






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 					_snake_case						(			_A       ,    _A       ,    _A       ,    unittest.TestCase ):
	_A          =   StableDiffusionInstructPixaPixPipeline
	_A          =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
	_A          =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_A          =   IMAGE_TO_IMAGE_IMAGE_PARAMS
	_A          =   IMAGE_TO_IMAGE_IMAGE_PARAMS






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					torch.manual_seed(0       )
					snake_case__      :Optional[int]         =   UNetaDConditionModel(
					    block_out_channels=(32, 64)					,layers_per_block=2					,sample_size=32					,in_channels=8					,out_channels=4					,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,cross_attention_dim=32					,)
					snake_case__      :Union[str, Any]         =   PNDMScheduler(skip_prk_steps=UpperCamelCase       )
					torch.manual_seed(0       )
					snake_case__      :Optional[Any]         =   AutoencoderKL(
					    block_out_channels=[32, 64]					,in_channels=3					,out_channels=3					,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,latent_channels=4					,)
					torch.manual_seed(0       )
					snake_case__      :Optional[int]         =   CLIPTextConfig(
					    bos_token_id=0					,eos_token_id=2					,hidden_size=32					,intermediate_size=37					,layer_norm_eps=1E-05					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=1_000					,)
					snake_case__      :Optional[Any]         =   CLIPTextModel(UpperCamelCase       )
					snake_case__      :Optional[int]         =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

					snake_case__      :Optional[Any]         =   {
					    "unet": unet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase=0       )						->    Optional[int]:
					snake_case__      :Tuple         =   floats_tensor((1, 3, 32, 32)					,rng=random.Random(UpperCamelCase       )       ).to(UpperCamelCase       )
					snake_case__      :Tuple         =   image.cpu().permute(0					,2					,3					,1       )[0]
					snake_case__      :Union[str, Any]         =   Image.fromarray(np.uinta(UpperCamelCase       )       ).convert("RGB"       )
					if str(UpperCamelCase       ).startswith("mps"       ):
									snake_case__      :Dict         =   torch.manual_seed(UpperCamelCase       )
					else:
									snake_case__      :List[Any]         =   torch.Generator(device=UpperCamelCase       ).manual_seed(UpperCamelCase       )
					snake_case__      :Tuple         =   {
					    "prompt": "A painting of a squirrel eating a burger",
					    "image": image,
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "image_guidance_scale": 1,
					    "output_type": "numpy",
					}
					return inputs






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :List[Any]         =   "cpu"  # ensure determinism for the device-dependent torch.Generator
					snake_case__      :Union[str, Any]         =   self.get_dummy_components()
					snake_case__      :List[Any]         =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase       )
					snake_case__      :int         =   sd_pipe.to(UpperCamelCase       )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :Dict         =   self.get_dummy_inputs(UpperCamelCase       )
					snake_case__      :Optional[Any]         =   sd_pipe(**UpperCamelCase       ).images
					snake_case__      :Optional[Any]         =   image[0, -3:, -3:, -1]
					assert image.shape == (1, 32, 32, 3)
					snake_case__      :Union[str, Any]         =   np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Tuple         =   "cpu"  # ensure determinism for the device-dependent torch.Generator
					snake_case__      :Any         =   self.get_dummy_components()
					snake_case__      :List[Any]         =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase       )
					snake_case__      :Optional[Any]         =   sd_pipe.to(UpperCamelCase       )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :Optional[int]         =   self.get_dummy_inputs(UpperCamelCase       )
					snake_case__      :List[str]         =   "french fries"
					snake_case__      :int         =   sd_pipe(**UpperCamelCase					,negative_prompt=UpperCamelCase       )
					snake_case__      :Optional[int]         =   output.images
					snake_case__      :Any         =   image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					snake_case__      :Optional[int]         =   np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Optional[Any]         =   "cpu"  # ensure determinism for the device-dependent torch.Generator
					snake_case__      :List[Any]         =   self.get_dummy_components()
					snake_case__      :Optional[int]         =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase       )
					snake_case__      :List[Any]         =   sd_pipe.to(UpperCamelCase       )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :Tuple         =   self.get_dummy_inputs(UpperCamelCase       )
					snake_case__      :int         =   [inputs["prompt"]] * 2

					snake_case__      :Optional[Any]         =   np.array(inputs["image"]       ).astype(np.floataa       ) / 255.0
					snake_case__      :Union[str, Any]         =   torch.from_numpy(UpperCamelCase       ).unsqueeze(0       ).to(UpperCamelCase       )
					snake_case__      :Tuple         =   image / 2 + 0.5
					snake_case__      :Tuple         =   image.permute(0					,3					,1					,2       )
					snake_case__      :Dict         =   image.repeat(2					,1					,1					,1       )

