



'''simple docstring'''





from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Dict     =       ["vqvae"]



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,)	->				Dict:
           super().__init__()
           self.register_modules(unet=UpperCAmelCase_  ,scheduler=UpperCAmelCase_  ,mel=UpperCAmelCase_  ,vqvae=UpperCAmelCase_   )



       def 				_a   (					self   )	->				int:
           return 50 if isinstance(self.scheduler  ,UpperCAmelCase_   ) else 1_000



       @torch.no_grad()
       def __call__(					self  ,UpperCAmelCase_ = 1  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = 0  ,UpperCAmelCase_ = 0  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = 0  ,UpperCAmelCase_ = 0  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = 0  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_=True  ,)	->				Union[
           Union[AudioPipelineOutput, ImagePipelineOutput],
           Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
       ]:
           lowercase__									=    steps or self.get_default_steps()
           self.scheduler.set_timesteps(UpperCAmelCase_   )
           lowercase__									=    step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size   ) == int:
               lowercase__									=    (self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
               lowercase__									=    randn_tensor(
                   (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size[0],
                       self.unet.config.sample_size[1],
                   )  ,generator=UpperCAmelCase_  ,device=self.device  ,)
           lowercase__									=    noise
           lowercase__									=    None

           if audio_file is not None or raw_audio is not None:
               self.mel.load_audio(UpperCAmelCase_  ,UpperCAmelCase_   )
               lowercase__									=    self.mel.audio_slice_to_image(UpperCAmelCase_   )
               lowercase__									=    np.frombuffer(input_image.tobytes()  ,dtype="uint8"   ).reshape(
                   (input_image.height, input_image.width)   )
               lowercase__									=    (input_image / 255) * 2 - 1
               lowercase__									=    torch.tensor(input_image[np.newaxis, :, :]  ,dtype=torch.float   ).to(self.device   )

               if self.vqvae is not None:
                   lowercase__									=    self.vqvae.encode(torch.unsqueeze(UpperCAmelCase_  ,0   )   ).latent_dist.sample(
                       generator=UpperCAmelCase_   )[0]
                   lowercase__									=    self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                   lowercase__									=    self.scheduler.add_noise(UpperCAmelCase_  ,UpperCAmelCase_  ,self.scheduler.timesteps[start_step - 1]   )

               lowercase__									=    (
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               lowercase__									=    int(mask_start_secs * pixels_per_second   )
               lowercase__									=    int(mask_end_secs * pixels_per_second   )
               lowercase__									=    self.scheduler.add_noise(UpperCAmelCase_  ,UpperCAmelCase_  ,torch.tensor(self.scheduler.timesteps[start_step:]   )   )

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]   )   ):
               if isinstance(self.unet  ,UpperCAmelCase_   ):
                   lowercase__									=    self.unet(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )["sample"]
               else:
                   lowercase__									=    self.unet(UpperCAmelCase_  ,UpperCAmelCase_   )["sample"]

               if isinstance(self.scheduler  ,UpperCAmelCase_   ):
                   lowercase__									=    self.scheduler.step(
                       model_output=UpperCAmelCase_  ,timestep=UpperCAmelCase_  ,sample=UpperCAmelCase_  ,eta=UpperCAmelCase_  ,generator=UpperCAmelCase_  ,)["prev_sample"]
               else:
                   lowercase__									=    self.scheduler.step(
                       model_output=UpperCAmelCase_  ,timestep=UpperCAmelCase_  ,sample=UpperCAmelCase_  ,generator=UpperCAmelCase_  ,)["prev_sample"]

               if mask is not None:
                   if mask_start > 0:
                       lowercase__									=    mask[:, step, :, :mask_start]
                   if mask_end > 0:
                       lowercase__									=    mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
               # 0.18215 was scaling factor used in training to ensure unit variance
               lowercase__									=    1 / self.vqvae.config.scaling_factor * images
               lowercase__									=    self.vqvae.decode(UpperCAmelCase_   )["sample"]

           lowercase__									=    (images / 2 + 0.5).clamp(0  ,1   )
           lowercase__									=    images.cpu().permute(0  ,2  ,3  ,1   ).numpy()
           lowercase__									=    (images * 255).round().astype("uint8"   )
           lowercase__									=    list(
               (Image.fromarray(_[:, :, 0]   ) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(UpperCAmelCase_  ,mode="RGB"   ).convert("L"   ) for _ in images)   )

           lowercase__									=    [self.mel.image_to_audio(UpperCAmelCase_   ) for _ in images]
           if not return_dict:
               return images, (self.mel.get_sample_rate(), audios)

           return BaseOutput(**AudioPipelineOutput(np.array(UpperCAmelCase_   )[:, np.newaxis, :]   )  ,**ImagePipelineOutput(UpperCAmelCase_   )   )



       @torch.no_grad()
       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = 50   )	->				np.ndarray:
           assert isinstance(self.scheduler  ,UpperCAmelCase_   )
           self.scheduler.set_timesteps(UpperCAmelCase_   )
           lowercase__									=    np.array(
               [np.frombuffer(image.tobytes()  ,dtype="uint8"   ).reshape((1, image.height, image.width)   ) for image in images]   )
           lowercase__									=    (sample / 255) * 2 - 1
           lowercase__									=    torch.Tensor(UpperCAmelCase_   ).to(self.device   )

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps  ,(0,)   )   ):
               lowercase__									=    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
               lowercase__									=    self.scheduler.alphas_cumprod[t]
               lowercase__									=    (
                   self.scheduler.alphas_cumprod[prev_timestep]
                   if prev_timestep >= 0
                   else self.scheduler.final_alpha_cumprod
               )
               lowercase__									=    1 - alpha_prod_t
               lowercase__									=    self.unet(UpperCAmelCase_  ,UpperCAmelCase_   )["sample"]
               lowercase__									=    (1 - alpha_prod_t_prev) ** 0.5 * model_output
               lowercase__									=    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
               lowercase__									=    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample







       @staticmethod
       def 				_a   (					UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				torch.Tensor:
           lowercase__									=    acos(torch.dot(torch.flatten(UpperCAmelCase_   )  ,torch.flatten(UpperCAmelCase_   )   ) / torch.norm(UpperCAmelCase_   ) / torch.norm(UpperCAmelCase_   )   )
           return sin((1 - alpha) * theta   ) * xa / sin(UpperCAmelCase_   ) + sin(alpha * theta   ) * xa / sin(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




