
'''simple docstring'''
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  __A				( _SCREAMING_SNAKE_CASE    ,						unittest.TestCase			):
 lowerCamelCase				       =KandinskyVaaControlnetPipeline
 lowerCamelCase				       =['''image_embeds''', '''negative_image_embeds''', '''hint''']
 lowerCamelCase				       =['''image_embeds''', '''negative_image_embeds''', '''hint''']
 lowerCamelCase				       =[
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 lowerCamelCase				       =False
 @property
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   return 32
 @property
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   return 32
 @property
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   return self.time_input_dim
 @property
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   return self.time_input_dim * 4
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   return 1_00
 @property
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   torch.manual_seed(0       )

   __A		:     str												=		{
       """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,
   }

   __A		:     int												=		UNetaDConditionModel(**lowerCamelCase       )
   return model
 @property
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   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			:    List[Any]       ):



   """simple docstring"""





   torch.manual_seed(0       )
   __A		:     int												=		VQModel(**self.dummy_movq_kwargs       )
   return model
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.dummy_unet
   __A		:     List[str]												=		self.dummy_movq

   __A		:     List[str]												=		DDIMScheduler(
       num_train_timesteps=10_00				,   beta_schedule="""linear"""				,   beta_start=0.00_085				,   beta_end=0.012				,   clip_sample=lowerCamelCase				,   set_alpha_to_one=lowerCamelCase				,   steps_offset=1				,   prediction_type="""epsilon"""				,   thresholding=lowerCamelCase				,   )

   __A		:     Union[str, Any]												=		{
       """unet""": unet,
       """scheduler""": scheduler,
       """movq""": movq,
   }
   return components
 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    List[Any]=0       ):



   """simple docstring"""





   __A		:     Tuple												=		floats_tensor((1, self.text_embedder_hidden_size)				,   rng=random.Random(lowerCamelCase       )       ).to(lowerCamelCase       )
   __A		:     Tuple												=		floats_tensor((1, self.text_embedder_hidden_size)				,   rng=random.Random(seed + 1       )       ).to(
       lowerCamelCase       )

   # create hint
   __A		:     Any												=		floats_tensor((1, 3, 64, 64)				,   rng=random.Random(lowerCamelCase       )       ).to(lowerCamelCase       )

   if str(lowerCamelCase       ).startswith("""mps"""       ):
     __A		:     Optional[Any]												=		torch.manual_seed(lowerCamelCase       )
   else:
     __A		:     Optional[Any]												=		torch.Generator(device=lowerCamelCase       ).manual_seed(lowerCamelCase       )
   __A		:     str												=		{
       """image_embeds""": image_embeds,
       """negative_image_embeds""": negative_image_embeds,
       """hint""": hint,
       """generator""": generator,
       """height""": 64,
       """width""": 64,
       """guidance_scale""": 4.0,
       """num_inference_steps""": 2,
       """output_type""": """np""",
   }
   return inputs






 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A		:     str												=		"""cpu"""

   __A		:     str												=		self.get_dummy_components()

   __A		:     str												=		self.pipeline_class(**lowerCamelCase       )
   __A		:     Dict												=		pipe.to(lowerCamelCase       )

   pipe.set_progress_bar_config(disable=lowerCamelCase       )

   __A		:     int												=		pipe(**self.get_dummy_inputs(lowerCamelCase       )       )
   __A		:     List[str]												=		output.images

   __A		:     Optional[Any]												=		pipe(
       **self.get_dummy_inputs(lowerCamelCase       )				,   return_dict=lowerCamelCase				,   )[0]

   __A		:     Dict												=		image[0, -3:, -3:, -1]
   __A		:     List[str]												=		image_from_tuple[0, -3:, -3:, -1]

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

   __A		:     List[str]												=		np.array(
       [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]       )

   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 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





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






 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     List[str]												=		load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""       )

   __A		:     Dict												=		load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinskyv22/hint_image_cat.png"""       )
   __A		:     Union[str, Any]												=		torch.from_numpy(np.array(lowerCamelCase       )       ).float() / 255.0
   __A		:     Dict												=		hint.permute(2				,   0				,   1       ).unsqueeze(0       )

   __A		:     Any												=		KandinskyVaaPriorPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-2-prior"""				,   torch_dtype=torch.floataa       )
   pipe_prior.to(lowerCamelCase       )

   __A		:     Optional[int]												=		KandinskyVaaControlnetPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-2-controlnet-depth"""				,   torch_dtype=torch.floataa       )
   __A		:     Union[str, Any]												=		pipeline.to(lowerCamelCase       )
   pipeline.set_progress_bar_config(disable=lowerCamelCase       )

   __A		:     Any												=		"""A robot, 4k photo"""

   __A		:     List[str]												=		torch.Generator(device="""cuda"""       ).manual_seed(0       )
   __A							,					__A		:     Optional[Any]												=		pipe_prior(
       lowerCamelCase				,   generator=lowerCamelCase				,   num_inference_steps=5				,   negative_prompt=""""""				,   ).to_tuple()

   __A		:     Optional[Any]												=		torch.Generator(device="""cuda"""       ).manual_seed(0       )
   __A		:     Optional[int]												=		pipeline(
       image_embeds=lowerCamelCase				,   negative_image_embeds=lowerCamelCase				,   hint=lowerCamelCase				,   generator=lowerCamelCase				,   num_inference_steps=1_00				,   output_type="""np"""				,   )

   __A		:     int												=		output.images[0]

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

   assert_mean_pixel_difference(lowerCamelCase				,   lowerCamelCase       )





'''simple docstring'''
from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
     import accelerate







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[int]:





  """simple docstring"""

  if not is_accelerate_available():
    return method
  __A		:     int												=		version.parse(accelerate.__version__ ).base_version
  if version.parse(__SCREAMING_SNAKE_CASE ) < version.parse("""0.17.0""" ):
    return method

  def wrapper(self						:  str			,     *__SCREAMING_SNAKE_CASE						:  Tuple			,     **__SCREAMING_SNAKE_CASE						:  Tuple ):
    if hasattr(self			,     """_hf_hook""" ) and hasattr(self._hf_hook			,     """pre_forward""" ):
      self._hf_hook.pre_forward(self )
    return method(self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE )

  return wrapper



