




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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 						__lowercase		(UpperCamelCase_					,		unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						KandinskyVaaControlnetPipeline
      _snake_case												=						["""image_embeds""", """negative_image_embeds""", """hint"""]
      _snake_case												=						["""image_embeds""", """negative_image_embeds""", """hint"""]
      _snake_case												=						[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      _snake_case												=						False

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

      @property
      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         return 3_2

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

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

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

      @property
      def      UpperCAmelCase				(				self     )		->    Any:
         torch.manual_seed(0     )

         snake_case				:    Any           =	{
             """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				:    Optional[int]           =	UNetaDConditionModel(**_a     )
         return model

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

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         snake_case				:    Union[str, Any]           =	self.dummy_unet
         snake_case				:    Optional[Any]           =	self.dummy_movq

         snake_case				:    Any           =	DDIMScheduler(
             num_train_timesteps=1_0_0_0    ,				beta_schedule="""linear"""    ,				beta_start=0.0_00_85    ,				beta_end=0.0_12    ,				clip_sample=_a    ,				set_alpha_to_one=_a    ,				steps_offset=1    ,				prediction_type="""epsilon"""    ,				thresholding=_a    ,				)

         snake_case				:    str           =	{
             """unet""": unet,
             """scheduler""": scheduler,
             """movq""": movq,
         }
         return components

      def      UpperCAmelCase				(				self    ,				A    ,				A=0     )		->    Optional[Any]:
         snake_case				:    Optional[int]           =	floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(_a     )     ).to(_a     )
         snake_case				:    Union[str, Any]           =	floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(seed + 1     )     ).to(
             _a     )

         # create hint
         snake_case				:    Dict           =	floats_tensor((1, 3, 6_4, 6_4)    ,				rng=random.Random(_a     )     ).to(_a     )

         if str(_a     ).startswith("""mps"""     ):
            snake_case				:    Optional[Any]           =	torch.manual_seed(_a     )
         else:
            snake_case				:    Tuple           =	torch.Generator(device=_a     ).manual_seed(_a     )
         snake_case				:    Tuple           =	{
             """image_embeds""": image_embeds,
             """negative_image_embeds""": negative_image_embeds,
             """hint""": hint,
             """generator""": generator,
             """height""": 6_4,
             """width""": 6_4,
             """guidance_scale""": 4.0,
             """num_inference_steps""": 2,
             """output_type""": """np""",
         }
         return inputs

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

         snake_case				:    Any           =	self.get_dummy_components()

         snake_case				:    str           =	self.pipeline_class(**_a     )
         snake_case				:    Optional[int]           =	pipe.to(_a     )

         pipe.set_progress_bar_config(disable=_a     )

         snake_case				:    Dict           =	pipe(**self.get_dummy_inputs(_a     )     )
         snake_case				:    Optional[int]           =	output.images

         snake_case				:    Any           =	pipe(
             **self.get_dummy_inputs(_a     )    ,				return_dict=_a    ,				)[0]

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

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

         snake_case				:    Tuple           =	np.array(
             [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]     )

         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							):




      """simple docstring"""





      def      UpperCAmelCase				(				self     )		->    Any:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

      def      UpperCAmelCase				(				self     )		->    str:
         snake_case				:    Dict           =	load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""     )

         snake_case				:    List[str]           =	load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/kandinskyv22/hint_image_cat.png"""     )
         snake_case				:    int           =	torch.from_numpy(np.array(_a     )     ).float() / 2_55.0
         snake_case				:    List[Any]           =	hint.permute(2    ,				0    ,				1     ).unsqueeze(0     )

         snake_case				:    Optional[Any]           =	KandinskyVaaPriorPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-2-prior"""    ,				torch_dtype=torch.floataa     )
         pipe_prior.to(_a     )

         snake_case				:    Optional[Any]           =	KandinskyVaaControlnetPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-2-controlnet-depth"""    ,				torch_dtype=torch.floataa     )
         snake_case				:    Dict           =	pipeline.to(_a     )
         pipeline.set_progress_bar_config(disable=_a     )

         snake_case				:    Tuple           =	"""A robot, 4k photo"""

         snake_case				:    Dict           =	torch.Generator(device="""cuda"""     ).manual_seed(0     )
         snake_case				:    List[Any]           =	pipe_prior(
             _a    ,				generator=_a    ,				num_inference_steps=5    ,				negative_prompt=""""""    ,				).to_tuple()

         snake_case				:    int           =	torch.Generator(device="""cuda"""     ).manual_seed(0     )
         snake_case				:    Union[str, Any]           =	pipeline(
             image_embeds=_a    ,				negative_image_embeds=_a    ,				hint=_a    ,				generator=_a    ,				num_inference_steps=1_0_0    ,				output_type="""np"""    ,				)

         snake_case				:    List[Any]           =	output.images[0]

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

         assert_mean_pixel_difference(_a    ,				_a     )











import warnings
from functools import wraps
from typing import Callable




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Callable:

   @wraps(lowercase					)
   def _inner_fn(*lowercase						,**lowercase					):
      warnings.warn(
          (f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")						,lowercase						,)
      return fn(*lowercase						,**lowercase					)

   return _inner_fn





