'''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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     Optional[Any]   		=			CycleDiffusionPipeline
       snake_case__    :     Optional[Any]   		=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           'negative_prompt',
           'height',
           'width',
           'negative_prompt_embeds',
       }
       snake_case__    :     int   		=			PipelineTesterMixin.required_optional_params - {'latents'}
       snake_case__    :     Union[str, Any]   		=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
       snake_case__    :     Dict   		=			IMAGE_TO_IMAGE_IMAGE_PARAMS
       snake_case__    :     Tuple   		=			IMAGE_TO_IMAGE_IMAGE_PARAMS




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            torch.manual_seed(0					)
            __lowerCamelCase						:       List[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	,    )
            __lowerCamelCase						:       Tuple				   =				DDIMScheduler(
                beta_start=0.0_0085	,    beta_end=0.012	,    beta_schedule="""scaled_linear"""	,    num_train_timesteps=1000	,    clip_sample=A__	,    set_alpha_to_one=A__	,    )
            torch.manual_seed(0					)
            __lowerCamelCase						:       Dict				   =				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					)
            __lowerCamelCase						:       Optional[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=1000	,    )
            __lowerCamelCase						:       Optional[Any]				   =				CLIPTextModel(A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

            __lowerCamelCase						:       Optional[int]				   =				{
                """unet""": unet,
                """scheduler""": scheduler,
                """vae""": vae,
                """text_encoder""": text_encoder,
                """tokenizer""": tokenizer,
                """safety_checker""": None,
                """feature_extractor""": None,
            }
            return components




       def 					a_					(					self      : int	,    A__      : str	,    A__      : Any=0					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				floats_tensor((1, 3, 32, 32)	,    rng=random.Random(A__					)					).to(A__					)
            __lowerCamelCase						:       Dict				   =				image / 2 + 0.5
            if str(A__					).startswith("""mps"""					):
                 __lowerCamelCase						:       Any				   =				torch.manual_seed(A__					)
            else:
                 __lowerCamelCase						:       Union[str, Any]				   =				torch.Generator(device=A__					).manual_seed(A__					)
            __lowerCamelCase						:       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 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator

            __lowerCamelCase						:       Dict				   =				self.get_dummy_components()
            __lowerCamelCase						:       Union[str, Any]				   =				CycleDiffusionPipeline(**A__					)
            __lowerCamelCase						:       Optional[Any]				   =				pipe.to(A__					)
            pipe.set_progress_bar_config(disable=A__					)

            __lowerCamelCase						:       List[str]				   =				self.get_dummy_inputs(A__					)
            __lowerCamelCase						:       List[Any]				   =				pipe(**A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				output.images

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

            assert images.shape == (1, 32, 32, 3)
            __lowerCamelCase						:       Optional[int]				   =				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 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				self.get_dummy_components()
            for name, module in components.items():
                 if hasattr(A__	,    """half"""					):
                      __lowerCamelCase						:       List[str]				   =				module.half()
            __lowerCamelCase						:       Dict				   =				CycleDiffusionPipeline(**A__					)
            __lowerCamelCase						:       Optional[int]				   =				pipe.to(A__					)
            pipe.set_progress_bar_config(disable=A__					)

            __lowerCamelCase						:       int				   =				self.get_dummy_inputs(A__					)
            __lowerCamelCase						:       int				   =				pipe(**A__					)
            __lowerCamelCase						:       Dict				   =				output.images

            __lowerCamelCase						:       int				   =				images[0, -3:, -3:, -1]

            assert images.shape == (1, 32, 32, 3)
            __lowerCamelCase						:       Tuple				   =				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 					a_					(					self      : Any					):


            """simple docstring"""







            return super().test_save_load_local()




       @unittest.skip("""non-deterministic pipeline"""					)
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            return super().test_inference_batch_single_identical()




       @skip_mps
       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            return super().test_dict_tuple_outputs_equivalent()




       @skip_mps
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            return super().test_save_load_optional_components()




