


'''simple docstring'''




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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 					a			(				snake_case__  ):
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      )      ->    Optional[int]:
    super().__init__()
    self.register_modules(transformer=lowerCamelCase_		,      vae=lowerCamelCase_		,      scheduler=lowerCamelCase_      )

    # create a imagenet -> id dictionary for easier use
    _a					:  Union[str, Any]						=  {}
    if idalabel is not None:
       for key, value in idalabel.items():
          for label in value.split(','      ):
             _a					:  Dict						=  int(lowerCamelCase_      )
       _a					:  str						=  dict(sorted(self.labels.items()      )      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    List[int]:

    if not isinstance(lowerCamelCase_		,      lowerCamelCase_      ):
       _a					:  Optional[Any]						=  list(lowerCamelCase_      )

    for l in label:
       if l not in self.labels:
          raise ValueError(
              F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''      )

    return [self.labels[l] for l in label]




 @torch.no_grad()
 def __call__(    self		,      lowerCamelCase_		,      lowerCamelCase_ = 4.0		,      lowerCamelCase_ = None		,      lowerCamelCase_ = 5_0		,      lowerCamelCase_ = "pil"		,      lowerCamelCase_ = True		,      )      ->    Union[ImagePipelineOutput, Tuple]:
    _a					:  Optional[int]						=  len(lowerCamelCase_      )
    _a					:  List[Any]						=  self.transformer.config.sample_size
    _a					:  List[str]						=  self.transformer.config.in_channels

    _a					:  Optional[Any]						=  randn_tensor(
        shape=(batch_size, latent_channels, latent_size, latent_size)		,      generator=lowerCamelCase_		,      device=self.device		,      dtype=self.transformer.dtype		,      )
    _a					:  Union[str, Any]						=  torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

    _a					:  int						=  torch.tensor(lowerCamelCase_		,      device=self.device      ).reshape(-1      )
    _a					:  List[Any]						=  torch.tensor([1_0_0_0] * batch_size		,      device=self.device      )
    _a					:  Dict						=  torch.cat([class_labels, class_null]		,      0      ) if guidance_scale > 1 else class_labels

    # set step values
    self.scheduler.set_timesteps(lowerCamelCase_      )

    for t in self.progress_bar(self.scheduler.timesteps      ):
       if guidance_scale > 1:
          _a					:  int						=  latent_model_input[: len(lowerCamelCase_      ) // 2]
          _a					:  Tuple						=  torch.cat([half, half]		,      dim=0      )
       _a					:  Tuple						=  self.scheduler.scale_model_input(lowerCamelCase_		,      lowerCamelCase_      )

       _a					:  Any						=  t
       if not torch.is_tensor(lowerCamelCase_      ):
          # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
          # This would be a good case for the `match` statement (Python 3.10+)
          _a					:  Union[str, Any]						=  latent_model_input.device.type == 'mps'
          if isinstance(lowerCamelCase_		,      lowerCamelCase_      ):
             _a					:  Union[str, Any]						=  torch.floataa if is_mps else torch.floataa
          else:
             _a					:  str						=  torch.intaa if is_mps else torch.intaa
          _a					:  str						=  torch.tensor([timesteps]		,      dtype=lowerCamelCase_		,      device=latent_model_input.device      )
       elif len(timesteps.shape      ) == 0:
          _a					:  str						=  timesteps[None].to(latent_model_input.device      )
       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       _a					:  int						=  timesteps.expand(latent_model_input.shape[0]      )
       # predict noise model_output
       _a					:  Optional[Any]						=  self.transformer(
           lowerCamelCase_		,      timestep=lowerCamelCase_		,      class_labels=lowerCamelCase_      ).sample

       # perform guidance
       if guidance_scale > 1:
          _a		,						_a					:  str						=  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
          _a		,						_a					:  int						=  torch.split(lowerCamelCase_		,      len(lowerCamelCase_      ) // 2		,      dim=0      )

          _a					:  Optional[int]						=  uncond_eps + guidance_scale * (cond_eps - uncond_eps)
          _a					:  Union[str, Any]						=  torch.cat([half_eps, half_eps]		,      dim=0      )

          _a					:  Tuple						=  torch.cat([eps, rest]		,      dim=1      )

       # learned sigma
       if self.transformer.config.out_channels // 2 == latent_channels:
          _a		,						_a					:  str						=  torch.split(lowerCamelCase_		,      lowerCamelCase_		,      dim=1      )
       else:
          _a					:  Union[str, Any]						=  noise_pred

       # compute previous image: x_t -> x_t-1
       _a					:  int						=  self.scheduler.step(lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      ).prev_sample

    if guidance_scale > 1:
       _a		,						_a					:  Tuple						=  latent_model_input.chunk(2		,      dim=0      )
    else:
       _a					:  List[str]						=  latent_model_input

    _a					:  Any						=  1 / self.vae.config.scaling_factor * latents
    _a					:  Tuple						=  self.vae.decode(lowerCamelCase_      ).sample

    _a					:  Dict						=  (samples / 2 + 0.5).clamp(0		,      1      )

    # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
    _a					:  int						=  samples.cpu().permute(0		,      2		,      3		,      1      ).float().numpy()

    if output_type == "pil":
       _a					:  Optional[Any]						=  self.numpy_to_pil(lowerCamelCase_      )

    if not return_dict:
       return (samples,)

    return ImagePipelineOutput(images=lowerCamelCase_      )






'''simple docstring'''




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		"""ClapFeatureExtractor"""
 __lowerCAmelCase						: str							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Union[str, Any]:
    super().__init__(lowerCamelCase_		,      lowerCamelCase_      )




 def __call__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      **lowerCamelCase_      )      ->    List[Any]:
    _a					:  Any						=  kwargs.pop('sampling_rate'		,      lowerCamelCase_      )

    if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.'      )

    if text is not None:
       _a					:  Union[str, Any]						=  self.tokenizer(lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if audios is not None:
       _a					:  List[str]						=  self.feature_extractor(
           lowerCamelCase_		,      sampling_rate=lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if text is not None and audios is not None:
       _a					:  List[Any]						=  audio_features.input_features
       return encoding
    elif text is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**lowerCamelCase_      )		,      tensor_type=lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    Tuple:
    return self.tokenizer.batch_decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    List[str]:
    return self.tokenizer.decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 @property
 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Dict						=  self.tokenizer.model_input_names
    _a					:  Tuple						=  self.feature_extractor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names      )      )


