
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	(		SCREAMING_SNAKE_CASE__				):
   A__      :	str		  =  CONFIG_NAME



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


          '''simple docstring'''


          super().__init__()



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      List[Any]				)	->			PIL.Image.Image:


          '''simple docstring'''


          if images.ndim == 3:
                 snake_case :     Any           =   images[None, ...]
          snake_case :     Optional[int]           =   (images * 2_55).round().astype("uint8"				)
          if images.shape[-1] == 1:
                 # special case for grayscale (single channel) images
                 snake_case :     List[Any]           =   [Image.fromarray(image.squeeze()					,		mode="L"				) for image in images]
          else:
                 snake_case :     Any           =   [Image.fromarray(snake_case__				) for image in images]

          return pil_images



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Dict				)	->			np.ndarray:


          '''simple docstring'''


          if not isinstance(snake_case__					,		snake_case__				):
                 snake_case :     Optional[int]           =   [images]
          snake_case :     Dict           =   [np.array(snake_case__				).astype(np.floataa				) / 2_55.0 for image in images]
          snake_case :     Dict           =   np.stack(snake_case__					,		axis=0				)

          return images



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      int				)	->			torch.FloatTensor:


          '''simple docstring'''


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

          snake_case :     str           =   torch.from_numpy(images.transpose(0					,		3					,		1					,		2				)				)
          return images



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Dict				)	->			np.ndarray:


          '''simple docstring'''


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



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Optional[int]				)	->			str:


          '''simple docstring'''


          return 2.0 * images - 1.0



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Tuple				)	->			Tuple:


          '''simple docstring'''


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



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Union[str, Any]				)	->			PIL.Image.Image:


          '''simple docstring'''


          snake_case :     str           =   image.convert("RGB"				)
          return image



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]					,		snake_case__	:      List[str]					,		snake_case__	:      Optional[int] = None					,		snake_case__	:      Dict = None					,		)	->			PIL.Image.Image:


          '''simple docstring'''


          if height is None:
                 snake_case :     Any           =   image.height
          if width is None:
                 snake_case :     str           =   image.width

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



   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      int					,		snake_case__	:      Dict = None					,		snake_case__	:      List[Any] = None					,		)	->			torch.Tensor:


          '''simple docstring'''


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

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

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

          if do_normalize:
                 snake_case :     Union[str, Any]           =   self.normalize(snake_case__				)

          return image



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]					,		snake_case__	:      List[Any]					,		snake_case__	:      str = "pil"					,		snake_case__	:      int = None					,		)	->			Any:


          '''simple docstring'''


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

          if output_type == "latent":
                 return image

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

          snake_case :     List[Any]           =   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

          snake_case :     Optional[Any]           =   self.pt_to_numpy(snake_case__				)

          if output_type == "np":
                 return image

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






class 						UpperCAmelCase	(		SCREAMING_SNAKE_CASE__				):
   A__      :	Optional[int]		  =  CONFIG_NAME



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


          '''simple docstring'''


          super().__init__()



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Union[str, Any]				)	->			Tuple:


          '''simple docstring'''


          if images.ndim == 3:
                 snake_case :     Any           =   images[None, ...]
          snake_case :     List[Any]           =   (images * 2_55).round().astype("uint8"				)
          if images.shape[-1] == 1:
                 # special case for grayscale (single channel) images
                 snake_case :     Dict           =   [Image.fromarray(image.squeeze()					,		mode="L"				) for image in images]
          else:
                 snake_case :     Any           =   [Image.fromarray(image[:, :, :3]				) for image in images]

          return pil_images



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Optional[Any]				)	->			Optional[Any]:


          '''simple docstring'''


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



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple					,		snake_case__	:      List[Any]				)	->			Optional[int]:


          '''simple docstring'''


          if images.ndim == 3:
                 snake_case :     Dict           =   images[None, ...]
          snake_case :     Any           =   images[:, :, :, 3:]
          if images.shape[-1] == 6:
                 snake_case :     str           =   (images_depth * 2_55).round().astype("uint8"				)
                 snake_case :     Tuple           =   [
                     Image.fromarray(self.rgblike_to_depthmap(snake_case__				)					,		mode="I;16"				) for image_depth in images_depth
                 ]
          elif images.shape[-1] == 4:
                 snake_case :     Optional[int]           =   (images_depth * 6_55_35.0).astype(np.uintaa				)
                 snake_case :     Optional[Any]           =   [Image.fromarray(snake_case__					,		mode="I;16"				) for image_depth in images_depth]
          else:
                 raise Exception("Not supported"				)

          return pil_images



   def        _SCREAMING_SNAKE_CASE     (self	:      int					,		snake_case__	:      Tuple					,		snake_case__	:      List[str] = "pil"					,		snake_case__	:      int = None					,		)	->			Union[str, Any]:


          '''simple docstring'''


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

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

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

          snake_case :     List[str]           =   self.pt_to_numpy(snake_case__				)

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

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







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils






class 						UpperCAmelCase	(		unittest.TestCase				):



   @patch("diffusers.utils.hub_utils.get_full_repo_name"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      Mock				)	->			None:


          '''simple docstring'''


          snake_case :     str           =   "full_repo_name"
          with TemporaryDirectory() as tmpdir:
                 # Dummy args values
                 snake_case :     List[str]           =   Mock()
                 snake_case :     List[str]           =   tmpdir
                 snake_case :     Optional[int]           =   0
                 snake_case :     int           =   "hub_token"
                 snake_case :     Union[str, Any]           =   "dataset_name"
                 snake_case :     Tuple           =   0.01
                 snake_case :     int           =   10_00_00
                 snake_case :     Optional[Any]           =   1_00_00
                 snake_case :     Optional[Any]           =   0.01
                 snake_case :     Dict           =   0.02
                 snake_case :     List[Any]           =   0.03
                 snake_case :     str           =   0.0005
                 snake_case :     List[str]           =   0.000001
                 snake_case :     Tuple           =   1
                 snake_case :     Optional[int]           =   10
                 snake_case :     List[str]           =   0.001
                 snake_case :     List[Any]           =   0.1
                 snake_case :     List[str]           =   0.2
                 snake_case :     Tuple           =   True

                 # Model card mush be rendered and saved
                 diffusers.utils.hub_utils.create_model_card(snake_case__					,		model_name="model_name"				)
                 self.assertTrue((Path(snake_case__				) / "README.md").is_file()				)





