



'''simple docstring'''






class 							a__:







    def __init__(				self			:  Dict     ):
      a						: Any					       =					0
      a						: Tuple					       =					0
      a						: List[Any]					       =					{}







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Any     ):
      if vertex not in self.adjacency:
        a						: Optional[int]					       =					{}
        self.num_vertices += 1







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  Union[str, Any]     ):
      self.add_vertex(__snake_case     )
      self.add_vertex(__snake_case     )

      if head == tail:
        return

      a						: int					       =					weight
      a						: Optional[Any]					       =					weight







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Optional[int]					       =					self.get_edges()
      for edge in edges:
        a       ,     a       ,     a						: int					       =					edge
        edges.remove((tail, head, weight)     )
      for i in range(len(__snake_case     )     ):
        a						: List[str]					       =					list(edges[i]     )

      edges.sort(key=lambda __snake_case     : e[2]     )
      for i in range(len(__snake_case     ) - 1     ):
        if edges[i][2] >= edges[i + 1][2]:
          a						: Dict					       =					edges[i][2] + 1
      for edge in edges:
        a       ,     a       ,     a						: Optional[int]					       =					edge
        a						: Dict					       =					weight
        a						: int					       =					weight







    def __str__(				self			:  Any     ):
      a						: Dict					       =					''
      for tail in self.adjacency:
        for head in self.adjacency[tail]:
          a						: List[str]					       =					self.adjacency[head][tail]
          string += F"""{head} -> {tail} == {weight}\n"""
      return string.rstrip('\n'     )







    def       lowercase_	(				self			:  Any     ):
      a						: Dict					       =					[]
      for tail in self.adjacency:
        for head in self.adjacency[tail]:
          output.append((tail, head, self.adjacency[head][tail])     )
      return output







    def       lowercase_	(				self			:  int     ):
      return self.adjacency.keys()







    @staticmethod
    def       lowercase_	(				__snake_case			:  Union[str, Any]=None						,      __snake_case			:  List[str]=None     ):
      a						: Optional[int]					       =					Graph()
      if vertices is None:
        a						: Any					       =					[]
      if edges is None:
        a						: int					       =					[]
      for vertex in vertices:
        g.add_vertex(__snake_case     )
      for edge in edges:
        g.add_edge(*__snake_case     )
      return g

    class 							a__:







        def __init__(				self			:  List[Any]     ):
          a						: Any					       =					{}
          a						: List[Any]					       =					{}







        def __len__(				self			:  Tuple     ):
          return len(self.parent     )







        def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  Union[str, Any]     ):
          if item in self.parent:
            return self.find(__snake_case     )

          a						: Optional[int]					       =					item
          a						: Any					       =					0
          return item







        def       lowercase_	(				self			:  int						,      __snake_case			:  List[Any]     ):
          if item not in self.parent:
            return self.make_set(__snake_case     )
          if item != self.parent[item]:
            a						: str					       =					self.find(self.parent[item]     )
          return self.parent[item]







        def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  Optional[int]						,      __snake_case			:  Optional[Any]     ):
          a						: Optional[int]					       =					self.find(__snake_case     )
          a						: Tuple					       =					self.find(__snake_case     )

          if roota == roota:
            return roota

          if self.rank[roota] > self.rank[roota]:
            a						: int					       =					roota
            return roota

          if self.rank[roota] < self.rank[roota]:
            a						: Tuple					       =					roota
            return roota

          if self.rank[roota] == self.rank[roota]:
            self.rank[roota] += 1
            a						: Any					       =					roota
            return roota
          return None







    @staticmethod
    def       lowercase_	(				__snake_case			:  str     ):
      a						: Optional[int]					       =					graph.num_vertices

      a						: List[Any]					       =					Graph.UnionFind()
      a						: Union[str, Any]					       =					[]
      while num_components > 1:
        a						: Union[str, Any]					       =					{}
        for vertex in graph.get_vertices():
          a						: Dict					       =					-1

        a						: str					       =					graph.get_edges()
        for edge in edges:
          a       ,     a       ,     a						: Optional[Any]					       =					edge
          edges.remove((tail, head, weight)     )
        for edge in edges:
          a       ,     a       ,     a						: str					       =					edge
          a						: Union[str, Any]					       =					union_find.find(__snake_case     )
          a						: Optional[int]					       =					union_find.find(__snake_case     )
          if seta != seta:
            if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
              a						: List[str]					       =					[head, tail, weight]

            if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
              a						: Any					       =					[head, tail, weight]
        for vertex in cheap_edge:
          if cheap_edge[vertex] != -1:
            a       ,     a       ,     a						: Dict					       =					cheap_edge[vertex]
            if union_find.find(__snake_case     ) != union_find.find(__snake_case     ):
              union_find.union(__snake_case						,      __snake_case     )
              mst_edges.append(cheap_edge[vertex]     )
              a						: str					       =					num_components - 1
      a						: Any					       =					Graph.build(edges=__snake_case     )
      return mst




