





'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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_       (				_lowerCamelCase     ,						unittest.TestCase							):
     __A								=      KandinskyVaaPipeline
     __A								=      [
         "image_embeds",
         "negative_image_embeds",
     ]
     __A								=      ["image_embeds", "negative_image_embeds"]
     __A								=      [
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     __A								=      False





     @property
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        return 32





     @property
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        return 32





     @property
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        return self.time_input_dim





     @property
     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        return self.time_input_dim * 4





     @property
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        return 100





     @property
     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        torch.manual_seed(0						)

        lowercase_      :List[Any]         =			{
            "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,
        }

        lowercase_      :Tuple         =			UNetaDConditionModel(**lowercase_						)
        return model





     @property
     def 							lowercase__				(			self		: List[Any]						):





        """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 							lowercase__				(			self		: str						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        lowercase_      :List[str]         =			self.dummy_unet
        lowercase_      :Any         =			self.dummy_movq

        lowercase_      :List[str]         =			DDIMScheduler(
            num_train_timesteps=1_000     ,				beta_schedule="linear"     ,				beta_start=0.0_00_85     ,				beta_end=0.0_12     ,				clip_sample=lowercase_     ,				set_alpha_to_one=lowercase_     ,				steps_offset=1     ,				prediction_type="epsilon"     ,				thresholding=lowercase_     ,				)

        lowercase_      :Dict         =			{
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: Optional[int]     ,				lowercase		: Union[str, Any]=0						):





        """simple docstring"""




        lowercase_      :Dict         =			floats_tensor((1, self.text_embedder_hidden_size)     ,				rng=random.Random(lowercase_						)						).to(lowercase_						)
        lowercase_      :Any         =			floats_tensor((1, self.text_embedder_hidden_size)     ,				rng=random.Random(seed + 1						)						).to(
            lowercase_						)
        if str(lowercase_						).startswith("mps"						):
           lowercase_      :Optional[Any]         =			torch.manual_seed(lowercase_						)
        else:
           lowercase_      :str         =			torch.Generator(device=lowercase_						).manual_seed(lowercase_						)
        lowercase_      :Optional[Any]         =			{
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "generator": generator,
            "height": 64,
            "width": 64,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs







     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :str         =			"cpu"

        lowercase_      :int         =			self.get_dummy_components()

        lowercase_      :Union[str, Any]         =			self.pipeline_class(**lowercase_						)
        lowercase_      :int         =			pipe.to(lowercase_						)

        pipe.set_progress_bar_config(disable=lowercase_						)

        lowercase_      :Any         =			pipe(**self.get_dummy_inputs(lowercase_						)						)
        lowercase_      :Dict         =			output.images

        lowercase_      :Dict         =			pipe(
            **self.get_dummy_inputs(lowercase_						)     ,				return_dict=lowercase_     ,				)[0]

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

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

        lowercase_      :List[str]         =			np.array(
            [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]						)

        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		: Optional[Any]						):





        """simple docstring"""




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







     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :int         =			load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"						)

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

        lowercase_      :List[str]         =			KandinskyVaaPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-decoder"     ,				torch_dtype=torch.floataa						)
        lowercase_      :List[Any]         =			pipeline.to(lowercase_						)
        pipeline.set_progress_bar_config(disable=lowercase_						)

        lowercase_      :Any         =			"red cat, 4k photo"

        lowercase_      :str         =			torch.Generator(device="cuda"						).manual_seed(0						)
        lowercase_      ,							lowercase_      :Any         =			pipe_prior(
            lowercase_     ,				generator=lowercase_     ,				num_inference_steps=5     ,				negative_prompt=""     ,				).to_tuple()

        lowercase_      :Tuple         =			torch.Generator(device="cuda"						).manual_seed(0						)
        lowercase_      :List[str]         =			pipeline(
            image_embeds=lowercase_     ,				negative_image_embeds=lowercase_     ,				generator=lowercase_     ,				num_inference_steps=100     ,				output_type="np"     ,				)

        lowercase_      :Optional[Any]         =			output.images[0]

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

        assert_mean_pixel_difference(lowercase_     ,				lowercase_						)









'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase : dict			):
   lowercase_      :set[int]         =			set()
   # To detect a back edge, keep track of vertices currently in the recursion stack
   lowercase_      :set[int]         =			set()
   return any(
       node not in visited and depth_first_search(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			)
       for node in graph			)
def 							UpperCAmelCase_  (						__lowerCamelCase : dict     ,__lowerCamelCase : int     ,__lowerCamelCase : set     ,__lowerCamelCase : set			):
   visited.add(__lowerCamelCase			)
   rec_stk.add(__lowerCamelCase			)

   for node in graph[vertex]:
      if node not in visited:
         if depth_first_search(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			):
            return True
      elif node in rec_stk:
         return True

    # The node needs to be removed from recursion stack before function ends
   rec_stk.remove(__lowerCamelCase			)
   return False


if __name__ == "__main__":
 from doctest import testmod

 testmod()


