


'''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        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=KandinskyVaaControlnetImgaImgPipeline
       __UpperCAmelCase					=['image_embeds', 'negative_image_embeds', 'image', 'hint']
       __UpperCAmelCase					=['image_embeds', 'negative_image_embeds', 'image', 'hint']
       __UpperCAmelCase					=[
           'generator',
           'height',
           'width',
           'strength',
           'guidance_scale',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       __UpperCAmelCase					=False






       @property
       def   UpperCamelCase						( self   )->      Any:
            return 32






       @property
       def   UpperCamelCase						( self   )->      Optional[Any]:
            return 32






       @property
       def   UpperCamelCase						( self   )->      Optional[int]:
            return self.time_input_dim






       @property
       def   UpperCamelCase						( self   )->      Tuple:
            return self.time_input_dim * 4






       @property
       def   UpperCamelCase						( self   )->      Optional[Any]:
            return 100






       @property
       def   UpperCamelCase						( self   )->      str:
            torch.manual_seed(0   )

            _A				=							{
                '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,
            }

            _A				=							UNetaDConditionModel(**_UpperCamelCase   )
            return model






       @property
       def   UpperCamelCase						( self   )->      int:
            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   )->      str:
            torch.manual_seed(0   )
            _A				=							VQModel(**self.dummy_movq_kwargs   )
            return model






       def   UpperCamelCase						( self   )->      Tuple:
            _A				=							self.dummy_unet
            _A				=							self.dummy_movq

            _A				=							{
                '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,
            }

            _A				=							DDIMScheduler(**_UpperCamelCase   )

            _A				=							{
                'unet': unet,
                'scheduler': scheduler,
                'movq': movq,
            }

            return components






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=0   )->      List[str]:
            _A				=							floats_tensor((1, self.text_embedder_hidden_size)			,     rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
            _A				=							floats_tensor((1, self.text_embedder_hidden_size)			,     rng=random.Random(seed + 1   )   ).to(
                _UpperCamelCase   )
            # create init_image
            _A				=							floats_tensor((1, 3, 64, 64)			,     rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
            _A				=							image.cpu().permute(0			,     2			,     3			,     1   )[0]
            _A				=							Image.fromarray(np.uinta(_UpperCamelCase   )   ).convert('RGB'   ).resize((256, 256)   )
            # create hint
            _A				=							floats_tensor((1, 3, 64, 64)			,     rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )

            if str(_UpperCamelCase   ).startswith('mps'   ):
                 _A				=							torch.manual_seed(_UpperCamelCase   )
            else:
                 _A				=							torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
            _A				=							{
                '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:
            _A				=							'cpu'

            _A				=							self.get_dummy_components()

            _A				=							self.pipeline_class(**_UpperCamelCase   )
            _A				=							pipe.to(_UpperCamelCase   )

            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							pipe(**self.get_dummy_inputs(_UpperCamelCase   )   )
            _A				=							output.images

            _A				=							pipe(
                **self.get_dummy_inputs(_UpperCamelCase   )			,     return_dict=_UpperCamelCase			,     )[0]

            _A				=							image[0, -3:, -3:, -1]
            _A				=							image_from_tuple[0, -3:, -3:, -1]

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

            _A				=							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        lowerCAmelCase_  (     unittest.TestCase	):






       def   UpperCamelCase						( self   )->      List[str]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'   )

            _A				=							load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
            _A				=							init_image.resize((512, 512)   )

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

            _A				=							'A robot, 4k photo'

            _A				=							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-2-prior'			,     torch_dtype=torch.floataa   )
            pipe_prior.to(_UpperCamelCase   )

            _A				=							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-2-controlnet-depth'			,     torch_dtype=torch.floataa   )
            _A				=							pipeline.to(_UpperCamelCase   )

            pipeline.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							torch.Generator(device='cpu'   ).manual_seed(0   )

            _A		,				_A				=							pipe_prior(
                _UpperCamelCase			,     image=_UpperCamelCase			,     strength=0.85			,     generator=_UpperCamelCase			,     negative_prompt=''			,     ).to_tuple()

            _A				=							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'			,     )

            _A				=							output.images[0]

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

            assert_mean_pixel_difference(_UpperCamelCase			,     _UpperCamelCase   )









'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	bool:







     """simple docstring"""
     if num < 0:
          return False

     _A				=							num
     _A				=							0
     while num > 0:
          _A				=							rev_num * 1_0 + (num % 1_0)
          num //= 1_0

     return num_copy == rev_num


if __name__ == "__main__":
    import doctest

    doctest.testmod()





