

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   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Tuple										=    KandinskyVaaControlnetImgaImgPipeline
      UpperCamelCase_      :		Optional[Any]										=    ["image_embeds", "negative_image_embeds", "image", "hint"]
      UpperCamelCase_      :		List[str]										=    ["image_embeds", "negative_image_embeds", "image", "hint"]
      UpperCamelCase_      :		Optional[int]										=    [
          "generator",
          "height",
          "width",
          "strength",
          "guidance_scale",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      UpperCamelCase_      :		Any										=    False



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



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



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



      @property
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Union[str, Any]:
        return self.time_input_dim * 4



      @property
      def 						UpperCamelCase_		(      self				:    str		)   ->					List[Any]:
        return 100



      @property
      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					str:
        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(**A__		)
        return model



      @property
      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Any:
        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				:    Dict		)   ->					Union[str, Any]:
        torch.manual_seed(0		)
        _snake_case						=							VQModel(**self.dummy_movq_kwargs		)
        return model



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Union[str, Any]:
        _snake_case						=							self.dummy_unet
        _snake_case						=							self.dummy_movq

        _snake_case						=							{
            '''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,
        }

        _snake_case						=							DDIMScheduler(**A__		)

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

        return components



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

        if str(A__		).startswith('''mps'''		):
          _snake_case						=							torch.manual_seed(A__		)
        else:
          _snake_case						=							torch.Generator(device=A__		).manual_seed(A__		)
        _snake_case						=							{
            '''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				:    List[str]		)   ->					Dict:
        _snake_case						=							'''cpu'''

        _snake_case						=							self.get_dummy_components()

        _snake_case						=							self.pipeline_class(**A__		)
        _snake_case						=							pipe.to(A__		)

        pipe.set_progress_bar_config(disable=A__		)

        _snake_case						=							pipe(**self.get_dummy_inputs(A__		)		)
        _snake_case						=							output.images

        _snake_case						=							pipe(
            **self.get_dummy_inputs(A__		)      , return_dict=A__      , )[0]

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

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

        _snake_case						=							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   lowercase_   (  unittest.TestCase					):



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







      def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[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((512, 512)		)

        _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(A__		)		).float() / 255.0
        _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(A__		)

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

        pipeline.set_progress_bar_config(disable=A__		)

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

        _snake_case,					_snake_case						=							pipe_prior(
            A__      , image=A__      , strength=0.85      , generator=A__      , negative_prompt=''''''      , ).to_tuple()

        _snake_case						=							pipeline(
            image=A__      , image_embeds=A__      , negative_image_embeds=A__      , hint=A__      , generator=A__      , num_inference_steps=100      , height=512      , width=512      , strength=0.5      , output_type='''np'''      , )

        _snake_case						=							output.images[0]

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

        assert_mean_pixel_difference(A__      , A__		)




def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  if a < 0 or b < 0:
    raise ValueError('''the value of both inputs must be positive'''					)
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]  # remove the leading "0b"
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]
  _snake_case						=							max(len(_UpperCamelCase					)			,	len(_UpperCamelCase					)					)
  return "0b" + "".join(
      str(int('''1''' in (char_a, char_b)					)					)
      for char_a, char_b in zip(a_binary.zfill(_UpperCamelCase					)			,	b_binary.zfill(_UpperCamelCase					)					)					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