       @skip_mps
       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            return super().test_attention_slicing_forward_pass()







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




       def 					a_					(					self      : Dict					):


            """simple docstring"""







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




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/cycle-diffusion/black_colored_car.png"""					)
            __lowerCamelCase						:       Optional[Any]				   =				load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""					)
            __lowerCamelCase						:       Dict				   =				init_image.resize((512, 512)					)

            __lowerCamelCase						:       Union[str, Any]				   =				"""CompVis/stable-diffusion-v1-4"""
            __lowerCamelCase						:       Tuple				   =				DDIMScheduler.from_pretrained(A__	,    subfolder="""scheduler"""					)
            __lowerCamelCase						:       Union[str, Any]				   =				CycleDiffusionPipeline.from_pretrained(
                A__	,    scheduler=A__	,    safety_checker=A__	,    torch_dtype=torch.floataa	,    revision="""fp16"""					)

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

            __lowerCamelCase						:       List[str]				   =				"""A black colored car"""
            __lowerCamelCase						:       str				   =				"""A blue colored car"""

            __lowerCamelCase						:       Dict				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       List[str]				   =				pipe(
                prompt=A__	,    source_prompt=A__	,    image=A__	,    num_inference_steps=100	,    eta=0.1	,    strength=0.85	,    guidance_scale=3	,    source_guidance_scale=1	,    generator=A__	,    output_type="""np"""	,    )
            __lowerCamelCase						:       int				   =				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 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/cycle-diffusion/black_colored_car.png"""					)
            __lowerCamelCase						:       Dict				   =				load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""					)
            __lowerCamelCase						:       Optional[Any]				   =				init_image.resize((512, 512)					)

            __lowerCamelCase						:       str				   =				"""CompVis/stable-diffusion-v1-4"""
            __lowerCamelCase						:       Tuple				   =				DDIMScheduler.from_pretrained(A__	,    subfolder="""scheduler"""					)
            __lowerCamelCase						:       Union[str, Any]				   =				CycleDiffusionPipeline.from_pretrained(A__	,    scheduler=A__	,    safety_checker=A__					)

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

            __lowerCamelCase						:       Optional[int]				   =				"""A black colored car"""
            __lowerCamelCase						:       Optional[int]				   =				"""A blue colored car"""

            __lowerCamelCase						:       Optional[int]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Any				   =				pipe(
                prompt=A__	,    source_prompt=A__	,    image=A__	,    num_inference_steps=100	,    eta=0.1	,    strength=0.85	,    guidance_scale=3	,    source_guidance_scale=1	,    generator=A__	,    output_type="""np"""	,    )
            __lowerCamelCase						:       List[Any]				   =				output.images

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











'''simple docstring'''

def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     # Return True if there is node that has not iterated.
     __lowerCamelCase						:       Optional[Any]				   =				[False] * len(_lowercase     )
     __lowerCamelCase						:       Dict				   =				[]
     queue.append(_lowercase     )
     __lowerCamelCase						:       Optional[int]				   =				True

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

     return visited[t]
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # This array is filled by BFS and to store path
     __lowerCamelCase						:       Any				   =				[-1] * (len(_lowercase     ))
     __lowerCamelCase						:       Optional[int]				   =				0
     while bfs(_lowercase,						_lowercase,						_lowercase,						_lowercase     ):
          __lowerCamelCase						:       List[Any]				   =				float("""Inf"""     )
          __lowerCamelCase						:       Union[str, Any]				   =				sink

          while s != source:
               # Find the minimum value in select path
               __lowerCamelCase						:       Optional[int]				   =				min(_lowercase,						graph[parent[s]][s]     )
               __lowerCamelCase						:       Union[str, Any]				   =				parent[s]

          max_flow += path_flow
          __lowerCamelCase						:       str				   =				sink

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


UpperCAmelCase__						:str          =			[
    [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],
]

UpperCAmelCase__       ,				UpperCAmelCase__						:Optional[Any]          =			0, 5
print(ford_fulkerson(graph, source, sink))



