"""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 	__SCREAMING_SNAKE_CASE     (				a__       ):


					'''simple docstring'''





					_a             =							['vqvae']



					def __init__(	self							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Dict,  )-> str:
								super().__init__()
								self.register_modules(unet=_lowerCamelCase,  scheduler=_lowerCamelCase,  mel=_lowerCamelCase,  vqvae=_lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[int]					)-> int:
								return 50 if isinstance(self.scheduler,  _lowerCamelCase					) else 1000



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

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

											if self.vqvae is not None:
														lowerCamelCase__		:		List[Any]		     =self.vqvae.encode(torch.unsqueeze(_lowerCamelCase,  0					)					).latent_dist.sample(
														    generator=_lowerCamelCase					)[0]
														lowerCamelCase__		:		List[str]		     =self.vqvae.config.scaling_factor * input_images

											if start_step > 0:
														lowerCamelCase__		:		Dict		     =self.scheduler.add_noise(_lowerCamelCase,  _lowerCamelCase,  self.scheduler.timesteps[start_step - 1]					)

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

								for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
											if isinstance(self.unet,  _lowerCamelCase					):
														lowerCamelCase__		:		Optional[Any]		     =self.unet(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase					)['''sample''']
											else:
														lowerCamelCase__		:		Optional[int]		     =self.unet(_lowerCamelCase,  _lowerCamelCase					)['''sample''']

											if isinstance(self.scheduler,  _lowerCamelCase					):
														lowerCamelCase__		:		int		     =self.scheduler.step(
														    model_output=_lowerCamelCase,  timestep=_lowerCamelCase,  sample=_lowerCamelCase,  eta=_lowerCamelCase,  generator=_lowerCamelCase,  )['''prev_sample''']
											else:
														lowerCamelCase__		:		int		     =self.scheduler.step(
														    model_output=_lowerCamelCase,  timestep=_lowerCamelCase,  sample=_lowerCamelCase,  generator=_lowerCamelCase,  )['''prev_sample''']

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

								if self.vqvae is not None:
											# 0.18215 was scaling factor used in training to ensure unit variance
											lowerCamelCase__		:		List[str]		     =1 / self.vqvae.config.scaling_factor * images
											lowerCamelCase__		:		Dict		     =self.vqvae.decode(_lowerCamelCase					)['''sample''']

								lowerCamelCase__		:		Union[str, Any]		     =(images / 2 + 0.5).clamp(0,  1					)
								lowerCamelCase__		:		int		     =images.cpu().permute(0,  2,  3,  1					).numpy()
								lowerCamelCase__		:		str		     =(images * 255).round().astype('''uint8'''					)
								lowerCamelCase__		:		Union[str, Any]		     =list(
								    (Image.fromarray(_[:, :, 0]					) for _ in images)
								    if images.shape[3] == 1
								    else (Image.fromarray(_lowerCamelCase,  mode='''RGB'''					).convert('''L'''					) for _ in images)					)

								lowerCamelCase__		:		Optional[int]		     =[self.mel.image_to_audio(_lowerCamelCase					) for _ in images]
								if not return_dict:
											return images, (self.mel.get_sample_rate(), audios)

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



					@torch.no_grad()
					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict = 50					)-> np.ndarray:
								assert isinstance(self.scheduler,  _lowerCamelCase					)
								self.scheduler.set_timesteps(_lowerCamelCase					)
								lowerCamelCase__		:		int		     =np.array(
								    [np.frombuffer(image.tobytes(),  dtype='''uint8'''					).reshape((1, image.height, image.width)					) for image in images]					)
								lowerCamelCase__		:		Union[str, Any]		     =(sample / 255) * 2 - 1
								lowerCamelCase__		:		Any		     =torch.Tensor(_lowerCamelCase					).to(self.device					)

								for t in self.progress_bar(torch.flip(self.scheduler.timesteps,  (0,)					)					):
											lowerCamelCase__		:		Tuple		     =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
											lowerCamelCase__		:		Optional[Any]		     =self.scheduler.alphas_cumprod[t]
											lowerCamelCase__		:		Optional[int]		     =(
											    self.scheduler.alphas_cumprod[prev_timestep]
											    if prev_timestep >= 0
											    else self.scheduler.final_alpha_cumprod
											)
											lowerCamelCase__		:		Union[str, Any]		     =1 - alpha_prod_t
											lowerCamelCase__		:		Union[str, Any]		     =self.unet(_lowerCamelCase,  _lowerCamelCase					)['''sample''']
											lowerCamelCase__		:		Dict		     =(1 - alpha_prod_t_prev) ** 0.5 * model_output
											lowerCamelCase__		:		Tuple		     =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
											lowerCamelCase__		:		str		     =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

								return sample




					@staticmethod
					def 		snake_case	(	lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Dict					)-> torch.Tensor:
								lowerCamelCase__		:		str		     =acos(torch.dot(torch.flatten(_lowerCamelCase					),  torch.flatten(_lowerCamelCase					)					) / torch.norm(_lowerCamelCase					) / torch.norm(_lowerCamelCase					)					)
								return sin((1 - alpha) * theta					) * xa / sin(_lowerCamelCase					) + sin(alpha * theta					) * xa / sin(_lowerCamelCase					)









"""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 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[int]		     =controlnet_params

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

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

								lowerCamelCase__		:		Optional[int]		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Dict		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		Tuple		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		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, 768, 512, 3)

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

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

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

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

								lowerCamelCase__		:		Tuple		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Optional[Any]		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		int		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		int		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		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, 768, 512, 3)

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

								lowerCamelCase__		:		int		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Any		     =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




