




'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()





class      lowerCAmelCase (			lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, unittest.TestCase):
       __lowercase	:   Tuple										=      StableDiffusionInstructPixaPixPipeline
       __lowercase	:   Optional[Any]										=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
       __lowercase	:   int										=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __lowercase	:   Tuple										=      IMAGE_TO_IMAGE_IMAGE_PARAMS
       __lowercase	:   List[str]										=      IMAGE_TO_IMAGE_IMAGE_PARAMS


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            torch.manual_seed(0	)
            __snake_case            =	UNetaDConditionModel(
                block_out_channels=(32, 64)     ,	layers_per_block=2     ,	sample_size=32     ,	in_channels=8     ,	out_channels=4     ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,	cross_attention_dim=32     ,	)
            __snake_case            =	PNDMScheduler(skip_prk_steps=__a	)
            torch.manual_seed(0	)
            __snake_case            =	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	)
            __snake_case            =	CLIPTextConfig(
                bos_token_id=0     ,	eos_token_id=2     ,	hidden_size=32     ,	intermediate_size=37     ,	layer_norm_eps=1E-05     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	pad_token_id=1     ,	vocab_size=1000     ,	)
            __snake_case            =	CLIPTextModel(__a	)
            __snake_case            =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=0	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(__a	)	).to(__a	)
            __snake_case            =	image.cpu().permute(0     ,	2     ,	3     ,	1	)[0]
            __snake_case            =	Image.fromarray(np.uinta(__a	)	).convert('''RGB'''	)
            if str(__a	).startswith('''mps'''	):
                 __snake_case            =	torch.manual_seed(__a	)
            else:
                 __snake_case            =	torch.Generator(device=__a	).manual_seed(__a	)
            __snake_case            =	{
                '''prompt''': '''A painting of a squirrel eating a burger''',
                '''image''': image,
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 6.0,
                '''image_guidance_scale''': 1,
                '''output_type''': '''numpy''',
            }
            return inputs


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
            __snake_case            =	self.get_dummy_components()
            __snake_case            =	StableDiffusionInstructPixaPixPipeline(**__a	)
            __snake_case            =	sd_pipe.to(__a	)
            sd_pipe.set_progress_bar_config(disable=__a	)

            __snake_case            =	self.get_dummy_inputs(__a	)
            __snake_case            =	sd_pipe(**__a	).images
            __snake_case            =	image[0, -3:, -3:, -1]
            assert image.shape == (1, 32, 32, 3)
            __snake_case            =	np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
            __snake_case            =	self.get_dummy_components()
            __snake_case            =	StableDiffusionInstructPixaPixPipeline(**__a	)
            __snake_case            =	sd_pipe.to(__a	)
            sd_pipe.set_progress_bar_config(disable=__a	)

            __snake_case            =	self.get_dummy_inputs(__a	)
            __snake_case            =	'''french fries'''
            __snake_case            =	sd_pipe(**__a     ,	negative_prompt=__a	)
            __snake_case            =	output.images
            __snake_case            =	image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __snake_case            =	np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
            __snake_case            =	self.get_dummy_components()
            __snake_case            =	StableDiffusionInstructPixaPixPipeline(**__a	)
            __snake_case            =	sd_pipe.to(__a	)
            sd_pipe.set_progress_bar_config(disable=__a	)

            __snake_case            =	self.get_dummy_inputs(__a	)
            __snake_case            =	[inputs['''prompt''']] * 2

            __snake_case            =	np.array(inputs['''image''']	).astype(np.floataa	) / 255.0
            __snake_case            =	torch.from_numpy(__a	).unsqueeze(0	).to(__a	)
            __snake_case            =	image / 2 + 0.5
            __snake_case            =	image.permute(0     ,	3     ,	1     ,	2	)
            __snake_case            =	image.repeat(2     ,	1     ,	1     ,	1	)

            __snake_case            =	sd_pipe(**__a	).images
            __snake_case            =	image[-1, -3:, -3:, -1]

            assert image.shape == (2, 32, 32, 3)
            __snake_case            =	np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
            __snake_case            =	self.get_dummy_components()
            __snake_case            =	EulerAncestralDiscreteScheduler(
                beta_start=0.00_085     ,	beta_end=0.012     ,	beta_schedule='''scaled_linear'''	)
            __snake_case            =	StableDiffusionInstructPixaPixPipeline(**__a	)
            __snake_case            =	sd_pipe.to(__a	)
            sd_pipe.set_progress_bar_config(disable=__a	)

            __snake_case            =	self.get_dummy_inputs(__a	)
            __snake_case            =	sd_pipe(**__a	).images
            __snake_case            =	image[0, -3:, -3:, -1]

            __snake_case            =	[round(__a     ,	4	) for x in image_slice.flatten().tolist()]
            print(''','''.join([str(__a	) for x in slice]	)	)

            assert image.shape == (1, 32, 32, 3)
            __snake_case            =	np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




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




       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	self.get_dummy_components()
            __snake_case            =	StableDiffusionInstructPixaPixPipeline(**__a	)
            __snake_case            =	VaeImageProcessor(do_resize=__a     ,	do_normalize=__a	)
            __snake_case            =	pipe.to(__a	)
            pipe.set_progress_bar_config(disable=__a	)

            __snake_case            =	pipe(**self.get_dummy_inputs_by_type(__a     ,	input_image_type='''pt'''	)	)[0]

            __snake_case            =	components['''vae''']
            __snake_case            =	self.get_dummy_inputs_by_type(__a     ,	input_image_type='''pt'''	)

            for image_param in self.image_latents_params:
                 if image_param in inputs.keys():
                      __snake_case            =	vae.encode(inputs[image_param]	).latent_dist.mode()

