"""simple docstring"""





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def        a_  (   _lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       lowercase__					,   lowercase__    :	str     					=			image.size
       lowercase__					,   lowercase__    :	Any     					=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       lowercase__    :	str     					=			image.resize((w, h)     ,				resample=PIL_INTERPOLATION['lanczos']  )
       lowercase__    :	List[Any]     					=			np.array(__snake_case  ).astype(np.floataa  ) / 2_5_5.0
       lowercase__    :	Any     					=			image[None].transpose(0     ,				3     ,				1     ,				2  )
       lowercase__    :	Dict     					=			torch.from_numpy(__snake_case  )
       return 2.0 * image - 1.0






class   UpperCAmelCase_			(					__lowercase):


      def __init__(     self	,    a	,    a	,    a	,    ) ->      int:
             super().__init__()
             self.register_modules(vqvae=_A	,    unet=_A	,    scheduler=_A							)


      @torch.no_grad()
      def __call__(     self	,    a = None	,    a = 1	,    a = 1_0_0	,    a = 0.0	,    a = None	,    a = "pil"	,    a = True	,    ) ->      Dict:
             if isinstance(_A	,    PIL.Image.Image							):
                    lowercase__    :	Tuple     					=			1
             elif isinstance(_A	,    torch.Tensor							):
                    lowercase__    :	Optional[Any]     					=			image.shape[0]
             else:
                    raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_A							)}"""							)

             if isinstance(_A	,    PIL.Image.Image							):
                    lowercase__    :	Union[str, Any]     					=			preprocess(_A							)

             lowercase__					,   lowercase__    :	List[Any]     					=			image.shape[-2:]

             # in_channels should be 6: 3 for latents, 3 for low resolution image
             lowercase__    :	Optional[int]     					=			(batch_size, self.unet.config.in_channels // 2, height, width)
             lowercase__    :	Optional[int]     					=			next(self.unet.parameters()							).dtype

             lowercase__    :	Optional[int]     					=			randn_tensor(_A	,    generator=_A	,    device=self.device	,    dtype=_A							)

             lowercase__    :	Any     					=			image.to(device=self.device	,    dtype=_A							)

             # set timesteps and move to the correct device
             self.scheduler.set_timesteps(_A	,    device=self.device							)
             lowercase__    :	str     					=			self.scheduler.timesteps

             # scale the initial noise by the standard deviation required by the scheduler
             lowercase__    :	Union[str, Any]     					=			latents * self.scheduler.init_noise_sigma

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
             # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
             # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
             # and should be between [0, 1]
             lowercase__    :	int     					=			'eta' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
             lowercase__    :	List[str]     					=			{}
             if accepts_eta:
                    lowercase__    :	Optional[int]     					=			eta

             for t in self.progress_bar(_A							):
                    # concat latents and low resolution image in the channel dimension.
                    lowercase__    :	Optional[int]     					=			torch.cat([latents, image]	,    dim=1							)
                    lowercase__    :	str     					=			self.scheduler.scale_model_input(_A	,    _A							)
                    # predict the noise residual
                    lowercase__    :	Union[str, Any]     					=			self.unet(_A	,    _A							).sample
                    # compute the previous noisy sample x_t -> x_t-1
                    lowercase__    :	Optional[int]     					=			self.scheduler.step(_A	,    _A	,    _A	,    **_A							).prev_sample

             # decode the image latents with the VQVAE
             lowercase__    :	int     					=			self.vqvae.decode(_A							).sample
             lowercase__    :	Optional[Any]     					=			torch.clamp(_A	,    -1.0	,    1.0							)
             lowercase__    :	Optional[int]     					=			image / 2 + 0.5
             lowercase__    :	List[Any]     					=			image.cpu().permute(0	,    2	,    3	,    1							).numpy()

             if output_type == "pil":
                    lowercase__    :	Any     					=			self.numpy_to_pil(_A							)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=_A							)







"""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   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


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

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

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

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[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)

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

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

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

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

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

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[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)

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

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			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