'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 							a__( lowerCamelCase__		,			lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   CycleDiffusionPipeline
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        """negative_prompt""",
        """height""",
        """width""",
        """negative_prompt_embeds""",
    }
    lowercase__			=   PipelineTesterMixin.required_optional_params - {"""latents"""}
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}     )
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS







    def       lowercase_	(				self			:  Any     ):
      torch.manual_seed(0     )
      a						: Union[str, Any]					       =					UNetaDConditionModel(
          block_out_channels=(32, 64)						,      layers_per_block=2						,      sample_size=32						,      in_channels=4						,      out_channels=4						,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,      cross_attention_dim=32						,      )
      a						: str					       =					DDIMScheduler(
          beta_start=0.00085						,      beta_end=0.012						,      beta_schedule='scaled_linear'						,      num_train_timesteps=10_00						,      clip_sample=__snake_case						,      set_alpha_to_one=__snake_case						,      )
      torch.manual_seed(0     )
      a						: List[str]					       =					AutoencoderKL(
          block_out_channels=[32, 64]						,      in_channels=3						,      out_channels=3						,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,      latent_channels=4						,      )
      torch.manual_seed(0     )
      a						: int					       =					CLIPTextConfig(
          bos_token_id=0						,      eos_token_id=2						,      hidden_size=32						,      intermediate_size=37						,      layer_norm_eps=1e-0_5						,      num_attention_heads=4						,      num_hidden_layers=5						,      pad_token_id=1						,      vocab_size=10_00						,      )
      a						: List[str]					       =					CLIPTextModel(__snake_case     )
      a						: int					       =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

      a						: Tuple					       =					{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }
      return components







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Dict						,      __snake_case			:  Any=0     ):
      a						: str					       =					floats_tensor((1, 3, 32, 32)						,      rng=random.Random(__snake_case     )     ).to(__snake_case     )
      a						: Optional[Any]					       =					image / 2 + 0.5
      if str(__snake_case     ).startswith('mps'     ):
        a						: List[str]					       =					torch.manual_seed(__snake_case     )
      else:
        a						: Union[str, Any]					       =					torch.Generator(device=__snake_case     ).manual_seed(__snake_case     )
      a						: List[Any]					       =					{
          'prompt': 'An astronaut riding an elephant',
          'source_prompt': 'An astronaut riding a horse',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'eta': 0.1,
          'strength': 0.8,
          'guidance_scale': 3,
          'source_guidance_scale': 1,
          'output_type': 'numpy',
      }
      return inputs







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Optional[Any]					       =					'cpu'  # ensure determinism for the device-dependent torch.Generator

      a						: int					       =					self.get_dummy_components()
      a						: str					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: List[str]					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: Dict					       =					self.get_dummy_inputs(__snake_case     )
      a						: Union[str, Any]					       =					pipe(**__snake_case     )
      a						: List[Any]					       =					output.images

      a						: Optional[Any]					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: Tuple					       =					np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







    @unittest.skipIf(torch_device != 'cuda'						,      'This test requires a GPU'     )
    def       lowercase_	(				self			:  int     ):
      a						: List[Any]					       =					self.get_dummy_components()
      for name, module in components.items():
        if hasattr(__snake_case						,      'half'     ):
          a						: Any					       =					module.half()
      a						: Tuple					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: Any					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: str					       =					self.get_dummy_inputs(__snake_case     )
      a						: int					       =					pipe(**__snake_case     )
      a						: Optional[int]					       =					output.images

      a						: Tuple					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: int					       =					np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







    @skip_mps
    def       lowercase_	(				self			:  List[Any]     ):
      return super().test_save_load_local()







    @unittest.skip('non-deterministic pipeline'     )
    def       lowercase_	(				self			:  Dict     ):
      return super().test_inference_batch_single_identical()







    @skip_mps
    def       lowercase_	(				self			:  int     ):
      return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def       lowercase_	(				self			:  Dict     ):
      return super().test_save_load_optional_components()







    @skip_mps
    def       lowercase_	(				self			:  List[Any]     ):
      return super().test_attention_slicing_forward_pass()

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







    def       lowercase_	(				self			:  Tuple     ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: Optional[int]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'     )
      a						: List[str]					       =					init_image.resize((5_12, 5_12)     )

      a						: Dict					       =					'CompVis/stable-diffusion-v1-4'
      a						: List[str]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: Any					       =					CycleDiffusionPipeline.from_pretrained(
          __snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case						,      torch_dtype=torch.floataa						,      revision='fp16'     )

      pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )
      pipe.enable_attention_slicing()

      a						: Union[str, Any]					       =					'A black colored car'
      a						: Optional[Any]					       =					'A blue colored car'

      a						: int					       =					torch.manual_seed(0     )
      a						: Optional[Any]					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: Dict					       =					output.images

      # the values aren't exactly equal, but the images look the same visually
      assert np.abs(image - expected_image     ).max() < 5e-1







    def       lowercase_	(				self			:  int     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: List[str]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'     )
      a						: str					       =					init_image.resize((5_12, 5_12)     )

      a						: Optional[int]					       =					'CompVis/stable-diffusion-v1-4'
      a						: Union[str, Any]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: str					       =					CycleDiffusionPipeline.from_pretrained(__snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case     )

      pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )
      pipe.enable_attention_slicing()

      a						: Tuple					       =					'A black colored car'
      a						: Tuple					       =					'A blue colored car'

      a						: List[str]					       =					torch.manual_seed(0     )
      a						: str					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: Tuple					       =					output.images

      assert np.abs(image - expected_image     ).max() < 2e-2