






'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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        _UpperCAmelCase						(		__snake_case				,					unittest.TestCase			):
     __lowerCamelCase:   List[Any]		       =    KandinskyVaaInpaintPipeline
     __lowerCamelCase:   List[Any]		       =    ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
     __lowerCamelCase:   List[Any]		       =    [
         'image_embeds',
         'negative_image_embeds',
         'image',
         'mask_image',
     ]
     __lowerCamelCase:   List[Any]		       =    [
         'generator',
         'height',
         'width',
         'latents',
         'guidance_scale',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     __lowerCamelCase:   Optional[int]		       =    False




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






        '''simple docstring'''






        return 3_2




     @property
     def 				lowerCAmelCase__				(			self  :       str			):






        '''simple docstring'''






        return 3_2




     @property
     def 				lowerCAmelCase__				(			self  :       Optional[int]			):






        '''simple docstring'''






        return self.time_input_dim




     @property
     def 				lowerCAmelCase__				(			self  :       Optional[int]			):






        '''simple docstring'''






        return self.time_input_dim * 4




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






        '''simple docstring'''






        return 1_0_0




     @property
     def 				lowerCAmelCase__				(			self  :       Optional[int]			):






        '''simple docstring'''






        torch.manual_seed(0			)

        lowercase_	:    List[Any]           =     {
            "in_channels": 9,
            # 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_	:    Optional[int]           =     UNetaDConditionModel(**A_			)
        return model




     @property
     def 				lowerCAmelCase__				(			self  :       int			):






        '''simple docstring'''






        return {
            "block_out_channels": [3_2, 6_4],
            "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": 1_2,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }




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






        '''simple docstring'''






        torch.manual_seed(0			)
        lowercase_	:    Union[str, Any]           =     VQModel(**self.dummy_movq_kwargs			)
        return model




     def 				lowerCAmelCase__				(			self  :       List[Any]			):






        '''simple docstring'''






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

        lowercase_	:    Optional[Any]           =     DDIMScheduler(
            num_train_timesteps=1_0_0_0  ,     beta_schedule="linear"  ,     beta_start=0.0_0085  ,     beta_end=0.012  ,     clip_sample=A_  ,     set_alpha_to_one=A_  ,     steps_offset=1  ,     prediction_type="epsilon"  ,     thresholding=A_  ,     )

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

        return components




     def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       Optional[int]  ,     a  :       Optional[int]=0			):






        '''simple docstring'''






        lowercase_	:    int           =     floats_tensor((1, self.text_embedder_hidden_size)  ,     rng=random.Random(A_			)			).to(A_			)
        lowercase_	:    Any           =     floats_tensor((1, self.text_embedder_hidden_size)  ,     rng=random.Random(seed + 1			)			).to(
            A_			)
        # create init_image
        lowercase_	:    Any           =     floats_tensor((1, 3, 6_4, 6_4)  ,     rng=random.Random(A_			)			).to(A_			)
        lowercase_	:    Optional[Any]           =     image.cpu().permute(0  ,     2  ,     3  ,     1			)[0]
        lowercase_	:    str           =     Image.fromarray(np.uinta(A_			)			).convert("RGB"			).resize((2_5_6, 2_5_6)			)
        # create mask
        lowercase_	:    Dict           =     np.ones((6_4, 6_4)  ,     dtype=np.floataa			)
        lowercase_	:    Union[str, Any]           =     0

        if str(A_			).startswith("mps"			):
           lowercase_	:    Tuple           =     torch.manual_seed(A_			)
        else:
           lowercase_	:    Optional[int]           =     torch.Generator(device=A_			).manual_seed(A_			)
        lowercase_	:    Optional[int]           =     {
            "image": init_image,
            "mask_image": mask,
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "generator": generator,
            "height": 6_4,
            "width": 6_4,
            "num_inference_steps": 2,
            "guidance_scale": 4.0,
            "output_type": "np",
        }
        return inputs




     def 				lowerCAmelCase__				(			self  :       Any			):






