


'''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       (       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 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      return 32
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      return 32
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      return self.time_input_dim
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      return self.time_input_dim * 4
 @property
 def 							__UpperCAmelCase						(	self  )   ->							int:
      return 1_00
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      torch.manual_seed(0  )

      _a      =							{
          '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      =							UNetaDConditionModel(**__magic_name__  )
      return model
 @property
 def 							__UpperCAmelCase						(	self  )   ->							int:
      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  )   ->							Optional[Any]:
      torch.manual_seed(0  )
      _a      =							VQModel(**self.dummy_movq_kwargs  )
      return model
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.dummy_unet
      _a      =							self.dummy_movq

      _a      =							DDIMScheduler(
          num_train_timesteps=10_00 ,			beta_schedule='linear' ,			beta_start=0.0_0_0_8_5 ,			beta_end=0.0_1_2 ,			clip_sample=__magic_name__ ,			set_alpha_to_one=__magic_name__ ,			steps_offset=1 ,			prediction_type='epsilon' ,			thresholding=__magic_name__ ,			)

      _a      =							{
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }
      return components
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=0  )   ->							Dict:
      _a      =							floats_tensor((1, self.text_embedder_hidden_size) ,			rng=random.Random(__magic_name__  )  ).to(__magic_name__  )
      _a      =							floats_tensor((1, self.text_embedder_hidden_size) ,			rng=random.Random(seed + 1  )  ).to(
          __magic_name__  )

      # create hint
      _a      =							floats_tensor((1, 3, 64, 64) ,			rng=random.Random(__magic_name__  )  ).to(__magic_name__  )

      if str(__magic_name__  ).startswith('mps'  ):
           _a      =							torch.manual_seed(__magic_name__  )
      else:
           _a      =							torch.Generator(device=__magic_name__  ).manual_seed(__magic_name__  )
      _a      =							{
          '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 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							'cpu'

      _a      =							self.get_dummy_components()

      _a      =							self.pipeline_class(**__magic_name__  )
      _a      =							pipe.to(__magic_name__  )

      pipe.set_progress_bar_config(disable=__magic_name__  )

      _a      =							pipe(**self.get_dummy_inputs(__magic_name__  )  )
      _a      =							output.images

      _a      =							pipe(
          **self.get_dummy_inputs(__magic_name__  ) ,			return_dict=__magic_name__ ,			)[0]

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

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

      _a      =							np.array(
          [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]  )

      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 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'  )

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

      _a      =							KandinskyVaaPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-prior' ,			torch_dtype=torch.floataa  )
      pipe_prior.to(__magic_name__  )

      _a      =							KandinskyVaaControlnetPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-controlnet-depth' ,			torch_dtype=torch.floataa  )
      _a      =							pipeline.to(__magic_name__  )
      pipeline.set_progress_bar_config(disable=__magic_name__  )

      _a      =							'A robot, 4k photo'

      _a      =							torch.Generator(device='cuda'  ).manual_seed(0  )
      _a      =							pipe_prior(
          __magic_name__ ,			generator=__magic_name__ ,			num_inference_steps=5 ,			negative_prompt='' ,			).to_tuple()

      _a      =							torch.Generator(device='cuda'  ).manual_seed(0  )
      _a      =							pipeline(
          image_embeds=__magic_name__ ,			negative_image_embeds=__magic_name__ ,			hint=__magic_name__ ,			generator=__magic_name__ ,			num_inference_steps=1_00 ,			output_type='np' ,			)

      _a      =							output.images[0]

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

      assert_mean_pixel_difference(__magic_name__ ,			__magic_name__  )





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     assert (
         isinstance(lowerCAmelCase__			,  lowerCAmelCase__						) and number_of_steps > 0
     ), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
     if number_of_steps == 1:
          return 1
     _a ,				_a      =							1, 1
     for _ in range(number_of_steps - 1						):
          _a ,				_a      =							current + previous, current
     return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()

