


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   ):

   '''simple docstring'''




   __A			:				Tuple							=		CONFIG_NAME






   @register_to_config
   def __init__(		self						,    __A = True						,    __A = 8						,    __A = "lanczos"						,    __A = True						,    __A = False						,    ):






     """simple docstring"""





     super().__init__()






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     if images.ndim == 3:
       lowerCamelCase			: List[Any]   =    images[None, ...]
     lowerCamelCase			: List[Any]   =    (images * 255).round().astype("uint8"			)
     if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       lowerCamelCase			: str   =    [Image.fromarray(image.squeeze()						,    mode="L"			) for image in images]
     else:
       lowerCamelCase			: Optional[Any]   =    [Image.fromarray(__A			) for image in images]

     return pil_images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     if not isinstance(__A						,    __A			):
       lowerCamelCase			: Any   =    [images]
     lowerCamelCase			: List[Any]   =    [np.array(__A			).astype(np.floataa			) / 255.0 for image in images]
     lowerCamelCase			: Any   =    np.stack(__A						,    axis=0			)

     return images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     if images.ndim == 3:
       lowerCamelCase			: Union[str, Any]   =    images[..., None]

     lowerCamelCase			: List[str]   =    torch.from_numpy(images.transpose(0						,    3						,    1						,    2			)			)
     return images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    images.cpu().permute(0						,    2						,    3						,    1			).float().numpy()
     return images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     return 2.0 * images - 1.0






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     return (images / 2 + 0.5).clamp(0						,    1			)






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    image.convert("RGB"			)
     return image






   def _snake_case   (		self						,    __A						,    __A = None						,    __A = None						,    ):






     """simple docstring"""





     if height is None:
       lowerCamelCase			: Dict   =    image.height
     if width is None:
       lowerCamelCase			: Union[str, Any]   =    image.width

     lowerCamelCase   ,     lowerCamelCase			: int   =    (
         x - x % self.config.vae_scale_factor for x in (width, height)
     )  # resize to integer multiple of vae_scale_factor
     lowerCamelCase			: int   =    image.resize((width, height)						,    resample=PIL_INTERPOLATION[self.config.resample]			)
     return image






   def _snake_case   (		self						,    __A						,    __A = None						,    __A = None						,    ):






     """simple docstring"""





     lowerCamelCase			: Any   =    (PIL.Image.Image, np.ndarray, torch.Tensor)
     if isinstance(__A						,    __A			):
       lowerCamelCase			: List[str]   =    [image]
     elif not (isinstance(__A						,    __A			) and all(isinstance(__A						,    __A			) for i in image			)):
       raise ValueError(
           F"""Input is in incorrect format: {[type(__A			) for i in image]}. Currently, we only support {', '.join(__A			)}"""			)

     if isinstance(image[0]						,    PIL.Image.Image			):
       if self.config.do_convert_rgb:
         lowerCamelCase			: Union[str, Any]   =    [self.convert_to_rgb(__A			) for i in image]
       if self.config.do_resize:
         lowerCamelCase			: List[Any]   =    [self.resize(__A						,    __A						,    __A			) for i in image]
       lowerCamelCase			: Tuple   =    self.pil_to_numpy(__A			)  # to np
       lowerCamelCase			: List[Any]   =    self.numpy_to_pt(__A			)  # to pt

     elif isinstance(image[0]						,    np.ndarray			):
       lowerCamelCase			: int   =    np.concatenate(__A						,    axis=0			) if image[0].ndim == 4 else np.stack(__A						,    axis=0			)
       lowerCamelCase			: Tuple   =    self.numpy_to_pt(__A			)
       lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			: Dict   =    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			):
       lowerCamelCase			: str   =    torch.cat(__A						,    axis=0			) if image[0].ndim == 4 else torch.stack(__A						,    axis=0			)
       lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			: Any   =    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]
     lowerCamelCase			: Any   =    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()}]"""						,    __A						,    )
       lowerCamelCase			: List[Any]   =    False

     if do_normalize:
       lowerCamelCase			: Dict   =    self.normalize(__A			)

     return image






   def _snake_case   (		self						,    __A						,    __A = "pil"						,    __A = None						,    ):