            __snake_case            =	pipe(**__a	)[0]

            __snake_case            =	np.abs(out - out_latents_inputs	).max()
            self.assertLess(__a     ,	1E-4     ,	'''passing latents as image input generate different result from passing image'''	)





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


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE=0	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	torch.manual_seed(__a	)
            __snake_case            =	load_image(
                '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''	)
            __snake_case            =	{
                '''prompt''': '''turn him into a cyborg''',
                '''image''': image,
                '''generator''': generator,
                '''num_inference_steps''': 3,
                '''guidance_scale''': 7.5,
                '''image_guidance_scale''': 1.0,
                '''output_type''': '''numpy''',
            }
            return inputs


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
                '''timbrooks/instruct-pix2pix'''     ,	safety_checker=__a	)
            pipe.to(__a	)
            pipe.set_progress_bar_config(disable=__a	)
            pipe.enable_attention_slicing()

            __snake_case            =	self.get_inputs()
            __snake_case            =	pipe(**__a	).images
            __snake_case            =	image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 512, 512, 3)
            __snake_case            =	np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]	)

            assert np.abs(expected_slice - image_slice	).max() < 1E-3


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
                '''timbrooks/instruct-pix2pix'''     ,	safety_checker=__a	)
            __snake_case            =	LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.to(__a	)
            pipe.set_progress_bar_config(disable=__a	)
            pipe.enable_attention_slicing()

            __snake_case            =	self.get_inputs()
            __snake_case            =	pipe(**__a	).images
            __snake_case            =	image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 512, 512, 3)
            __snake_case            =	np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]	)

            assert np.abs(expected_slice - image_slice	).max() < 1E-3


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
                '''timbrooks/instruct-pix2pix'''     ,	safety_checker=__a	)
            __snake_case            =	DDIMScheduler.from_config(pipe.scheduler.config	)
            pipe.to(__a	)
            pipe.set_progress_bar_config(disable=__a	)
            pipe.enable_attention_slicing()

            __snake_case            =	self.get_inputs()
            __snake_case            =	pipe(**__a	).images
            __snake_case            =	image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 512, 512, 3)
            __snake_case            =	np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]	)

            assert np.abs(expected_slice - image_slice	).max() < 1E-3


       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	0

            def callback_fn(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) -> None:
                 __snake_case            =	True
                 nonlocal number_of_steps
                 number_of_steps += 1
                 if step == 1:
                      __snake_case            =	latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      __snake_case            =	latents[0, -3:, -3:, -1]
                      __snake_case            =	np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]	)

                      assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2
                 elif step == 2:
                      __snake_case            =	latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      __snake_case            =	latents[0, -3:, -3:, -1]
                      __snake_case            =	np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]	)

                      assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2

            __snake_case            =	False

            __snake_case            =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
                '''timbrooks/instruct-pix2pix'''     ,	safety_checker=__a     ,	torch_dtype=torch.floataa	)
            __snake_case            =	pipe.to(__a	)
            pipe.set_progress_bar_config(disable=__a	)
            pipe.enable_attention_slicing()

            __snake_case            =	self.get_inputs()
            pipe(**__a     ,	callback=__a     ,	callback_steps=1	)
            assert callback_fn.has_been_called
            assert number_of_steps == 3


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

            __snake_case            =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
                '''timbrooks/instruct-pix2pix'''     ,	safety_checker=__a     ,	torch_dtype=torch.floataa	)
            __snake_case            =	pipe.to(__a	)
            pipe.set_progress_bar_config(disable=__a	)
            pipe.enable_attention_slicing(1	)
            pipe.enable_sequential_cpu_offload()

            __snake_case            =	self.get_inputs()
            __snake_case            =	pipe(**__a	)

            __snake_case            =	torch.cuda.max_memory_allocated()
            # make sure that less than 2.2 GB is allocated
            assert mem_bytes < 2.2 * 10**9




       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	self.get_inputs()
            # resize to resolution that is divisible by 8 but not 16 or 32
            __snake_case            =	inputs['''image'''].resize((504, 504)	)

            __snake_case            =	'''timbrooks/instruct-pix2pix'''
            __snake_case            =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
                __a     ,	safety_checker=__a     ,	)
            pipe.to(__a	)
            pipe.set_progress_bar_config(disable=__a	)
            pipe.enable_attention_slicing()

            __snake_case            =	pipe(**__a	)
            __snake_case            =	output.images[0]

            __snake_case            =	image[255:258, 383:386, -1]

            assert image.shape == (504, 504, 3)
            __snake_case            =	np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 5E-3










"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [False] * len(_UpperCamelCase )
			__lowerCAmelCase							       =     []
			queue.append(_UpperCamelCase )
			__lowerCAmelCase							       =     True

			while queue:
						__lowerCAmelCase							       =     queue.pop(0 )
						for ind in range(len(graph[u] ) ):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(_UpperCamelCase )
												__lowerCAmelCase							       =     True
												__lowerCAmelCase							       =     u

			return visited[t]


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [-1] * (len(_UpperCamelCase ))
			__lowerCAmelCase							       =     0
			while bfs(_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):
						__lowerCAmelCase							       =     float("Inf" )
						__lowerCAmelCase							       =     sink

						while s != source:
									# Find the minimum value in select path
									__lowerCAmelCase							       =     min(_UpperCamelCase       ,					graph[parent[s]][s] )
									__lowerCAmelCase							       =     parent[s]

						max_flow += path_flow
						__lowerCAmelCase							       =     sink

						while v != source:
									__lowerCAmelCase							       =     parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									__lowerCAmelCase							       =     parent[v]
			return max_flow


A	:  Union[str, Any]            =     [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

A       ,       A	:  Any            =     0, 5


print(ford_fulkerson(graph, source, sink))




