code: 
  
  
  
  '''simple docstring'''
  
  
  
  import warnings
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from .configuration_utils import ConfigMixin, register_to_config
  from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				CONFIG_NAME
  
  
  
   @register_to_config
   def __init__(  self		,	lowercase = True		,	lowercase = 8		,	lowercase = "lanczos"		,	lowercase = True		,	lowercase = False		,	)  ->							int:
    super().__init__()
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							PIL.Image.Image:
    if images.ndim == 3:
     __UpperCamelCase				      =  images[None, ...]
    __UpperCamelCase				      =  (images * 2_5_5).round().astype("""uint8"""			)
    if images.shape[-1] == 1:
     # special case for grayscale (single channel) images
     __UpperCamelCase				      =  [Image.fromarray(image.squeeze()		,	mode="""L"""			) for image in images]
    else:
     __UpperCamelCase				      =  [Image.fromarray(lowercase			) for image in images]
  
    return pil_images
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							np.ndarray:
    if not isinstance(lowercase		,	lowercase			):
     __UpperCamelCase				      =  [images]
    __UpperCamelCase				      =  [np.array(lowercase			).astype(np.floataa			) / 255.0 for image in images]
    __UpperCamelCase				      =  np.stack(lowercase		,	axis=0			)
  
    return images
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							torch.FloatTensor:
    if images.ndim == 3:
     __UpperCamelCase				      =  images[..., None]
  
    __UpperCamelCase				      =  torch.from_numpy(images.transpose(0		,	3		,	1		,	2			)			)
    return images
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							np.ndarray:
    __UpperCamelCase				      =  images.cpu().permute(0		,	2		,	3		,	1			).float().numpy()
    return images
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							Union[str, Any]:
    return 2.0 * images - 1.0
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							Any:
    return (images / 2 + 0.5).clamp(0		,	1			)
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							PIL.Image.Image:
    __UpperCamelCase				      =  image.convert("""RGB"""			)
    return image
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase = None		,	lowercase = None		,	)  ->							PIL.Image.Image:
    if height is None:
     __UpperCamelCase				      =  image.height
    if width is None:
     __UpperCamelCase				      =  image.width
  
    __UpperCamelCase      ,		__UpperCamelCase				      =  (
        x - x % self.config.vae_scale_factor for x in (width, height)
    )  # resize to integer multiple of vae_scale_factor
    __UpperCamelCase				      =  image.resize((width, height)		,	resample=PIL_INTERPOLATION[self.config.resample]			)
    return image
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase = None		,	lowercase = None		,	)  ->							torch.Tensor:
    __UpperCamelCase				      =  (PIL.Image.Image, np.ndarray, torch.Tensor)
    if isinstance(lowercase		,	lowercase			):
     __UpperCamelCase				      =  [image]
    elif not (isinstance(lowercase		,	lowercase			) and all(isinstance(lowercase		,	lowercase			) for i in image			)):
     raise ValueError(
         f"Input is in incorrect format: {[type(lowercase			) for i in image]}. Currently, we only support {', '.join(lowercase			)}"			)
  
    if isinstance(image[0]		,	PIL.Image.Image			):
     if self.config.do_convert_rgb:
      __UpperCamelCase				      =  [self.convert_to_rgb(lowercase			) for i in image]
     if self.config.do_resize:
      __UpperCamelCase				      =  [self.resize(lowercase		,	lowercase		,	lowercase			) for i in image]
     __UpperCamelCase				      =  self.pil_to_numpy(lowercase			)  # to np
     __UpperCamelCase				      =  self.numpy_to_pt(lowercase			)  # to pt
  
    elif isinstance(image[0]		,	np.ndarray			):
     __UpperCamelCase				      =  np.concatenate(lowercase		,	axis=0			) if image[0].ndim == 4 else np.stack(lowercase		,	axis=0			)
     __UpperCamelCase				      =  self.numpy_to_pt(lowercase			)
     __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  image.shape
     if self.config.do_resize and (
         height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
     ):
      raise ValueError(
          f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
          f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"			)
  
    elif isinstance(image[0]		,	torch.Tensor			):
     __UpperCamelCase				      =  torch.cat(lowercase		,	axis=0			) if image[0].ndim == 4 else torch.stack(lowercase		,	axis=0			)
     __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  image.shape
  
     # don't need any preprocess if the image is latents
     if channel == 4:
      return image
  
     if self.config.do_resize and (
         height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
     ):
      raise ValueError(
          f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
          f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"			)
  
