






'''simple docstring'''
import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
						import librosa  # noqa: E402

						__lowerCAmelCase							=True
						__lowerCAmelCase							=""
except Exception as e:
						__lowerCAmelCase							=False
						__lowerCAmelCase							=(
						    f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
						)


from PIL import Image  # noqa: E402


class    _snake_case   (				snake_case     ,					snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      "mel_config.json"




					@register_to_config
					def __init__(     self   , UpperCAmelCase__ = 256   , UpperCAmelCase__ = 256   , UpperCAmelCase__ = 2_2050   , UpperCAmelCase__ = 2048   , UpperCAmelCase__ = 512   , UpperCAmelCase__ = 80   , UpperCAmelCase__ = 32   , )      ->	Optional[int]:

										'''simple docstring'''





										a_									=   hop_length
										a_									=   sample_rate
										a_									=   n_fft
										a_									=   top_db
										a_									=   n_iter
										self.set_resolution(UpperCAmelCase__   , UpperCAmelCase__     )
										a_									=   None

										if not _librosa_can_be_imported:
															raise ValueError(_import_error     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Tuple:

										'''simple docstring'''





										a_									=   x_res
										a_									=   y_res
										a_									=   self.y_res
										a_									=   self.x_res * self.hop_length - 1




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__ = None   , UpperCAmelCase__ = None     )      ->	Optional[Any]:

										'''simple docstring'''





										if audio_file is not None:
															a_							,					a_									=   librosa.load(UpperCAmelCase__   , mono=UpperCAmelCase__   , sr=self.sr     )
										else:
															a_									=   raw_audio

										# Pad with silence if necessary.
										if len(self.audio     ) < self.x_res * self.hop_length:
															a_									=   np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio     ),)     )]     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:

										'''simple docstring'''





										return len(self.audio     ) // self.slice_size




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__ = 0     )      ->	np.ndarray:

										'''simple docstring'''





										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:

										'''simple docstring'''





										return self.sr




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Image.Image:

										'''simple docstring'''





										a_									=   librosa.feature.melspectrogram(
										    y=self.get_audio_slice(UpperCAmelCase__     )   , sr=self.sr   , n_fft=self.n_fft   , hop_length=self.hop_length   , n_mels=self.n_mels     )
										a_									=   librosa.power_to_db(UpperCAmelCase__   , ref=np.max   , top_db=self.top_db     )
										a_									=   (((log_S + self.top_db) * 255 / self.top_db).clip(0   , 255     ) + 0.5).astype(np.uinta     )
										a_									=   Image.fromarray(UpperCAmelCase__     )
										return image




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	np.ndarray:

										'''simple docstring'''





										a_									=   np.frombuffer(image.tobytes()   , dtype='uint8'     ).reshape((image.height, image.width)     )
										a_									=   bytedata.astype('float'     ) * self.top_db / 255 - self.top_db
										a_									=   librosa.db_to_power(UpperCAmelCase__     )
										a_									=   librosa.feature.inverse.mel_to_audio(
										    UpperCAmelCase__   , sr=self.sr   , n_fft=self.n_fft   , hop_length=self.hop_length   , n_iter=self.n_iter     )
										return audio









'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	List[str]:
										super().__init__()

										self.register_modules(vae=UpperCAmelCase__   , text_encoder=UpperCAmelCase__   , tokenizer=UpperCAmelCase__   , unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										with torch.no_grad():
															a_									=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase__     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
															a_									=   0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	List[str]:
										a_									=   (1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
															a_									=   self.vae.decode(UpperCAmelCase__     ).sample
										a_									=   (img / 2 + 0.5).clamp(0   , 1     )
										a_									=   img.detach().cpu().permute(0   , 2   , 3   , 1     ).numpy()
										a_									=   (img * 255).round().astype('uint8'     )
										return Image.fromarray(img[0]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Any:
										a_									=   self.tokenizer(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

										a_									=   self.tokenizer(
										    ''   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

										return torch.cat([uncond_embedding, text_embedding]     )




					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.3   , UpperCAmelCase__ = 0.6   , UpperCAmelCase__ = 0.5   , UpperCAmelCase__ = 42   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , )      ->	Image.Image:
										a_									=   steps - int(kmin * steps     )
										a_									=   steps - int(kmax * steps     )

										a_									=   self.prep_text(UpperCAmelCase__     )

										self.scheduler.set_timesteps(UpperCAmelCase__     )

										a_							,					a_									=   img.size
										a_									=   self.encode(UpperCAmelCase__     )

										torch.manual_seed(UpperCAmelCase__     )
										a_									=   torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)   , ).to(self.device     )

										a_									=   self.scheduler.add_noise(
										    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=self.scheduler.timesteps[tmax]   , )

										a_									=   torch.cat([latents] * 2     )

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , self.scheduler.timesteps[tmax]     )

										with torch.no_grad():
															a_									=   self.unet(
															    UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , encoder_hidden_states=UpperCAmelCase__   , ).sample

										a_							,					a_									=   pred.chunk(2     )
										a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

										a_									=   self.scheduler.step(UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , UpperCAmelCase__     ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps     )     ):
															if i > tmax:
																				if i < tmin:  # layout generation phase
																									a_									=   self.scheduler.add_noise(
																									    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )

																									a_									=   (mix_factor * latents) + (
																									    1 - mix_factor
																									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																									a_									=   torch.cat([input] * 2     )

																				else:  # content generation phase
																									a_									=   torch.cat([latents] * 2     )

																				a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

																				with torch.no_grad():
																									a_									=   self.unet(
																									    UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__   , ).sample

																				a_							,					a_									=   pred.chunk(2     )
																				a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										return self.decode(UpperCAmelCase__     )

