



'''simple docstring'''





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE__          =  transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def 							lowerCamelCase	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


    if isinstance(_snake_case  ,torch.Tensor							):
        return image
    elif isinstance(_snake_case  ,PIL.Image.Image							):
        lowercase__									=    [image]

    lowercase__									=    [trans(img.convert("RGB"							)							) for img in image]
    lowercase__									=    torch.stack(_snake_case							)
    return image



class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Any:
           super().__init__()

           # make sure scheduler can always be converted to DDIM
           lowercase__									=    DDIMScheduler.from_config(scheduler.config   )

           self.register_modules(unet=UpperCAmelCase_  ,scheduler=UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Any:
           if strength < 0 or strength > 1:
               raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				int:
           # get the original timestep using init_timestep
           lowercase__									=    min(int(num_inference_steps * strength   )  ,UpperCAmelCase_   )

           lowercase__									=    max(num_inference_steps - init_timestep  ,0   )
           lowercase__									=    self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=None   )	->				Any:
           if not isinstance(UpperCAmelCase_  ,(torch.Tensor, PIL.Image.Image, list)   ):
               raise ValueError(
                   F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_   )}'''   )

           lowercase__									=    image.to(device=UpperCAmelCase_  ,dtype=UpperCAmelCase_   )

           if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ) and len(UpperCAmelCase_   ) != batch_size:
               raise ValueError(
                   F'''You have passed a list of generators of length {len(UpperCAmelCase_   )}, but requested an effective batch'''
                   F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

           lowercase__									=    init_latents.shape
           lowercase__									=    randn_tensor(UpperCAmelCase_  ,generator=UpperCAmelCase_  ,device=UpperCAmelCase_  ,dtype=UpperCAmelCase_   )

           # get latents
           print("add noise to latents at timestep"  ,UpperCAmelCase_   )
           lowercase__									=    self.scheduler.add_noise(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )
           lowercase__									=    init_latents

           return latents







       @torch.no_grad()
       def __call__(					self  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = 0.8  ,UpperCAmelCase_ = 1  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = 0.0  ,UpperCAmelCase_ = 50  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = "pil"  ,UpperCAmelCase_ = True  ,)	->				Union[ImagePipelineOutput, Tuple]:
           self.check_inputs(UpperCAmelCase_   )

           # 2. Preprocess image
           lowercase__									=    preprocess(UpperCAmelCase_   )

           # 3. set timesteps
           self.scheduler.set_timesteps(UpperCAmelCase_  ,device=self.device   )
           lowercase__							,							lowercase__									=    self.get_timesteps(UpperCAmelCase_  ,UpperCAmelCase_  ,self.device   )
           lowercase__									=    timesteps[:1].repeat(UpperCAmelCase_   )

           # 4. Prepare latent variables
           lowercase__									=    self.prepare_latents(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,self.unet.dtype  ,self.device  ,UpperCAmelCase_   )
           lowercase__									=    latents

           # 5. Denoising loop
           for t in self.progress_bar(UpperCAmelCase_   ):
               # 1. predict noise model_output
               lowercase__									=    self.unet(UpperCAmelCase_  ,UpperCAmelCase_   ).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               lowercase__									=    self.scheduler.step(
                   UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,eta=UpperCAmelCase_  ,use_clipped_model_output=UpperCAmelCase_  ,generator=UpperCAmelCase_  ,).prev_sample

           lowercase__									=    (image / 2 + 0.5).clamp(0  ,1   )
           lowercase__									=    image.cpu().permute(0  ,2  ,3  ,1   ).numpy()
           if output_type == "pil":
               lowercase__									=    self.numpy_to_pil(UpperCAmelCase_   )

           if not return_dict:
               return (image, latent_timestep.item())

           return ImagePipelineOutput(images=UpperCAmelCase_   )









'''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 					snake_case			(unittest.TestCase ):



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



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

           lowercase__									=    "bird"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    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)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-openpose"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

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

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    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)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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




