
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	(						A_							,				unittest.TestCase						):
      UpperCAmelCase__       :							Optional[int]     =	KandinskyVaaControlnetImgaImgPipeline
      UpperCAmelCase__       :							Any     =	["image_embeds", "negative_image_embeds", "image", "hint"]
      UpperCAmelCase__       :							Union[str, Any]     =	["image_embeds", "negative_image_embeds", "image", "hint"]
      UpperCAmelCase__       :							Union[str, Any]     =	[
          "generator",
          "height",
          "width",
          "strength",
          "guidance_scale",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      UpperCAmelCase__       :							Dict     =	False





      @property
      def 	UpperCAmelCase(self   :					List[Any]     )		->		Tuple:
          return 3_2





      @property
      def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
          return 3_2





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





      @property
      def 	UpperCAmelCase(self   :					List[str]     )		->		Any:
          return self.time_input_dim * 4





      @property
      def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Union[str, Any]:
          return 1_0_0





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

          snake_case				  =      {
              "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,
          }

          snake_case				  =      UNetaDConditionModel(**lowercase_     )
          return model





      @property
      def 	UpperCAmelCase(self   :					int     )		->		Optional[int]:
          return {
              "block_out_channels": [3_2, 3_2, 6_4, 6_4],
              "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": 1_2,
              "out_channels": 3,
              "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
              "vq_embed_dim": 4,
          }





      @property
      def 	UpperCAmelCase(self   :					int     )		->		Dict:
          torch.manual_seed(0     )
          snake_case				  =      VQModel(**self.dummy_movq_kwargs     )
          return model





      def 	UpperCAmelCase(self   :					int     )		->		Any:
          snake_case				  =      self.dummy_unet
          snake_case				  =      self.dummy_movq

          snake_case				  =      {
              "num_train_timesteps": 1_0_0_0,
              "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,
          }

          snake_case				  =      DDIMScheduler(**lowercase_     )

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

          return components





      def 	UpperCAmelCase(self   :					str       ,					_A   :					Dict       ,					_A   :					List[str]=0     )		->		List[str]:
          snake_case				  =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(lowercase_     )     ).to(lowercase_     )
          snake_case				  =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(seed + 1     )     ).to(
              lowercase_     )
          # create init_image
          snake_case				  =      floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(lowercase_     )     ).to(lowercase_     )
          snake_case				  =      image.cpu().permute(0       ,					2       ,					3       ,					1     )[0]
          snake_case				  =      Image.fromarray(np.uinta(lowercase_     )     ).convert("RGB"     ).resize((2_5_6, 2_5_6)     )
          # create hint
          snake_case				  =      floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(lowercase_     )     ).to(lowercase_     )

          if str(lowercase_     ).startswith("mps"     ):
              snake_case				  =      torch.manual_seed(lowercase_     )
          else:
              snake_case				  =      torch.Generator(device=lowercase_     ).manual_seed(lowercase_     )
          snake_case				  =      {
              "image": init_image,
              "image_embeds": image_embeds,
              "negative_image_embeds": negative_image_embeds,
              "hint": hint,
              "generator": generator,
              "height": 6_4,
              "width": 6_4,
              "num_inference_steps": 1_0,
              "guidance_scale": 7.0,
              "strength": 0.2,
              "output_type": "np",
          }
          return inputs





      def 	UpperCAmelCase(self   :					Any     )		->		str:
          snake_case				  =      "cpu"

          snake_case				  =      self.get_dummy_components()

          snake_case				  =      self.pipeline_class(**lowercase_     )
          snake_case				  =      pipe.to(lowercase_     )

          pipe.set_progress_bar_config(disable=lowercase_     )

          snake_case				  =      pipe(**self.get_dummy_inputs(lowercase_     )     )
          snake_case				  =      output.images

          snake_case				  =      pipe(
              **self.get_dummy_inputs(lowercase_     )       ,					return_dict=lowercase_       ,					)[0]

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

          assert image.shape == (1, 6_4, 6_4, 3)

          snake_case				  =      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   lowerCamelCase	(						unittest.TestCase						):





      def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		int:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()





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

          snake_case				  =      load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
          snake_case				  =      init_image.resize((5_1_2, 5_1_2)     )

          snake_case				  =      load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinskyv22/hint_image_cat.png"     )
          snake_case				  =      torch.from_numpy(np.array(lowercase_     )     ).float() / 2_5_5.0
          snake_case				  =      hint.permute(2       ,					0       ,					1     ).unsqueeze(0     )

          snake_case				  =      "A robot, 4k photo"

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

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

          pipeline.set_progress_bar_config(disable=lowercase_     )

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

          snake_case  ,							snake_case				  =      pipe_prior(
              lowercase_       ,					image=lowercase_       ,					strength=0.85       ,					generator=lowercase_       ,					negative_prompt=""       ,					).to_tuple()

          snake_case				  =      pipeline(
              image=lowercase_       ,					image_embeds=lowercase_       ,					negative_image_embeds=lowercase_       ,					hint=lowercase_       ,					generator=lowercase_       ,					num_inference_steps=1_0_0       ,					height=5_1_2       ,					width=5_1_2       ,					strength=0.5       ,					output_type="np"       ,					)

          snake_case				  =      output.images[0]

          assert image.shape == (5_1_2, 5_1_2, 3)

          assert_mean_pixel_difference(lowercase_       ,					lowercase_     )



_A						      =				0  # The first color of the flag.
_A						      =				1  # The second color of the flag.
_A						      =				2  # The third color of the flag.
_A						      =				(red, white, blue)



def 		lowercase_    (     A__	)							->							list:
    """simple docstring"""

    if not sequence:
        return []
    if len(A__	) == 1:
        return list(A__	)
    snake_case				  =      0
    snake_case				  =      len(A__	) - 1
    snake_case				  =      0
    while mid <= high:
        if sequence[mid] == colors[0]:
            snake_case  ,							snake_case				  =      sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            snake_case  ,							snake_case				  =      sequence[high], sequence[mid]
            high -= 1
        else:
            snake_case				  =      F'The elements inside the sequence must contains only {colors} values'
            raise ValueError(A__	)
    return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _A						      =				input("Enter numbers separated by commas:\n").strip()
   _A						      =				[int(item.strip()) for item in user_input.split(",")]
   print(f"{dutch_national_flag_sort(unsorted)}")