					snake_case__      :List[Any]         =   sd_pipe(**UpperCamelCase       ).images
					snake_case__      :Tuple         =   image[-1, -3:, -3:, -1]

					assert image.shape == (2, 32, 32, 3)
					snake_case__      :str         =   np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :int         =   "cpu"  # ensure determinism for the device-dependent torch.Generator
					snake_case__      :Dict         =   self.get_dummy_components()
					snake_case__      :Any         =   EulerAncestralDiscreteScheduler(
					    beta_start=0.00085					,beta_end=0.012					,beta_schedule="scaled_linear"       )
					snake_case__      :Tuple         =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase       )
					snake_case__      :str         =   sd_pipe.to(UpperCamelCase       )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :Dict         =   self.get_dummy_inputs(UpperCamelCase       )
					snake_case__      :Dict         =   sd_pipe(**UpperCamelCase       ).images
					snake_case__      :int         =   image[0, -3:, -3:, -1]

					snake_case__      :Optional[int]         =   [round(UpperCamelCase					,4       ) for x in image_slice.flatten().tolist()]
					print(",".join([str(UpperCamelCase       ) for x in slice]       )       )

					assert image.shape == (1, 32, 32, 3)
					snake_case__      :str         =   np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






	def    lowerCAmelCase_							(		self       )						->    Dict:
					super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Optional[Any]         =   self.get_dummy_components()
					snake_case__      :List[Any]         =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase       )
					snake_case__      :List[Any]         =   VaeImageProcessor(do_resize=UpperCamelCase					,do_normalize=UpperCamelCase       )
					snake_case__      :Any         =   pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :Union[str, Any]         =   pipe(**self.get_dummy_inputs_by_type(UpperCamelCase					,input_image_type="pt"       )       )[0]

					snake_case__      :Dict         =   components["vae"]
					snake_case__      :Optional[Any]         =   self.get_dummy_inputs_by_type(UpperCamelCase					,input_image_type="pt"       )

					for image_param in self.image_latents_params:
									if image_param in inputs.keys():
													snake_case__      :List[str]         =   vae.encode(inputs[image_param]       ).latent_dist.mode()

					snake_case__      :Dict         =   pipe(**UpperCamelCase       )[0]

					snake_case__      :str         =   np.abs(out - out_latents_inputs       ).max()
					self.assertLess(UpperCamelCase					,1E-4					,"passing latents as image input generate different result from passing image"       )




@slow
@require_torch_gpu
class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def    lowerCAmelCase_							(		self					,UpperCamelCase=0       )						->    Optional[int]:
					snake_case__      :List[Any]         =   torch.manual_seed(UpperCamelCase       )
					snake_case__      :Union[str, Any]         =   load_image(
					    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"       )
					snake_case__      :List[Any]         =   {
					    "prompt": "turn him into a cyborg",
					    "image": image,
					    "generator": generator,
					    "num_inference_steps": 3,
					    "guidance_scale": 7.5,
					    "image_guidance_scale": 1.0,
					    "output_type": "numpy",
					}
					return inputs






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Optional[Any]         =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"					,safety_checker=UpperCamelCase       )
					pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )
					pipe.enable_attention_slicing()

					snake_case__      :int         =   self.get_inputs()
					snake_case__      :List[str]         =   pipe(**UpperCamelCase       ).images
					snake_case__      :Dict         =   image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					snake_case__      :Dict         =   np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]       )

					assert np.abs(expected_slice - image_slice       ).max() < 1E-3






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :List[str]         =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"					,safety_checker=UpperCamelCase       )
					snake_case__      :List[str]         =   LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
					pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )
					pipe.enable_attention_slicing()

					snake_case__      :Optional[Any]         =   self.get_inputs()
					snake_case__      :Dict         =   pipe(**UpperCamelCase       ).images
					snake_case__      :Any         =   image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					snake_case__      :Optional[Any]         =   np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]       )

					assert np.abs(expected_slice - image_slice       ).max() < 1E-3






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :str         =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"					,safety_checker=UpperCamelCase       )
					snake_case__      :Any         =   DDIMScheduler.from_config(pipe.scheduler.config       )
					pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )
					pipe.enable_attention_slicing()

					snake_case__      :Tuple         =   self.get_inputs()
					snake_case__      :str         =   pipe(**UpperCamelCase       ).images
					snake_case__      :Union[str, Any]         =   image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					snake_case__      :List[Any]         =   np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]       )

					assert np.abs(expected_slice - image_slice       ).max() < 1E-3






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :str         =   0

					def callback_fn(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       ) -> None:
									snake_case__      :Union[str, Any]         =   True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
													snake_case__      :Optional[int]         =   latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 64, 64)
													snake_case__      :Any         =   latents[0, -3:, -3:, -1]
													snake_case__      :Optional[Any]         =   np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]       )

													assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2
									elif step == 2:
													snake_case__      :Tuple         =   latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 64, 64)
													snake_case__      :List[str]         =   latents[0, -3:, -3:, -1]
													snake_case__      :Tuple         =   np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]       )

													assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2

					snake_case__      :Any         =   False

					snake_case__      :Tuple         =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"					,safety_checker=UpperCamelCase					,torch_dtype=torch.floataa       )
					snake_case__      :Optional[Any]         =   pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )
					pipe.enable_attention_slicing()

					snake_case__      :Dict         =   self.get_inputs()
					pipe(**UpperCamelCase					,callback=UpperCamelCase					,callback_steps=1       )
					assert callback_fn.has_been_called
					assert number_of_steps == 3






