

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    A_				( _a  ):


    '''simple docstring'''






    a__       							=    CONFIG_NAME


    @register_to_config
    def __init__(self       , lowercase__ = True       , lowercase__ = 8       , lowercase__ = "lanczos"       , lowercase__ = True       , lowercase__ = False       , )       -> Tuple:
        super().__init__()


    @staticmethod
    def        lowerCAmelCase_  (lowercase__					)       -> PIL.Image.Image:
        if images.ndim == 3:
            __UpperCAmelCase			   =     images[None, ...]
        __UpperCAmelCase			   =     (images * 255).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__					)       -> Optional[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       , )       -> Dict:
        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    A_				( _a  ):


    '''simple docstring'''






    a__       							=    CONFIG_NAME


    @register_to_config
    def __init__(self       , lowercase__ = True       , lowercase__ = 8       , lowercase__ = "lanczos"       , lowercase__ = True       , )       -> Any:
        super().__init__()


    @staticmethod
    def        lowerCAmelCase_  (lowercase__					)       -> Tuple:
        if images.ndim == 3:
            __UpperCAmelCase			   =     images[None, ...]
        __UpperCAmelCase			   =     (images * 255).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__					)       -> Dict:
        return image[:, :, 1] * 2**8 + image[:, :, 2]


    def        lowerCAmelCase_  (self       , lowercase__					)       -> Union[str, Any]:
        if images.ndim == 3:
            __UpperCAmelCase			   =     images[None, ...]
        __UpperCAmelCase			   =     images[:, :, :, 3:]
        if images.shape[-1] == 6:
            __UpperCAmelCase			   =     (images_depth * 255).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 * 65535.0).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[str]:
        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'''					)







def  __a    (				SCREAMING_SNAKE_CASE ) ->       float:






    '''simple docstring'''
    return -num if num < 0 else num


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_min() arg is an empty sequence''' )
    __UpperCAmelCase			   =     x[0]
    for i in x:
        if abs_val(SCREAMING_SNAKE_CASE ) < abs_val(SCREAMING_SNAKE_CASE ):
            __UpperCAmelCase			   =     i
    return j


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_max() arg is an empty sequence''' )
    __UpperCAmelCase			   =     x[0]
    for i in x:
        if abs(SCREAMING_SNAKE_CASE ) > abs(SCREAMING_SNAKE_CASE ):
            __UpperCAmelCase			   =     i
    return j


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_max_sort() arg is an empty sequence''' )
    return sorted(SCREAMING_SNAKE_CASE    ,				key=SCREAMING_SNAKE_CASE )[-1]


def  __a    (				) ->       Union[str, Any]:






    '''simple docstring'''
    assert abs_val(0 ) == 0
    assert abs_val(3_4 ) == 3_4
    assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0 ) == 1_0_0_0_0_0_0_0_0_0_0_0

    __UpperCAmelCase			   =     [-3, -1, 2, -1_1]
    assert abs_max(SCREAMING_SNAKE_CASE ) == -1_1
    assert abs_max_sort(SCREAMING_SNAKE_CASE ) == -1_1
    assert abs_min(SCREAMING_SNAKE_CASE ) == -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       test_abs_val()
       print(abs_val(-34))  # --> 34