     """simple docstring"""





     if not isinstance(__A						,    torch.Tensor			):
       raise ValueError(
           F"""Input for postprocessing is in incorrect format: {type(__A			)}. We only support pytorch tensor"""			)
     if output_type not in ["latent", "pt", "np", "pil"]:
       lowerCamelCase			: List[str]   =    (
           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"						,    __A						,    standard_warn=__A			)
       lowerCamelCase			: Any   =    "np"

     if output_type == "latent":
       return image

     if do_denormalize is None:
       lowerCamelCase			: Optional[int]   =    [self.config.do_normalize] * image.shape[0]

     lowerCamelCase			: str   =    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

     lowerCamelCase			: int   =    self.pt_to_numpy(__A			)

     if output_type == "np":
       return image

     if output_type == "pil":
       return self.numpy_to_pil(__A			)



class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Dict							=		CONFIG_NAME






   @register_to_config
   def __init__(		self						,    __A = True						,    __A = 8						,    __A = "lanczos"						,    __A = True						,    ):






     """simple docstring"""





     super().__init__()






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     if images.ndim == 3:
       lowerCamelCase			: List[str]   =    images[None, ...]
     lowerCamelCase			: Tuple   =    (images * 255).round().astype("uint8"			)
     if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       lowerCamelCase			: List[Any]   =    [Image.fromarray(image.squeeze()						,    mode="L"			) for image in images]
     else:
       lowerCamelCase			: Any   =    [Image.fromarray(image[:, :, :3]			) for image in images]

     return pil_images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     return image[:, :, 1] * 2**8 + image[:, :, 2]






   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     if images.ndim == 3:
       lowerCamelCase			: Union[str, Any]   =    images[None, ...]
     lowerCamelCase			: int   =    images[:, :, :, 3:]
     if images.shape[-1] == 6:
       lowerCamelCase			: List[str]   =    (images_depth * 255).round().astype("uint8"			)
       lowerCamelCase			: Optional[int]   =    [
           Image.fromarray(self.rgblike_to_depthmap(__A			)						,    mode="I;16"			) for image_depth in images_depth
       ]
     elif images.shape[-1] == 4:
       lowerCamelCase			: Tuple   =    (images_depth * 65535.0).astype(np.uintaa			)
       lowerCamelCase			: int   =    [Image.fromarray(__A						,    mode="I;16"			) for image_depth in images_depth]
     else:
       raise Exception("Not supported"			)

     return pil_images






   def _snake_case   (		self						,    __A						,    __A = "pil"						,    __A = None						,    ):






     """simple docstring"""





     if not isinstance(__A						,    torch.Tensor			):
       raise ValueError(
           F"""Input for postprocessing is in incorrect format: {type(__A			)}. We only support pytorch tensor"""			)
     if output_type not in ["latent", "pt", "np", "pil"]:
       lowerCamelCase			: Optional[Any]   =    (
           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"						,    __A						,    standard_warn=__A			)
       lowerCamelCase			: str   =    "np"

     if do_denormalize is None:
       lowerCamelCase			: Optional[Any]   =    [self.config.do_normalize] * image.shape[0]

     lowerCamelCase			: Dict   =    torch.stack(
         [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

     lowerCamelCase			: int   =    self.pt_to_numpy(__A			)

     if output_type == "np":
       if image.shape[-1] == 6:
         lowerCamelCase			: Optional[Any]   =    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]						,    axis=0			)
       else:
         lowerCamelCase			: Union[str, Any]   =    image[:, :, :, 3:]
       return image[:, :, :, :3], image_depth

     if output_type == "pil":
       return self.numpy_to_pil(__A			), self.numpy_to_depth(__A			)
     else:
       raise Exception(F"""This type {output_type} is not supported"""			)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case					=					{
    '''configuration_rwkv''': ['''RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RwkvConfig''', '''RwkvOnnxConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''RWKV_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RwkvForCausalLM''',
       '''RwkvModel''',
       '''RwkvPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

   _snake_case					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



