'''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

     _UpperCamelCase				:		int       			=		True
     _UpperCamelCase				:		str       			=		''
except Exception as e:
     _UpperCamelCase				:		List[Any]       			=		False
     _UpperCamelCase				:		Optional[int]       			=		(
         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   _lowercase(     __UpperCAmelCase   ,__UpperCAmelCase				):
   __lowerCamelCase    =  """mel_config.json"""





   @register_to_config
   def __init__(							self:    str			,a:    int = 256			,a:    List[Any] = 256			,a:    Optional[int] = 22050			,a:    Optional[Any] = 2048			,a:    Tuple = 512			,a:    int = 80			,a:    Optional[Any] = 32			,):
    __UpperCAmelCase         =					hop_length
    __UpperCAmelCase         =					sample_rate
    __UpperCAmelCase         =					n_fft
    __UpperCAmelCase         =					top_db
    __UpperCAmelCase         =					n_iter
    self.set_resolution(lowerCamelCase__			,lowerCamelCase__	)
    __UpperCAmelCase         =					None

    if not _librosa_can_be_imported:
     raise ValueError(_import_error	)





   def 			snake_case	(							self:    str			,a:    Any			,a:    List[Any]	):
    __UpperCAmelCase         =					x_res
    __UpperCAmelCase         =					y_res
    __UpperCAmelCase         =					self.y_res
    __UpperCAmelCase         =					self.x_res * self.hop_length - 1





   def 			snake_case	(							self:    Dict			,a:    int = None			,a:    Tuple = None	):



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

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





   def 			snake_case	(							self:    int	):
    return len(self.audio	) // self.slice_size





   def 			snake_case	(							self:    Union[str, Any]			,a:    int = 0	):
    return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]





   def 			snake_case	(							self:    Optional[int]	):
    return self.sr





   def 			snake_case	(							self:    int			,a:    Dict	):
    __UpperCAmelCase         =					librosa.feature.melspectrogram(
        y=self.get_audio_slice(lowerCamelCase__	)			,sr=self.sr			,n_fft=self.n_fft			,hop_length=self.hop_length			,n_mels=self.n_mels	)
    __UpperCAmelCase         =					librosa.power_to_db(lowerCamelCase__			,ref=np.max			,top_db=self.top_db	)
    __UpperCAmelCase         =					(((log_S + self.top_db) * 255 / self.top_db).clip(0			,255	) + 0.5).astype(np.uinta	)
    __UpperCAmelCase         =					Image.fromarray(lowerCamelCase__	)
    return image





   def 			snake_case	(							self:    List[Any]			,a:    Dict	):
    __UpperCAmelCase         =					np.frombuffer(image.tobytes()			,dtype='uint8'	).reshape((image.height, image.width)	)
    __UpperCAmelCase         =					bytedata.astype('float'	) * self.top_db / 255 - self.top_db
    __UpperCAmelCase         =					librosa.db_to_power(lowerCamelCase__	)
    __UpperCAmelCase         =					librosa.feature.inverse.mel_to_audio(
        lowerCamelCase__			,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   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   def __init__(							self:    List[str]			,a:    AutoencoderKL			,a:    CLIPTextModel			,a:    CLIPTokenizer			,a:    UNetaDConditionModel			,a:    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]			,):
    super().__init__()

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





   def 			snake_case	(							self:    Union[str, Any]			,a:    Any	):
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.encode(tfms.ToTensor()(a	).unsqueeze(0	).to(self.device	) * 2 - 1	)
     __UpperCAmelCase         =					0.18215 * latent.latent_dist.sample()
    return latent





   def 			snake_case	(							self:    Any			,a:    List[str]	):
    __UpperCAmelCase         =					(1 / 0.18215) * latent
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.decode(a	).sample
    __UpperCAmelCase         =					(img / 2 + 0.5).clamp(0			,1	)
    __UpperCAmelCase         =					img.detach().cpu().permute(0			,2			,3			,1	).numpy()
    __UpperCAmelCase         =					(img * 255).round().astype('uint8'	)
    return Image.fromarray(img[0]	)





   def 			snake_case	(							self:    Any			,a:    str	):
    __UpperCAmelCase         =					self.tokenizer(
        a			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

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

    __UpperCAmelCase         =					self.tokenizer(
        ''			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

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

    return torch.cat([uncond_embedding, text_embedding]	)





   def __call__(							self:    Optional[Any]			,a:    Image.Image			,a:    str			,a:    float = 0.3			,a:    float = 0.6			,a:    float = 0.5			,a:    int = 42			,a:    int = 50			,a:    float = 7.5			,):
    __UpperCAmelCase         =					steps - int(kmin * steps	)
    __UpperCAmelCase         =					steps - int(kmax * steps	)

    __UpperCAmelCase         =					self.prep_text(a	)

    self.scheduler.set_timesteps(a	)

    __UpperCAmelCase  ,			__UpperCAmelCase         =					img.size
    __UpperCAmelCase         =					self.encode(a	)

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

    __UpperCAmelCase         =					self.scheduler.add_noise(
        a			,a			,timesteps=self.scheduler.timesteps[tmax]			,)

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

    __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,self.scheduler.timesteps[tmax]	)

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

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

    __UpperCAmelCase         =					self.scheduler.step(a			,self.scheduler.timesteps[tmax]			,a	).prev_sample

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

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

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

      __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,a	)

      with torch.no_grad():
       __UpperCAmelCase         =					self.unet(
           a			,a			,encoder_hidden_states=a			,).sample

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

      __UpperCAmelCase         =					self.scheduler.step(a			,a			,a	).prev_sample

    return self.decode(a	)



