


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  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().tearDown()
										gc.collect()






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					FlaxControlNetModel.from_pretrained(
										    'lllyasviel/sd-controlnet-canny'			,						from_pt=lowerCAmelCase__			,						dtype=jnp.bfloataa       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
										    'runwayml/stable-diffusion-v1-5'			,						controlnet=lowerCAmelCase__			,						from_pt=lowerCAmelCase__			,						dtype=jnp.bfloataa       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					controlnet_params

										SCREAMING_SNAKE_CASE_		:     List[str]					=					"bird"
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					jax.device_count()
										SCREAMING_SNAKE_CASE_		:     int					=					pipe.prepare_text_inputs([prompts] * num_samples       )

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

										SCREAMING_SNAKE_CASE_		:     Tuple					=					jax.random.PRNGKey(0       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					jax.random.split(lowerCAmelCase__			,						jax.device_count()       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					replicate(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					shard(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					shard(lowerCAmelCase__       )

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

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

										SCREAMING_SNAKE_CASE_		:     Any					=					jnp.asarray(jax.device_get(image_slice.flatten()       )       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					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 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					FlaxControlNetModel.from_pretrained(
										    'lllyasviel/sd-controlnet-openpose'			,						from_pt=lowerCAmelCase__			,						dtype=jnp.bfloataa       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
										    'runwayml/stable-diffusion-v1-5'			,						controlnet=lowerCAmelCase__			,						from_pt=lowerCAmelCase__			,						dtype=jnp.bfloataa       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					controlnet_params

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					"Chef in the kitchen"
										SCREAMING_SNAKE_CASE_		:     Tuple					=					jax.device_count()
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					pipe.prepare_text_inputs([prompts] * num_samples       )

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

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					jax.random.PRNGKey(0       )
										SCREAMING_SNAKE_CASE_		:     int					=					jax.random.split(lowerCAmelCase__			,						jax.device_count()       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					replicate(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					shard(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					shard(lowerCAmelCase__       )

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

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

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					jnp.asarray(jax.device_get(image_slice.flatten()       )       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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







def 		UpperCamelCase     (       _UpperCAmelCase  :   list[int] ,			_UpperCAmelCase  :   list[int]  )   ->			tuple[float, float]:






						'''simple docstring'''

						if not len(_UpperCAmelCase  ) == len(_UpperCAmelCase  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_lowercase      ,    _lowercase      ,    _lowercase       :	List[str]           =   equationa
						_lowercase      ,    _lowercase      ,    _lowercase       :	List[str]           =   equationa

						# Calculate the determinants of the matrices
						_lowercase       :	List[Any]           =   aa * ba - aa * ba
						_lowercase       :	Tuple           =   ca * ba - ca * ba
						_lowercase       :	Union[str, Any]           =   aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_lowercase       :	str           =   determinant_x / determinant
																		_lowercase       :	Union[str, Any]           =   determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)




