[1][1]				)) - (
										    d(matrix[0][1]				) * d(matrix[1][0]				)
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										lowerCamelCase_					:      str     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				lowerCamelCase_					:      int     =		cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										lowerCamelCase_					:      int     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				inverse_matrix[i][j] /= d(_lowercase				)

        # Calculate the inverse of the matrix
										return [[float(d(_lowercase				)				) or 0.0 for n in row] for row in inverse_matrix]
					raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''				)


