


"""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 __snake_case		(       _a					,							unittest.TestCase   ):







   """simple docstring"""




   lowerCAmelCase_					: Optional[Any]  						=      KandinskyVaaControlnetPipeline
   lowerCAmelCase_					: List[str]  						=      ['''image_embeds''', '''negative_image_embeds''', '''hint''']
   lowerCAmelCase_					: Optional[int]  						=      ['''image_embeds''', '''negative_image_embeds''', '''hint''']
   lowerCAmelCase_					: Tuple  						=      [
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   lowerCAmelCase_					: List[str]  						=      False




   @property
   def SCREAMING_SNAKE_CASE_   (		self				:int							):
          return 32




   @property
   def SCREAMING_SNAKE_CASE_   (		self				:Union[str, Any]							):
          return 32




   @property
   def SCREAMING_SNAKE_CASE_   (		self				:str							):
          return self.time_input_dim




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




   @property
   def SCREAMING_SNAKE_CASE_   (		self				:Any							):
          return 100




   @property
   def SCREAMING_SNAKE_CASE_   (		self				:List[str]							):
          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(**UpperCamelCase__							)
          return model




   @property
   def SCREAMING_SNAKE_CASE_   (		self				:Optional[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 SCREAMING_SNAKE_CASE_   (		self				:Optional[int]							):
          torch.manual_seed(0							)
          _a							 =					VQModel(**self.dummy_movq_kwargs							)
          return model




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]							):
          _a							 =					self.dummy_unet
          _a							 =					self.dummy_movq

          _a							 =					DDIMScheduler(
              num_train_timesteps=1_000					,	beta_schedule="linear"					,	beta_start=0.00085					,	beta_end=0.012					,	clip_sample=UpperCamelCase__					,	set_alpha_to_one=UpperCamelCase__					,	steps_offset=1					,	prediction_type="epsilon"					,	thresholding=UpperCamelCase__					,	)

          _a							 =					{
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }
          return components




   def SCREAMING_SNAKE_CASE_   (		self				:Optional[Any]					,	UpperCamelCase__				:List[str]					,	UpperCamelCase__				:List[str]=0							):
          _a							 =					floats_tensor((1, self.text_embedder_hidden_size)					,	rng=random.Random(UpperCamelCase__							)							).to(UpperCamelCase__							)
          _a							 =					floats_tensor((1, self.text_embedder_hidden_size)					,	rng=random.Random(seed + 1							)							).to(
              UpperCamelCase__							)

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

          if str(UpperCamelCase__							).startswith("mps"							):
                 _a							 =					torch.manual_seed(UpperCamelCase__							)
          else:
                 _a							 =					torch.Generator(device=UpperCamelCase__							).manual_seed(UpperCamelCase__							)
          _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 SCREAMING_SNAKE_CASE_   (		self				:Tuple							):
          _a							 =					"cpu"

          _a							 =					self.get_dummy_components()

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

          pipe.set_progress_bar_config(disable=UpperCamelCase__							)

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

          _a							 =					pipe(
              **self.get_dummy_inputs(UpperCamelCase__							)					,	return_dict=UpperCamelCase__					,	)[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(
              [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]							)

          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 __snake_case		(       unittest.TestCase   ):







   """simple docstring"""




   def SCREAMING_SNAKE_CASE_   (		self				:Optional[int]							):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




   def SCREAMING_SNAKE_CASE_   (		self				:int							):
          _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(UpperCamelCase__							)							).float() / 255.0
          _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(UpperCamelCase__							)

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

          _a							 =					"A robot, 4k photo"

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

          _a							 =					torch.Generator(device="cuda"							).manual_seed(0							)
          _a							 =					pipeline(
              image_embeds=UpperCamelCase__					,	negative_image_embeds=UpperCamelCase__					,	hint=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=100					,	output_type="np"					,	)

          _a							 =					output.images[0]

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

          assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__							)








class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any]):



          """simple docstring"""

          _lowercase              ={}





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          print(self.vertex)
          for i in self.vertex:
             print(snake_case,      ' -> ',      ' -> '.join([str(snake_case) for j in self.vertex[i]]))





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:int):



          """simple docstring"""

          if from_vertex in self.vertex:
             self.vertex[from_vertex].append(snake_case)
          else:
             # else make a new vertex
             _lowercase              =[to_vertex]





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =[False] * len(self.vertex)

          # call the recursive helper function
          for i in range(len(self.vertex)):
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:list):



          """simple docstring"""

          _lowercase              =True

          print(snake_case,      end=' ')

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    print("DFS:")
    g.dfs()

    # OUTPUT:
    # 0  ->  1 -> 2
    # 1  ->  2
    # 2  ->  0 -> 3
    # 3  ->  3
    # DFS:
    #  0 1 2 3


