





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			:		Optional[Any]					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







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

										_snake_case :			Optional[Any]  						=   'bird'
										_snake_case :			List[Any]  						=   jax.device_count()
										_snake_case :			Union[str, Any]  						=   pipe.prepare_text_inputs([prompts] * num_samples					)

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

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

										_snake_case :			int  						=   replicate(lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   shard(lowerCamelCase_					)
										_snake_case :			List[str]  						=   shard(lowerCamelCase_					)

										_snake_case :			List[str]  						=   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)

										_snake_case :			Tuple  						=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
										_snake_case :			List[str]  						=   images[0, 2_53:2_56, 2_53:2_56, -1]

										_snake_case :			int  						=   jnp.asarray(jax.device_get(image_slice.flatten()					)					)
										_snake_case :			int  						=   jnp.array(
										    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]					)
										print(f'''output_slice: {output_slice}'''					)
										assert jnp.abs(output_slice - expected_slice					).max() < 1e-2







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







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

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

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

										_snake_case :			str  						=   jax.random.PRNGKey(0					)
										_snake_case :			Any  						=   jax.random.split(lowerCamelCase_ ,				jax.device_count()					)

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

										_snake_case :			Tuple  						=   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)

										_snake_case :			List[str]  						=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
										_snake_case :			List[Any]  						=   images[0, 2_53:2_56, 2_53:2_56, -1]

										_snake_case :			List[Any]  						=   jnp.asarray(jax.device_get(image_slice.flatten()					)					)
										_snake_case :			Optional[Any]  						=   jnp.array(
										    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]					)
										print(f'''output_slice: {output_slice}'''					)
										assert jnp.abs(output_slice - expected_slice					).max() < 1e-2













import functools

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):

				# Validation
				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) for day in days ):
								raise ValueError('The parameter days should be a list of integers' )

				if len(__lowerCAmelCase ) != 3 or not all(isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) for cost in costs ):
								raise ValueError('The parameter costs should be a list of three integers' )

				if len(__lowerCAmelCase ) == 0:
								return 0

				if min(__lowerCAmelCase ) <= 0:
								raise ValueError('All days elements should be greater than 0' )

				if max(__lowerCAmelCase ) >= 3_66:
								raise ValueError('All days elements should be less than 366' )

				_snake_case :			Optional[int]  						=   set(__lowerCAmelCase )

				@functools.cache
				def dynamic_programming(__lowerCAmelCase ) -> int:
								if index > 3_65:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1 )

								return min(
								    costs[0] + dynamic_programming(index + 1 )	,			costs[1] + dynamic_programming(index + 7 )	,			costs[2] + dynamic_programming(index + 30 )	,			)

				return dynamic_programming(1 )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






