


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


__lowerCamelCase   	=  logging.get_logger(__name__)


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  Dict				     =  """vision-text-dual-encoder"""
       lowerCamelCase__:  Dict				     =  True

       def __init__( self   :  str   ,				lowerCamelCase_   :  int=5_1_2   ,				lowerCamelCase_   :  List[Any]=2.6_5_9_2   ,				**lowerCamelCase_   :  Any     ):
            super().__init__(**lowerCamelCase_     )

            if "vision_config" not in kwargs:
                 raise ValueError("""`vision_config` can not be `None`."""     )

            if "text_config" not in kwargs:
                 raise ValueError("""`text_config` can not be `None`."""     )

            a_     :  str				      =     kwargs.pop("""vision_config"""     )
            a_     :  List[str]				      =     kwargs.pop("""text_config"""     )

            a_     :  str				      =     vision_config.pop("""model_type"""     )
            a_     :  Union[str, Any]				      =     text_config.pop("""model_type"""     )

            if vision_model_type == "clip":
                 a_     :  Dict				      =     AutoConfig.for_model(lowerCamelCase_   ,				**lowerCamelCase_     ).vision_config
            elif vision_model_type == "clip_vision_model":
                 a_     :  Union[str, Any]				      =     CLIPVisionConfig(**lowerCamelCase_     )
            else:
                 a_     :  str				      =     AutoConfig.for_model(lowerCamelCase_   ,				**lowerCamelCase_     )

            a_     :  str				      =     AutoConfig.for_model(lowerCamelCase_   ,				**lowerCamelCase_     )

            a_     :  str				      =     projection_dim
            a_     :  Tuple				      =     logit_scale_init_value

       @classmethod
       def     UpperCAmelCase( cls   :  Union[str, Any]   ,				lowerCamelCase_   :  PretrainedConfig   ,				lowerCamelCase_   :  PretrainedConfig   ,				**lowerCamelCase_   :  List[Any]     ):
            return cls(vision_config=vision_config.to_dict()   ,				text_config=text_config.to_dict()   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  Dict				      =     copy.deepcopy(self.__dict__     )
            a_     :  Any				      =     self.vision_config.to_dict()
            a_     :  int				      =     self.text_config.to_dict()
            a_     :  List[Any]				      =     self.__class__.model_type
            return output






import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =1
     a__    =3
     a__    =8
     a__    =8

     a__    =torch.rand((batch_size, num_channels, height, width)			)

     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






     if isinstance(image[0],       PIL.Image.Image			):
       return np.stack([np.array(lowercase_			) for i in image],       axis=0			)
     elif isinstance(lowercase_,       torch.Tensor			):
       return image.cpu().numpy().transpose(0,       2,       3,       1			)
     return image



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =self.to_np(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(
           image_processor.preprocess(lowercase_			),       output_type=lowercase_,       )
       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)
     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)

       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)
     a__    =image_processor.numpy_to_pil(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)
       for i, o in zip(lowercase_,       lowercase_			):
         a__    =np.array(lowercase_			)
         a__    =self.to_np(lowercase_			) if output_type == '''pil''' else (self.to_np(lowercase_			) * 255).round()
         assert (
             np.abs(in_np - out_np			).max() < 1E-6
         ), F"""decoded output does not match input for output_type {output_type}"""



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






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =input_pt_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     a__    =input_np_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_ad			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_ad			).max() < 1E-6



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_list			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_list			).max() < 1E-6
