




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					( __lowerCamelCase     ):


     """simple docstring"""


     _A				:    Tuple									=  CONFIG_NAME




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


         '''simple docstring'''





         super().__init__()




     @staticmethod
     def   __A				(	_UpperCamelCase			: np.ndarray						)		->   PIL.Image.Image:


         '''simple docstring'''





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

         return pil_images




     @staticmethod
     def   __A				(	_UpperCamelCase			: Union[List[PIL.Image.Image], PIL.Image.Image]						)		->   np.ndarray:


         '''simple docstring'''





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

         return images




     @staticmethod
     def   __A				(	_UpperCamelCase			: np.ndarray						)		->   torch.FloatTensor:


         '''simple docstring'''





         if images.ndim == 3:
             SCREAMING_SNAKE_CASE__								=images[..., None]

         SCREAMING_SNAKE_CASE__								=torch.from_numpy(images.transpose(0				,3				,1				,2						)						)
         return images




     @staticmethod
     def   __A				(	_UpperCamelCase			: torch.FloatTensor						)		->   np.ndarray:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=images.cpu().permute(0				,2				,3				,1						).float().numpy()
         return images




     @staticmethod
     def   __A				(	_UpperCamelCase			: Optional[Any]						)		->   List[str]:


         '''simple docstring'''





         return 2.0 * images - 1.0




     @staticmethod
     def   __A				(	_UpperCamelCase			: str						)		->   Optional[int]:


         '''simple docstring'''





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




     @staticmethod
     def   __A				(	_UpperCamelCase			: PIL.Image.Image						)		->   PIL.Image.Image:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=image.convert("""RGB"""						)
         return image




     def   __A				(	self			: Optional[Any]				,_UpperCamelCase			: PIL.Image.Image				,_UpperCamelCase			: Optional[int] = None				,_UpperCamelCase			: Optional[int] = None				,)		->   PIL.Image.Image:


         '''simple docstring'''





         if height is None:
             SCREAMING_SNAKE_CASE__								=image.height
         if width is None:
             SCREAMING_SNAKE_CASE__								=image.width

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




     def   __A				(	self			: Optional[int]				,_UpperCamelCase			: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]				,_UpperCamelCase			: Optional[int] = None				,_UpperCamelCase			: Optional[int] = None				,)		->   torch.Tensor:


         '''simple docstring'''





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

         elif isinstance(image[0]				,np.ndarray						):
             SCREAMING_SNAKE_CASE__								=np.concatenate(_UpperCamelCase				,axis=0						) if image[0].ndim == 4 else np.stack(_UpperCamelCase				,axis=0						)
             SCREAMING_SNAKE_CASE__								=self.numpy_to_pt(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=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						):
             SCREAMING_SNAKE_CASE__								=torch.cat(_UpperCamelCase				,axis=0						) if image[0].ndim == 4 else torch.stack(_UpperCamelCase				,axis=0						)
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=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]
         SCREAMING_SNAKE_CASE__								=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				,)
             SCREAMING_SNAKE_CASE__								=False

         if do_normalize:
             SCREAMING_SNAKE_CASE__								=self.normalize(_UpperCamelCase						)

         return image




     def   __A				(	self			: List[Any]				,_UpperCamelCase			: torch.FloatTensor				,_UpperCamelCase			: str = "pil"				,_UpperCamelCase			: Optional[List[bool]] = None				,)		->   Optional[int]:


         '''simple docstring'''





         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"]:
             SCREAMING_SNAKE_CASE__								=(
                 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						)
             SCREAMING_SNAKE_CASE__								="""np"""

         if output_type == "latent":
             return image

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

         SCREAMING_SNAKE_CASE__								=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

         SCREAMING_SNAKE_CASE__								=self.pt_to_numpy(_UpperCamelCase						)

         if output_type == "np":
             return image

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






class       __a					( __lowerCamelCase     ):


     """simple docstring"""


     _A				:    Tuple									=  CONFIG_NAME




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


         '''simple docstring'''





         super().__init__()




     @staticmethod
     def   __A				(	_UpperCamelCase			: int						)		->   Union[str, Any]:


         '''simple docstring'''