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					snake_case__      :int         =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"					,safety_checker=UpperCamelCase					,torch_dtype=torch.floataa       )
					snake_case__      :int         =   pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )
					pipe.enable_attention_slicing(1       )
					pipe.enable_sequential_cpu_offload()

					snake_case__      :Any         =   self.get_inputs()
					snake_case__      :int         =   pipe(**UpperCamelCase       )

					snake_case__      :Dict         =   torch.cuda.max_memory_allocated()
					# make sure that less than 2.2 GB is allocated
					assert mem_bytes < 2.2 * 10**9






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Any         =   self.get_inputs()
					# resize to resolution that is divisible by 8 but not 16 or 32
					snake_case__      :Optional[int]         =   inputs["image"].resize((504, 504)       )

					snake_case__      :List[Any]         =   "timbrooks/instruct-pix2pix"
					snake_case__      :List[str]         =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    UpperCamelCase					,safety_checker=UpperCamelCase					,)
					pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )
					pipe.enable_attention_slicing()

					snake_case__      :Optional[int]         =   pipe(**UpperCamelCase       )
					snake_case__      :Any         =   output.images[0]

					snake_case__      :Union[str, Any]         =   image[255:258, 383:386, -1]

					assert image.shape == (504, 504, 3)
					snake_case__      :Optional[int]         =   np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-3







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 					_snake_case						(			unittest.TestCase ):






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






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__    ,						snake_case__      :Tuple         =   FlaxControlNetModel.from_pretrained(
					    "lllyasviel/sd-controlnet-canny"					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__    ,						snake_case__      :Any         =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"					,controlnet=UpperCamelCase					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__      :List[str]         =   controlnet_params

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

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

					snake_case__      :List[str]         =   jax.random.PRNGKey(0       )
					snake_case__      :str         =   jax.random.split(UpperCamelCase					,jax.device_count()       )

					snake_case__      :int         =   replicate(UpperCamelCase       )
					snake_case__      :Any         =   shard(UpperCamelCase       )
					snake_case__      :Any         =   shard(UpperCamelCase       )

					snake_case__      :str         =   pipe(
					    prompt_ids=UpperCamelCase					,image=UpperCamelCase					,params=UpperCamelCase					,prng_seed=UpperCamelCase					,num_inference_steps=50					,jit=UpperCamelCase					,).images
					assert images.shape == (jax.device_count(), 1, 768, 512, 3)

					snake_case__      :List[str]         =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
					snake_case__      :Any         =   images[0, 253:256, 253:256, -1]

					snake_case__      :Union[str, Any]         =   jnp.asarray(jax.device_get(image_slice.flatten()       )       )
					snake_case__      :List[Any]         =   jnp.array(
					    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__    ,						snake_case__      :List[str]         =   FlaxControlNetModel.from_pretrained(
					    "lllyasviel/sd-controlnet-openpose"					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__    ,						snake_case__      :Optional[Any]         =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"					,controlnet=UpperCamelCase					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__      :str         =   controlnet_params

					snake_case__      :int         =   "Chef in the kitchen"
					snake_case__      :List[Any]         =   jax.device_count()
					snake_case__      :Dict         =   pipe.prepare_text_inputs([prompts] * num_samples       )

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

					snake_case__      :List[str]         =   jax.random.PRNGKey(0       )
					snake_case__      :Any         =   jax.random.split(UpperCamelCase					,jax.device_count()       )

					snake_case__      :Dict         =   replicate(UpperCamelCase       )
					snake_case__      :Tuple         =   shard(UpperCamelCase       )
					snake_case__      :Optional[int]         =   shard(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   pipe(
					    prompt_ids=UpperCamelCase					,image=UpperCamelCase					,params=UpperCamelCase					,prng_seed=UpperCamelCase					,num_inference_steps=50					,jit=UpperCamelCase					,).images
					assert images.shape == (jax.device_count(), 1, 768, 512, 3)

					snake_case__      :int         =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
					snake_case__      :List[str]         =   images[0, 253:256, 253:256, -1]

					snake_case__      :Tuple         =   jnp.asarray(jax.device_get(image_slice.flatten()       )       )
					snake_case__      :List[str]         =   jnp.array(
					    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2