          # expected range [0,1], normalize to [-1,1]
    __UpperCamelCase				      =  self.config.do_normalize
    if image.min() < 0:
     warnings.warn(
         """Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] """
         f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"		,	lowercase		,	)
     __UpperCamelCase				      =  False
  
    if do_normalize:
     __UpperCamelCase				      =  self.normalize(lowercase			)
  
    return image
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase = "pil"		,	lowercase = None		,	)  ->							Optional[Any]:
    if not isinstance(lowercase		,	torch.Tensor			):
     raise ValueError(
         f"Input for postprocessing is in incorrect format: {type(lowercase			)}. We only support pytorch tensor"			)
    if output_type not in ["latent", "pt", "np", "pil"]:
     __UpperCamelCase				      =  (
         f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
         """`pil`, `np`, `pt`, `latent`"""
     )
     deprecate("""Unsupported output_type"""		,	"""1.0.0"""		,	lowercase		,	standard_warn=lowercase			)
     __UpperCamelCase				      =  """np"""
  
    if output_type == "latent":
     return image
  
    if do_denormalize is None:
     __UpperCamelCase				      =  [self.config.do_normalize] * image.shape[0]
  
    __UpperCamelCase				      =  torch.stack(
        [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)
  
    if output_type == "pt":
     return image
  
    __UpperCamelCase				      =  self.pt_to_numpy(lowercase			)
  
    if output_type == "np":
     return image
  
    if output_type == "pil":
     return self.numpy_to_pil(lowercase			)
  
  
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				CONFIG_NAME
  
  
  
   @register_to_config
   def __init__(  self		,	lowercase = True		,	lowercase = 8		,	lowercase = "lanczos"		,	lowercase = True		,	)  ->							Union[str, Any]:
    super().__init__()
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							Any:
    if images.ndim == 3:
     __UpperCamelCase				      =  images[None, ...]
    __UpperCamelCase				      =  (images * 2_5_5).round().astype("""uint8"""			)
    if images.shape[-1] == 1:
     # special case for grayscale (single channel) images
     __UpperCamelCase				      =  [Image.fromarray(image.squeeze()		,	mode="""L"""			) for image in images]
    else:
     __UpperCamelCase				      =  [Image.fromarray(image[:, :, :3]			) for image in images]
  
    return pil_images
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							Tuple:
    return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							int:
    if images.ndim == 3:
     __UpperCamelCase				      =  images[None, ...]
    __UpperCamelCase				      =  images[:, :, :, 3:]
    if images.shape[-1] == 6:
     __UpperCamelCase				      =  (images_depth * 2_5_5).round().astype("""uint8"""			)
     __UpperCamelCase				      =  [
         Image.fromarray(self.rgblike_to_depthmap(lowercase			)		,	mode="""I;16"""			) for image_depth in images_depth
     ]
    elif images.shape[-1] == 4:
     __UpperCamelCase				      =  (images_depth * 65_535.0).astype(np.uintaa			)
     __UpperCamelCase				      =  [Image.fromarray(lowercase		,	mode="""I;16"""			) for image_depth in images_depth]
    else:
     raise Exception("""Not supported"""			)
  
    return pil_images
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase = "pil"		,	lowercase = None		,	)  ->							List[Any]:
    if not isinstance(lowercase		,	torch.Tensor			):
     raise ValueError(
         f"Input for postprocessing is in incorrect format: {type(lowercase			)}. We only support pytorch tensor"			)
    if output_type not in ["latent", "pt", "np", "pil"]:
     __UpperCamelCase				      =  (
         f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
         """`pil`, `np`, `pt`, `latent`"""
     )
     deprecate("""Unsupported output_type"""		,	"""1.0.0"""		,	lowercase		,	standard_warn=lowercase			)
     __UpperCamelCase				      =  """np"""
  
    if do_denormalize is None:
     __UpperCamelCase				      =  [self.config.do_normalize] * image.shape[0]
  
    __UpperCamelCase				      =  torch.stack(
        [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)
  
    __UpperCamelCase				      =  self.pt_to_numpy(lowercase			)
  
    if output_type == "np":
     if image.shape[-1] == 6:
      __UpperCamelCase				      =  np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]		,	axis=0			)
     else:
      __UpperCamelCase				      =  image[:, :, :, 3:]
     return image[:, :, :, :3], image_depth
  
    if output_type == "pil":
     return self.numpy_to_pil(lowercase			), self.numpy_to_depth(lowercase			)
    else:
     raise Exception(f"This type {output_type} is not supported"			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  a__						:			Optional[int]										=			{
      'configuration_bark': [
          'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'BarkCoarseConfig',
          'BarkConfig',
          'BarkFineConfig',
          'BarkSemanticConfig',
      ],
      'processing_bark': ['BarkProcessor'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			Any										=			[
       'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BarkFineModel',
       'BarkSemanticModel',
       'BarkCoarseModel',
       'BarkModel',
       'BarkPreTrainedModel',
       'BarkCausalModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_bark import (
       BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BarkCoarseConfig,
       BarkConfig,
       BarkFineConfig,
       BarkSemanticConfig,
   )
   from .processing_bark import BarkProcessor
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_bark import (
        BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
        BarkCausalModel,
        BarkCoarseModel,
        BarkFineModel,
        BarkModel,
        BarkPreTrainedModel,
        BarkSemanticModel,
    )
  
  else:
   import sys
  
   a__						:			Tuple										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  