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

         return pil_images




     @staticmethod
     def   __A				(	_UpperCamelCase			: Dict						)		->   List[str]:


         '''simple docstring'''





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




     def   __A				(	self			: Dict				,_UpperCamelCase			: Any						)		->   Dict:


         '''simple docstring'''





         if images.ndim == 3:
             SCREAMING_SNAKE_CASE__								=images[None, ...]
         SCREAMING_SNAKE_CASE__								=images[:, :, :, 3:]
         if images.shape[-1] == 6:
             SCREAMING_SNAKE_CASE__								=(images_depth * 2_5_5).round().astype("""uint8"""						)
             SCREAMING_SNAKE_CASE__								=[
                 Image.fromarray(self.rgblike_to_depthmap(_UpperCamelCase						)				,mode="""I;16"""						) for image_depth in images_depth
             ]
         elif images.shape[-1] == 4:
             SCREAMING_SNAKE_CASE__								=(images_depth * 6_5535.0).astype(np.uintaa						)
             SCREAMING_SNAKE_CASE__								=[Image.fromarray(_UpperCamelCase				,mode="""I;16"""						) for image_depth in images_depth]
         else:
             raise Exception("""Not supported"""						)

         return pil_images




     def   __A				(	self			: Optional[Any]				,_UpperCamelCase			: torch.FloatTensor				,_UpperCamelCase			: str = "pil"				,_UpperCamelCase			: Optional[List[bool]] = None				,)		->   str:


         '''simple docstring'''





         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"]:
             SCREAMING_SNAKE_CASE__								=(
                 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						)
             SCREAMING_SNAKE_CASE__								="""np"""

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

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

         SCREAMING_SNAKE_CASE__								=self.pt_to_numpy(_UpperCamelCase						)

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












import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ConvNextImageProcessor
class       __a					( unittest.TestCase     ):


     """simple docstring"""


     def __init__(	self			: List[str]				,_UpperCamelCase			: int				,_UpperCamelCase			: int=7				,_UpperCamelCase			: int=3				,_UpperCamelCase			: List[str]=1_8				,_UpperCamelCase			: Dict=3_0				,_UpperCamelCase			: Tuple=4_0_0				,_UpperCamelCase			: Optional[int]=True				,_UpperCamelCase			: int=None				,_UpperCamelCase			: str=0.875				,_UpperCamelCase			: Tuple=True				,_UpperCamelCase			: Dict=[0.5, 0.5, 0.5]				,_UpperCamelCase			: str=[0.5, 0.5, 0.5]				,)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=size if size is not None else {"""shortest_edge""": 2_0}
         SCREAMING_SNAKE_CASE__								=parent
         SCREAMING_SNAKE_CASE__								=batch_size
         SCREAMING_SNAKE_CASE__								=num_channels
         SCREAMING_SNAKE_CASE__								=image_size
         SCREAMING_SNAKE_CASE__								=min_resolution
         SCREAMING_SNAKE_CASE__								=max_resolution
         SCREAMING_SNAKE_CASE__								=do_resize
         SCREAMING_SNAKE_CASE__								=size
         SCREAMING_SNAKE_CASE__								=crop_pct
         SCREAMING_SNAKE_CASE__								=do_normalize
         SCREAMING_SNAKE_CASE__								=image_mean
         SCREAMING_SNAKE_CASE__								=image_std




     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }






@require_torch
@require_vision
class       __a					( __lowerCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    Dict									=  ConvNextImageProcessor if is_vision_available() else None




     def   __A				(	self			: List[str]						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=ConvNextImageProcessingTester(self						)




     @property
     def   __A				(	self			: str						)		->   Optional[int]:


         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()




     def   __A				(	self			: Optional[int]						)		->   Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_resize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""size"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""crop_pct"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_normalize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_mean"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_std"""						)						)




     def   __A				(	self			: Tuple						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 2_0}						)

         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict				,size=4_2						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 4_2}						)




     def   __A				(	self			: Any						)		->   List[str]:


         '''simple docstring'''





         pass




     def   __A				(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,Image.Image						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processing(image_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: str						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase				,numpify=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,np.ndarray						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processing(image_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: List[Any]						)		->   Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase				,torchify=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,torch.Tensor						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processing(image_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)