        '''simple docstring'''






        lowercase_	:    int           =     "cpu"

        lowercase_	:    Dict           =     self.get_dummy_components()

        lowercase_	:    str           =     self.pipeline_class(**A_			)
        lowercase_	:    Any           =     pipe.to(A_			)

        pipe.set_progress_bar_config(disable=A_			)

        lowercase_	:    int           =     pipe(**self.get_dummy_inputs(A_			)			)
        lowercase_	:    List[Any]           =     output.images

        lowercase_	:    List[str]           =     pipe(
            **self.get_dummy_inputs(A_			)  ,     return_dict=A_  ,     )[0]

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

        print(f"""image.shape {image.shape}"""			)

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

        lowercase_	:    List[str]           =     np.array(
            [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]			)

        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()}"""





     def 				lowerCAmelCase__				(			self  :       Dict			):






        '''simple docstring'''






        super().test_inference_batch_single_identical(expected_max_diff=3e-3			)


@slow
@require_torch_gpu
class        _UpperCAmelCase						(		unittest.TestCase			):




     def 				lowerCAmelCase__				(			self  :       int			):






        '''simple docstring'''






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





     def 				lowerCAmelCase__				(			self  :       int			):






        '''simple docstring'''






        lowercase_	:    Optional[Any]           =     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"			)

        lowercase_	:    Any           =     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"			)
        lowercase_	:    Tuple           =     np.ones((7_6_8, 7_6_8)  ,     dtype=np.floataa			)
        lowercase_	:    str           =     0

        lowercase_	:    Tuple           =     "a hat"

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

        lowercase_	:    Optional[Any]           =     KandinskyVaaInpaintPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-decoder-inpaint"  ,     torch_dtype=torch.floataa			)
        lowercase_	:    int           =     pipeline.to(A_			)
        pipeline.set_progress_bar_config(disable=A_			)

        lowercase_	:    Tuple           =     torch.Generator(device="cpu"			).manual_seed(0			)
        lowercase_	:    List[Any]           =     pipe_prior(
            A_  ,     generator=A_  ,     num_inference_steps=5  ,     negative_prompt=""  ,     ).to_tuple()

        lowercase_	:    Union[str, Any]           =     pipeline(
            image=A_  ,     mask_image=A_  ,     image_embeds=A_  ,     negative_image_embeds=A_  ,     generator=A_  ,     num_inference_steps=1_0_0  ,     height=7_6_8  ,     width=7_6_8  ,     output_type="np"  ,     )

        lowercase_	:    Union[str, Any]           =     output.images[0]

        assert image.shape == (7_6_8, 7_6_8, 3)

        assert_mean_pixel_difference(A_  ,     A_			)












'''simple docstring'''
import heapq




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    list[list]           =     []

   # for each node and his adjacency list add them and the rank of the node to queue
   # using heapq module the queue will be filled like a Priority Queue
   # heapq works with a min priority queue, so I used -1*len(v) to build it
   for key, value in graph.items():
      # O(log(n))
      heapq.heappush(_UpperCamelCase						,		[-1 * len(_UpperCamelCase							), (key, value)]							)

   # chosen_vertices = set of chosen vertices
   lowercase_	:    Optional[Any]           =     set()

   # while queue isn't empty and there are still edges
   #   (queue[0][0] is the rank of the node with max rank)
   while queue and queue[0][0] != 0:
      # extract vertex with max rank from queue and add it to chosen_vertices
      lowercase_	:    Any           =     heapq.heappop(_UpperCamelCase							)[1][0]
      chosen_vertices.add(_UpperCamelCase							)

      # Remove all arcs adjacent to argmax
      for elem in queue:
         # if v haven't adjacent node, skip
         if elem[0] == 0:
            continue
         # if argmax is reachable from elem
         # remove argmax from elem's adjacent list and update his rank
         if argmax in elem[1][1]:
            lowercase_	:    str           =     elem[1][1].index(_UpperCamelCase							)
            del elem[1][1][index]
            elem[0] += 1
        # re-order the queue
      heapq.heapify(_UpperCamelCase							)
   return chosen_vertices


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 UpperCamelCase__ =			{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




