


"""simple docstring"""







def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      List[Any]:
  '''simple docstring'''







  a						:		List[Any]      							=			[False] * len(A_       )
  a						:		int      							=			[]
  queue.append(A_       )
  a						:		int      							=			True

  while queue:
    a						:		List[str]      							=			queue.pop(0       )
    for ind in range(len(graph[u]       )       ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(A_       )
        a						:		Dict      							=			True
        a						:		Optional[int]      							=			u

  return visited[t]


def      lowercase			(				A_  ,   A_  ,   A_       )->      str:
  '''simple docstring'''







  a						:		int      							=			[-1] * (len(A_       ))
  a						:		List[Any]      							=			0
  while bfs(A_  ,   A_  ,   A_  ,   A_       ):
    a						:		Tuple      							=			float("Inf"       )
    a						:		List[str]      							=			sink

    while s != source:
      # Find the minimum value in select path
      a						:		List[Any]      							=			min(A_  ,   graph[parent[s]][s]       )
      a						:		str      							=			parent[s]

    max_flow += path_flow
    a						:		Optional[Any]      							=			sink

    while v != source:
      a						:		List[Any]      							=			parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      a						:		str      							=			parent[v]
  return max_flow


__lowercase    					=      [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__lowercase					,							__lowercase    					=      0, 5







print(ford_fulkerson(graph, source, sink))










"""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  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




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






    @property
    def 	__snake_case							(					self				:    Optional[Any]):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return self.time_input_dim






    @property
    def 	__snake_case							(					self				:    Any):
      return self.time_input_dim * 4






    @property
    def 	__snake_case							(					self				:    str):
      return 100






    @property
    def 	__snake_case							(					self				:    str):
      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						:		Dict      							=			UNetaDConditionModel(**__UpperCAmelCase)
      return model






    @property
    def 	__snake_case							(					self				:    str):
      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 	__snake_case							(					self				:    Union[str, Any]):
      torch.manual_seed(0)
      a						:		Dict      							=			VQModel(**self.dummy_movq_kwargs)
      return model






    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Optional[Any]      							=			self.dummy_unet
      a						:		int      							=			self.dummy_movq

      a						:		str      							=			DDIMScheduler(
          num_train_timesteps=1000				, beta_schedule="linear"				, beta_start=0.00_085				, beta_end=0.012				, clip_sample=__UpperCAmelCase				, set_alpha_to_one=__UpperCAmelCase				, steps_offset=1				, prediction_type="epsilon"				, thresholding=__UpperCAmelCase				, )

      a						:		Optional[Any]      							=			{
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    int=0):
      a						:		Optional[int]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(seed + 1)).to(
          __UpperCAmelCase)

      # create hint
      a						:		Union[str, Any]      							=			floats_tensor((1, 3, 64, 64)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)

      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Union[str, Any]      							=			torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		List[Any]      							=			torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      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 	__snake_case							(					self				:    Dict):
      a						:		str      							=			"cpu"

      a						:		Tuple      							=			self.get_dummy_components()

      a						:		Dict      							=			self.pipeline_class(**__UpperCAmelCase)
      a						:		Optional[int]      							=			pipe.to(__UpperCAmelCase)

      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Optional[Any]      							=			pipe(**self.get_dummy_inputs(__UpperCAmelCase))
      a						:		Any      							=			output.images

      a						:		Any      							=			pipe(
          **self.get_dummy_inputs(__UpperCAmelCase)				, return_dict=__UpperCAmelCase				, )[0]

      a						:		Union[str, Any]      							=			image[0, -3:, -3:, -1]
      a						:		int      							=			image_from_tuple[0, -3:, -3:, -1]

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

      a						:		Tuple      							=			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  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Optional[int]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






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

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

      a						:		Optional[int]      							=			KandinskyVaaPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"				, torch_dtype=torch.floataa)
      pipe_prior.to(__UpperCAmelCase)

      a						:		List[str]      							=			KandinskyVaaControlnetPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"				, torch_dtype=torch.floataa)
      a						:		int      							=			pipeline.to(__UpperCAmelCase)
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Tuple      							=			"A robot, 4k photo"

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

      a						:		str      							=			torch.Generator(device="cuda").manual_seed(0)
      a						:		Union[str, Any]      							=			pipeline(
          image_embeds=__UpperCAmelCase				, negative_image_embeds=__UpperCAmelCase				, hint=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=100				, output_type="np"				, )

      a						:		str      							=			output.images[0]

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

      assert_mean_pixel_difference(__UpperCAmelCase				, __UpperCAmelCase)






