





'''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					(      snake_case_							,		unittest.TestCase       ):
      _lowercase:      Dict											=       KandinskyVaaControlnetImgaImgPipeline
      _lowercase:      Dict											=       ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
      _lowercase:      Optional[Any]											=       ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
      _lowercase:      int											=       [
          '''generator''',
          '''height''',
          '''width''',
          '''strength''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      _lowercase:      Optional[Any]											=       False




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




      @property
      def     lowercase__   (      self      :			Optional[Any]  )    ->				Union[str, Any]:
            return 32




      @property
      def     lowercase__   (      self      :			int  )    ->				List[Any]:
            return self.time_input_dim




      @property
      def     lowercase__   (      self      :			int  )    ->				Optional[int]:
            return self.time_input_dim * 4




      @property
      def     lowercase__   (      self      :			int  )    ->				Optional[int]:
            return 1_00




      @property
      def     lowercase__   (      self      :			List[Any]  )    ->				Dict:
            torch.manual_seed(0  )

            _lowerCAmelCase												=							{
                """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,
            }

            _lowerCAmelCase												=							UNetaDConditionModel(**__snake_case  )
            return model




      @property
      def     lowercase__   (      self      :			List[Any]  )    ->				List[str]:
            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     lowercase__   (      self      :			Any  )    ->				int:
            torch.manual_seed(0  )
            _lowerCAmelCase												=							VQModel(**self.dummy_movq_kwargs  )
            return model




      def     lowercase__   (      self      :			Optional[int]  )    ->				Any:
            _lowerCAmelCase												=							self.dummy_unet
            _lowerCAmelCase												=							self.dummy_movq

            _lowerCAmelCase												=							{
                """num_train_timesteps""": 10_00,
                """beta_schedule""": """linear""",
                """beta_start""": 0.0_00_85,
                """beta_end""": 0.0_12,
                """clip_sample""": False,
                """set_alpha_to_one""": False,
                """steps_offset""": 0,
                """prediction_type""": """epsilon""",
                """thresholding""": False,
            }

            _lowerCAmelCase												=							DDIMScheduler(**__snake_case  )

            _lowerCAmelCase												=							{
                """unet""": unet,
                """scheduler""": scheduler,
                """movq""": movq,
            }

            return components




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Dict=0  )    ->				List[str]:
            _lowerCAmelCase												=							floats_tensor((1, self.text_embedder_hidden_size)      ,      rng=random.Random(__snake_case  )  ).to(__snake_case  )
            _lowerCAmelCase												=							floats_tensor((1, self.text_embedder_hidden_size)      ,      rng=random.Random(seed + 1  )  ).to(
                __snake_case  )
            # create init_image
            _lowerCAmelCase												=							floats_tensor((1, 3, 64, 64)      ,      rng=random.Random(__snake_case  )  ).to(__snake_case  )
            _lowerCAmelCase												=							image.cpu().permute(0      ,      2      ,      3      ,      1  )[0]
            _lowerCAmelCase												=							Image.fromarray(np.uinta(__snake_case  )  ).convert("""RGB"""  ).resize((2_56, 2_56)  )
            # create hint
            _lowerCAmelCase												=							floats_tensor((1, 3, 64, 64)      ,      rng=random.Random(__snake_case  )  ).to(__snake_case  )

            if str(__snake_case  ).startswith("""mps"""  ):
                  _lowerCAmelCase												=							torch.manual_seed(__snake_case  )
            else:
                  _lowerCAmelCase												=							torch.Generator(device=__snake_case  ).manual_seed(__snake_case  )
            _lowerCAmelCase												=							{
                """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     lowercase__   (      self      :			Optional[Any]  )    ->				int:
            _lowerCAmelCase												=							"""cpu"""

            _lowerCAmelCase												=							self.get_dummy_components()

            _lowerCAmelCase												=							self.pipeline_class(**__snake_case  )
            _lowerCAmelCase												=							pipe.to(__snake_case  )

            pipe.set_progress_bar_config(disable=__snake_case  )

            _lowerCAmelCase												=							pipe(**self.get_dummy_inputs(__snake_case  )  )
            _lowerCAmelCase												=							output.images

            _lowerCAmelCase												=							pipe(
                **self.get_dummy_inputs(__snake_case  )      ,      return_dict=__snake_case      ,      )[0]

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

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

            _lowerCAmelCase												=							np.array(
                [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]  )
            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     lowercase__   (      self      :			Optional[int]  )    ->				List[str]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




      def     lowercase__   (      self      :			Optional[Any]  )    ->				str:
            _lowerCAmelCase												=							load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""  )

            _lowerCAmelCase												=							load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
            _lowerCAmelCase												=							init_image.resize((5_12, 5_12)  )

            _lowerCAmelCase												=							load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/kandinskyv22/hint_image_cat.png"""  )
            _lowerCAmelCase												=							torch.from_numpy(np.array(__snake_case  )  ).float() / 2_55.0
            _lowerCAmelCase												=							hint.permute(2      ,      0      ,      1  ).unsqueeze(0  )

            _lowerCAmelCase												=							"""A robot, 4k photo"""

            _lowerCAmelCase												=							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-2-prior"""      ,      torch_dtype=torch.floataa  )
            pipe_prior.to(__snake_case  )

            _lowerCAmelCase												=							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-2-controlnet-depth"""      ,      torch_dtype=torch.floataa  )
            _lowerCAmelCase												=							pipeline.to(__snake_case  )

            pipeline.set_progress_bar_config(disable=__snake_case  )

            _lowerCAmelCase												=							torch.Generator(device="""cpu"""  ).manual_seed(0  )

            _lowerCAmelCase					,							_lowerCAmelCase												=							pipe_prior(
                __snake_case      ,      image=__snake_case      ,      strength=0.85      ,      generator=__snake_case      ,      negative_prompt=""""""      ,      ).to_tuple()

            _lowerCAmelCase												=							pipeline(
                image=__snake_case      ,      image_embeds=__snake_case      ,      negative_image_embeds=__snake_case      ,      hint=__snake_case      ,      generator=__snake_case      ,      num_inference_steps=1_00      ,      height=5_12      ,      width=5_12      ,      strength=0.5      ,      output_type="""np"""      ,      )

            _lowerCAmelCase												=							output.images[0]

            assert image.shape == (5_12, 5_12, 3)

            assert_mean_pixel_difference(__snake_case      ,      __snake_case  )













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase = 4_00_00_00  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      _lowerCAmelCase					,							_lowerCAmelCase												=							0, 1
      while b <= n:
            if b % 2 == 0:
                  even_fibs.append(lowerCAmelCase  )
            _lowerCAmelCase					,							_lowerCAmelCase												=							b, a + b
      return sum(lowerCAmelCase  )


if __name__ == "__main__":
      print(F"""{solution() = }""")






