





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    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   A							(						__lowercase		,   unittest.TestCase    ):
      _snake_case				  =KandinskyVaaImgaImgPipeline
      _snake_case				  =['''image_embeds''', '''negative_image_embeds''', '''image''']
      _snake_case				  =[
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
      ]
      _snake_case				  =[
          '''generator''',
          '''height''',
          '''width''',
          '''strength''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      _snake_case				  =False



      @property
      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Dict:
         '''simple docstring'''

         return 32



      @property
      def 						lowerCAmelCase__     (						self:    Any							)    ->     Optional[int]:
         '''simple docstring'''

         return 32



      @property
      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     List[str]:
         '''simple docstring'''

         return self.time_input_dim



      @property
      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Dict:
         '''simple docstring'''

         return self.time_input_dim * 4



      @property
      def 						lowerCAmelCase__     (						self:    int							)    ->     str:
         '''simple docstring'''

         return 100



      @property
      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Union[str, Any]:
         '''simple docstring'''

         torch.manual_seed(0							)

         UpperCAmelCase_           ={
             "in_channels": 4,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "image",
             "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,
         }

         UpperCAmelCase_           =UNetaDConditionModel(**_lowerCAmelCase							)
         return model



      @property
      def 						lowerCAmelCase__     (						self:    Any							)    ->     Tuple:
         '''simple docstring'''

         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["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",
             ],
             "vq_embed_dim": 4,
         }



      @property
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Any:
         '''simple docstring'''

         torch.manual_seed(0							)
         UpperCAmelCase_           =VQModel(**self.dummy_movq_kwargs							)
         return model



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.dummy_unet
         UpperCAmelCase_           =self.dummy_movq

         UpperCAmelCase_           ={
             "num_train_timesteps": 1000,
             "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,
         }

         UpperCAmelCase_           =DDIMScheduler(**_lowerCAmelCase							)

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

         return components



      def 						lowerCAmelCase__     (						self:    int		,	_lowerCAmelCase:    Any		,	_lowerCAmelCase:    Optional[Any]=0							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(_lowerCAmelCase							)							).to(_lowerCAmelCase							)
         UpperCAmelCase_           =floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(seed + 1							)							).to(
             _lowerCAmelCase							)
         # create init_image
         UpperCAmelCase_           =floats_tensor((1, 3, 64, 64)		,	rng=random.Random(_lowerCAmelCase							)							).to(_lowerCAmelCase							)
         UpperCAmelCase_           =image.cpu().permute(0		,	2		,	3		,	1							)[0]
         UpperCAmelCase_           =Image.fromarray(np.uinta(_lowerCAmelCase							)							).convert("RGB"							).resize((256, 256)							)

         if str(_lowerCAmelCase							).startswith("mps"							):
            UpperCAmelCase_           =torch.manual_seed(_lowerCAmelCase							)
         else:
            UpperCAmelCase_           =torch.Generator(device=_lowerCAmelCase							).manual_seed(_lowerCAmelCase							)
         UpperCAmelCase_           ={
             "image": init_image,
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "num_inference_steps": 10,
             "guidance_scale": 7.0,
             "strength": 0.2,
             "output_type": "np",
         }
         return inputs






      def 						lowerCAmelCase__     (						self:    int							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           ="cpu"

         UpperCAmelCase_           =self.get_dummy_components()

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

         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

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

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

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

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

         UpperCAmelCase_           =np.array(
             [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]							)
         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   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     str:
         '''simple docstring'''

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






      def 						lowerCAmelCase__     (						self:    int							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_img2img_frog.npy"							)

         UpperCAmelCase_           =load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"							)
         UpperCAmelCase_           ="A red cartoon frog, 4k"

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

         UpperCAmelCase_           =KandinskyVaaImgaImgPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-decoder"		,	torch_dtype=torch.floataa							)
         UpperCAmelCase_           =pipeline.to(_lowerCAmelCase							)

         pipeline.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =torch.Generator(device="cpu"							).manual_seed(0							)
         UpperCAmelCase_    ,							UpperCAmelCase_           =pipe_prior(
             _lowerCAmelCase		,	generator=_lowerCAmelCase		,	num_inference_steps=5		,	negative_prompt=""		,	).to_tuple()

         UpperCAmelCase_           =pipeline(
             image=_lowerCAmelCase		,	image_embeds=_lowerCAmelCase		,	negative_image_embeds=_lowerCAmelCase		,	generator=_lowerCAmelCase		,	num_inference_steps=100		,	height=768		,	width=768		,	strength=0.2		,	output_type="np"		,	)

         UpperCAmelCase_           =output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(_lowerCAmelCase		,	_lowerCAmelCase							)











import numpy as np
def      A__       (    SCREAMING_SNAKE_CASE_   :      np.ndarray				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  np.ndarray:






   """simple docstring"""







   return np.where(vector > 0				,	SCREAMING_SNAKE_CASE_				,	(alpha * (np.exp(SCREAMING_SNAKE_CASE_						) - 1))						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()