




import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			str:




       class 		UpperCamelCase__     :

           '''simple docstring'''



           def __init__(   self					,  UpperCamelCase__					)					->				None:
                  assert isinstance(UpperCamelCase__					,  (bytes, str)					)
                  lowerCamelCase       :				Optional[Any]      			= content







           def    _lowercase					(   self					)					->				Optional[int]:
                  return json.loads(self.content					)

       def mock_response(*_SCREAMING_SNAKE_CASE			,**_SCREAMING_SNAKE_CASE   ):
              assert args[0] == AUTHENTICATED_USER_ENDPOINT
              assert "Authorization" in kwargs["headers"]
              assert kwargs["headers"]["Authorization"].startswith("token "   )
              assert "Accept" in kwargs["headers"]
              return FakeResponse(B"{\"login\":\"test\",\"id\":1}"   )

       monkeypatch.setattr(_SCREAMING_SNAKE_CASE			,"get"			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[str]      			= fetch_github_info("token"   )
       assert result["login"] == "test"
       assert result["id"] == 1












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__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Optional[Any]     					=				CONFIG_NAME







    @register_to_config
    def __init__(   self					,  UpperCamelCase__ = True					,  UpperCamelCase__ = 8					,  UpperCamelCase__ = "lanczos"					,  UpperCamelCase__ = True					,  UpperCamelCase__ = False					,  )					->				Optional[int]:
           super().__init__()







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				PIL.Image.Image:
           if images.ndim == 3:
                  lowerCamelCase       :				str      			= images[None, ...]
           lowerCamelCase       :				Any      			= (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       :				List[Any]      			= [Image.fromarray(UpperCamelCase__					) for image in images]

           return pil_images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				np.ndarray:
           if not isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                  lowerCamelCase       :				List[str]      			= [images]
           lowerCamelCase       :				Union[str, Any]      			= [np.array(UpperCamelCase__					).astype(np.floataa					) / 255.0 for image in images]
           lowerCamelCase       :				List[str]      			= np.stack(UpperCamelCase__					,  axis=0					)

           return images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				torch.FloatTensor:
           if images.ndim == 3:
                  lowerCamelCase       :				Union[str, Any]      			= images[..., None]

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







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				np.ndarray:
           lowerCamelCase       :				List[str]      			= images.cpu().permute(0					,  2					,  3					,  1					).float().numpy()
           return images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				int:
           return 2.0 * images - 1.0







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				Dict:
           return (images / 2 + 0.5).clamp(0					,  1					)







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				PIL.Image.Image:
           lowerCamelCase       :				Union[str, Any]      			= image.convert("RGB"					)
           return image







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				PIL.Image.Image:
           if height is None:
                  lowerCamelCase       :				Any      			= image.height
           if width is None:
                  lowerCamelCase       :				Union[str, Any]      			= image.width

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







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				torch.Tensor:
           lowerCamelCase       :				Union[str, Any]      			= (PIL.Image.Image, np.ndarray, torch.Tensor)
           if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                  lowerCamelCase       :				Optional[int]      			= [image]
           elif not (isinstance(UpperCamelCase__					,  UpperCamelCase__					) and all(isinstance(UpperCamelCase__					,  UpperCamelCase__					) for i in image					)):
                  raise ValueError(
                      F'''Input is in incorrect format: {[type(UpperCamelCase__					) for i in image]}. Currently, we only support {", ".join(UpperCamelCase__					)}'''					)

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

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

           if do_normalize:
                  lowerCamelCase       :				int      			= self.normalize(UpperCamelCase__					)

           return image







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = None					,  )					->				Dict:
           if not isinstance(UpperCamelCase__					,  torch.Tensor					):
                  raise ValueError(
                      F'''Input for postprocessing is in incorrect format: {type(UpperCamelCase__					)}. We only support pytorch tensor'''					)
           if output_type not in ["latent", "pt", "np", "pil"]:
                  lowerCamelCase       :				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"					,  UpperCamelCase__					,  standard_warn=UpperCamelCase__					)
                  lowerCamelCase       :				Optional[Any]      			= "np"

           if output_type == "latent":
                  return image

           if do_denormalize is None:
                  lowerCamelCase       :				List[str]      			= [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]					)]					)

           if output_type == "pt":
                  return image

           lowerCamelCase       :				Any      			= self.pt_to_numpy(UpperCamelCase__					)

           if output_type == "np":
                  return image

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




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Union[str, Any]     					=				CONFIG_NAME







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







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				int:
           if images.ndim == 3:
                  lowerCamelCase       :				Union[str, Any]      			= images[None, ...]
           lowerCamelCase       :				Any      			= (images * 255).round().astype("uint8"					)
           if images.shape[-1] == 1:
                  # special case for grayscale (single channel) images
                  lowerCamelCase       :				Any      			= [Image.fromarray(image.squeeze()					,  mode="L"					) for image in images]
           else:
                  lowerCamelCase       :				Dict      			= [Image.fromarray(image[:, :, :3]					) for image in images]

           return pil_images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				Optional[int]:
           return image[:, :, 1] * 2**8 + image[:, :, 2]







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

           return pil_images







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = None					,  )					->				Dict:
           if not isinstance(UpperCamelCase__					,  torch.Tensor					):
                  raise ValueError(
                      F'''Input for postprocessing is in incorrect format: {type(UpperCamelCase__					)}. 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"					,  UpperCamelCase__					,  standard_warn=UpperCamelCase__					)
                  lowerCamelCase       :				Tuple      			= "np"

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

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

           lowerCamelCase       :				Union[str, Any]      			= self.pt_to_numpy(UpperCamelCase__					)

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

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






