



'''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"			)










'''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__)





