"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class  UpperCamelCase	(    lowercase_		,   unittest.TestCase						):
   lowercase							=						KandinskyVaaControlnetImgaImgPipeline
   lowercase							=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
   lowercase							=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
   lowercase							=						[
       'generator',
       'height',
       'width',
       'strength',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   lowercase							=						False
   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return 32
   @property
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        return 32
   @property
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        return self.time_input_dim
   @property
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        return self.time_input_dim * 4
   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return 100
   @property
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        torch.manual_seed(0    )

        lowercase_			:       int									=			{
            'in_channels': 8,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 8,
            'addition_embed_type': 'image_hint',
            'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
            'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
            'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
            'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
            'layers_per_block': 1,
            'encoder_hid_dim': self.text_embedder_hidden_size,
            'encoder_hid_dim_type': 'image_proj',
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': None,
        }

        lowercase_			:       str									=			UNetaDConditionModel(**__UpperCamelCase    )
        return model
   @property
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }
   @property
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        torch.manual_seed(0    )
        lowercase_			:       int									=			VQModel(**self.dummy_movq_kwargs    )
        return model
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			self.dummy_unet
        lowercase_			:       int									=			self.dummy_movq

        lowercase_			:       str									=			{
            'num_train_timesteps': 1000,
            'beta_schedule': 'linear',
            'beta_start': 0.0_0085,
            'beta_end': 0.012,
            'clip_sample': False,
            'set_alpha_to_one': False,
            'steps_offset': 0,
            'prediction_type': 'epsilon',
            'thresholding': False,
        }

        lowercase_			:       Optional[Any]									=			DDIMScheduler(**__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			{
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase=0    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Any									=			floats_tensor((1, self.text_embedder_hidden_size)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
        lowercase_			:       Any									=			floats_tensor((1, self.text_embedder_hidden_size)  ,rng=random.Random(seed + 1    )    ).to(
            __UpperCamelCase    )
        # create init_image
        lowercase_			:       Optional[int]									=			floats_tensor((1, 3, 64, 64)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
        lowercase_			:       Any									=			image.cpu().permute(0  ,2  ,3  ,1    )[0]
        lowercase_			:       List[Any]									=			Image.fromarray(np.uinta(__UpperCamelCase    )    ).convert('RGB'    ).resize((256, 256)    )
        # create hint
        lowercase_			:       Union[str, Any]									=			floats_tensor((1, 3, 64, 64)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )

        if str(__UpperCamelCase    ).startswith('mps'    ):
             lowercase_			:       int									=			torch.manual_seed(__UpperCamelCase    )
        else:
             lowercase_			:       Tuple									=			torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )
        lowercase_			:       str									=			{
            'image': init_image,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'hint': hint,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 10,
            'guidance_scale': 7.0,
            'strength': 0.2,
            'output_type': 'np',
        }
        return inputs







   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			'cpu'

        lowercase_			:       List[str]									=			self.get_dummy_components()

        lowercase_			:       Optional[Any]									=			self.pipeline_class(**__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			pipe.to(__UpperCamelCase    )

        pipe.set_progress_bar_config(disable=__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			pipe(**self.get_dummy_inputs(__UpperCamelCase    )    )
        lowercase_			:       Dict									=			output.images

        lowercase_			:       List[Any]									=			pipe(
            **self.get_dummy_inputs(__UpperCamelCase    )  ,return_dict=__UpperCamelCase  ,)[0]

        lowercase_			:       Optional[Any]									=			image[0, -3:, -3:, -1]
        lowercase_			:       Tuple									=			image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        lowercase_			:       int									=			np.array(
            [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]    )
        assert (
            np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







@slow
@require_torch_gpu
class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       str									=			load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'    )

        lowercase_			:       Optional[int]									=			load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
        lowercase_			:       Tuple									=			init_image.resize((512, 512)    )

        lowercase_			:       List[str]									=			load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/hint_image_cat.png'    )
        lowercase_			:       List[Any]									=			torch.from_numpy(np.array(__UpperCamelCase    )    ).float() / 255.0
        lowercase_			:       str									=			hint.permute(2  ,0  ,1    ).unsqueeze(0    )

        lowercase_			:       List[Any]									=			'A robot, 4k photo'

        lowercase_			:       Optional[int]									=			KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-prior'  ,torch_dtype=torch.floataa    )
        pipe_prior.to(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-controlnet-depth'  ,torch_dtype=torch.floataa    )
        lowercase_			:       Dict									=			pipeline.to(__UpperCamelCase    )

        pipeline.set_progress_bar_config(disable=__UpperCamelCase    )

        lowercase_			:       str									=			torch.Generator(device='cpu'    ).manual_seed(0    )

        lowercase_							,						lowercase_			:       Dict									=			pipe_prior(
            __UpperCamelCase  ,image=__UpperCamelCase  ,strength=0.85  ,generator=__UpperCamelCase  ,negative_prompt=''  ,).to_tuple()

        lowercase_			:       List[str]									=			pipeline(
            image=__UpperCamelCase  ,image_embeds=__UpperCamelCase  ,negative_image_embeds=__UpperCamelCase  ,hint=__UpperCamelCase  ,generator=__UpperCamelCase  ,num_inference_steps=100  ,height=512  ,width=512  ,strength=0.5  ,output_type='np'  ,)

        lowercase_			:       Optional[int]									=			output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(__UpperCamelCase  ,__UpperCamelCase    )


"""simple docstring"""







__SCREAMING_SNAKE_CASE								="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"



def     lowercase__( __SCREAMING_SNAKE_CASE							:  bytes   ):
     # Make sure the supplied data is a bytes-like object
     if not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Dict									=			F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[Any]									=			''.join(bin(__SCREAMING_SNAKE_CASE   )[2:].zfill(8   ) for byte in data   )

     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase_			:       Optional[int]									=			b'=' * ((6 - len(__SCREAMING_SNAKE_CASE   ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(__SCREAMING_SNAKE_CASE   ) % 6)
     else:
          lowercase_			:       List[Any]									=			b''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]  ,    2   )]
             for index in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    6   )   ).encode()
         + padding
     )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str   ):
     # Make sure encoded_data is either a string or a bytes-like object
     if not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) and not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Tuple									=			(
              'argument should be a bytes-like object or ASCII string, '
              F'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(__SCREAMING_SNAKE_CASE   )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          try:
               lowercase_			:       List[str]									=			encoded_data.decode('utf-8'   )
          except UnicodeDecodeError:
               raise ValueError('base64 encoded data should only contain ASCII characters'   )

     lowercase_			:       List[str]									=			encoded_data.count('='   )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(__SCREAMING_SNAKE_CASE   ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase_			:       Union[str, Any]									=			encoded_data[:-padding]

          lowercase_			:       List[Any]									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
     else:
          lowercase_			:       int									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )

     lowercase_			:       str									=			[
         int(binary_stream[index : index + 8]  ,    2   )
         for index in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    8   )
     ]

     return bytes(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

