





"""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 					_a		( __lowercase							):
				UpperCamelCase								=       CONFIG_NAME






				@register_to_config
				def __init__(					self						:       Optional[Any],					lowerCAmelCase__						:       Tuple = True,					lowerCAmelCase__						:       List[Any] = 8,					lowerCAmelCase__						:       Any = "lanczos",					lowerCAmelCase__						:       Any = True,					lowerCAmelCase__						:       Dict = False,					)      -> int:




										'''simple docstring'''



										super().__init__()






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       List[Any]				)      -> PIL.Image.Image:




										'''simple docstring'''



										if images.ndim == 3:
																_UpperCamelCase     :   Optional[int]      =       images[None, ...]
										_UpperCamelCase     :   List[str]      =       (images * 2_5_5).round().astype('''uint8'''				)
										if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																_UpperCamelCase     :   List[Any]      =       [Image.fromarray(image.squeeze(),					mode='''L'''				) for image in images]
										else:
																_UpperCamelCase     :   Optional[int]      =       [Image.fromarray(A_				) for image in images]

										return pil_images






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       Union[str, Any]				)      -> np.ndarray:




										'''simple docstring'''



										if not isinstance(A_,					A_				):
																_UpperCamelCase     :   Optional[Any]      =       [images]
										_UpperCamelCase     :   Dict      =       [np.array(A_				).astype(np.floataa				) / 255.0 for image in images]
										_UpperCamelCase     :   str      =       np.stack(A_,					axis=0				)

										return images






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       str				)      -> torch.FloatTensor:




										'''simple docstring'''



										if images.ndim == 3:
																_UpperCamelCase     :   Any      =       images[..., None]

										_UpperCamelCase     :   Optional[int]      =       torch.from_numpy(images.transpose(0,					3,					1,					2				)				)
										return images






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       Optional[int]				)      -> np.ndarray:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       images.cpu().permute(0,					2,					3,					1				).float().numpy()
										return images






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       Any				)      -> Dict:




										'''simple docstring'''



										return 2.0 * images - 1.0






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       Optional[int]				)      -> Optional[int]:




										'''simple docstring'''



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






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       Optional[int]				)      -> PIL.Image.Image:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       image.convert('''RGB'''				)
										return image






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       str = None,					lowerCAmelCase__						:       Tuple = None,					)      -> PIL.Image.Image:




										'''simple docstring'''



										if height is None:
																_UpperCamelCase     :   Tuple      =       image.height
										if width is None:
																_UpperCamelCase     :   Optional[int]      =       image.width

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






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Tuple = None,					lowerCAmelCase__						:       str = None,					)      -> torch.Tensor:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       (PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(A_,					A_				):
																_UpperCamelCase     :   List[Any]      =       [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:
																						_UpperCamelCase     :   Any      =       [self.convert_to_rgb(A_				) for i in image]
																if self.config.do_resize:
																						_UpperCamelCase     :   Tuple      =       [self.resize(A_,					A_,					A_				) for i in image]
																_UpperCamelCase     :   List[str]      =       self.pil_to_numpy(A_				)  # to np
																_UpperCamelCase     :   Union[str, Any]      =       self.numpy_to_pt(A_				)  # to pt

										elif isinstance(image[0],					np.ndarray				):
																_UpperCamelCase     :   Optional[int]      =       np.concatenate(A_,					axis=0				) if image[0].ndim == 4 else np.stack(A_,					axis=0				)
																_UpperCamelCase     :   str      =       self.numpy_to_pt(A_				)
																_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       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     :   Dict      =       torch.cat(A_,					axis=0				) if image[0].ndim == 4 else torch.stack(A_,					axis=0				)
																_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   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]
										_UpperCamelCase     :   List[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_,					)
																_UpperCamelCase     :   Union[str, Any]      =       False

										if do_normalize:
																_UpperCamelCase     :   Optional[Any]      =       self.normalize(A_				)

										return image






				def 							snake_case      (					self						:       List[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       str = "pil",					lowerCAmelCase__						:       Optional[Any] = None,					)      -> List[str]:




										'''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"]:
																_UpperCamelCase     :   Optional[int]      =       (
																    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_				)
																_UpperCamelCase     :   int      =       '''np'''

										if output_type == "latent":
																return image

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

										_UpperCamelCase     :   int      =       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     :   str      =       self.pt_to_numpy(A_				)

										if output_type == "np":
																return image

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




class 					_a		( __lowercase							):
				UpperCamelCase								=       CONFIG_NAME






				@register_to_config
				def __init__(					self						:       List[Any],					lowerCAmelCase__						:       Optional[int] = True,					lowerCAmelCase__						:       str = 8,					lowerCAmelCase__						:       Optional[Any] = "lanczos",					lowerCAmelCase__						:       str = True,					)      -> str:




										'''simple docstring'''



										super().__init__()






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       List[Any]				)      -> Any:




										'''simple docstring'''



										if images.ndim == 3:
																_UpperCamelCase     :   Union[str, Any]      =       images[None, ...]
										_UpperCamelCase     :   Dict      =       (images * 2_5_5).round().astype('''uint8'''				)
										if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																_UpperCamelCase     :   Tuple      =       [Image.fromarray(image.squeeze(),					mode='''L'''				) for image in images]
										else:
																_UpperCamelCase     :   Any      =       [Image.fromarray(image[:, :, :3]				) for image in images]

										return pil_images






				@staticmethod
				def 							snake_case      (					lowerCAmelCase__						:       str				)      -> List[str]:




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       List[str]				)      -> Optional[int]:




										'''simple docstring'''



										if images.ndim == 3:
																_UpperCamelCase     :   List[Any]      =       images[None, ...]
										_UpperCamelCase     :   Dict      =       images[:, :, :, 3:]
										if images.shape[-1] == 6:
																_UpperCamelCase     :   Optional[int]      =       (images_depth * 2_5_5).round().astype('''uint8'''				)
																_UpperCamelCase     :   Optional[Any]      =       [
																    Image.fromarray(self.rgblike_to_depthmap(A_				),					mode='''I;16'''				) for image_depth in images_depth
																]
										elif images.shape[-1] == 4:
																_UpperCamelCase     :   List[Any]      =       (images_depth * 65_535.0).astype(np.uintaa				)
																_UpperCamelCase     :   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						:       Optional[int],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Union[str, Any] = "pil",					lowerCAmelCase__						:       List[Any] = None,					)      -> str:




										'''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"]:
																_UpperCamelCase     :   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_				)
																_UpperCamelCase     :   Dict      =       '''np'''

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

										_UpperCamelCase     :   Union[str, Any]      =       torch.stack(
										    [self.denormalize(image[i]				) if do_denormalize[i] else image[i] for i in range(image.shape[0]				)]				)

										_UpperCamelCase     :   int      =       self.pt_to_numpy(A_				)

										if output_type == "np":
																if image.shape[-1] == 6:
																						_UpperCamelCase     :   Dict      =       np.stack([self.rgblike_to_depthmap(im[:, :, 3:]				) for im in image],					axis=0				)
																else:
																						_UpperCamelCase     :   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"""				)




# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case__						      =			abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def    __magic_name__(	__UpperCAmelCase		)    ->     int:



      '''simple docstring'''

      from transformers.testing_utils import pytest_addoption_shared

      pytest_addoption_shared(__UpperCAmelCase		)




def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      from transformers.testing_utils import pytest_terminal_summary_main

      _lowerCamelCase           =					terminalreporter.config.getoption('''--make-reports'''		)
      if make_reports:
            pytest_terminal_summary_main(__UpperCAmelCase			,    id=__UpperCAmelCase		)