






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    _UpperCAmelCase      ( A__	,A__	,A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= StableDiffusionInstructPixaPixPipeline
				lowercase__       							= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
				lowercase__       							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowercase__       							= IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowercase__       							= IMAGE_TO_IMAGE_IMAGE_PARAMS




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								torch.manual_seed(0						)
								lowercase__     				=    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,						)
								lowercase__     				=    PNDMScheduler(skip_prk_steps=lowerCamelCase						)
								torch.manual_seed(0						)
								lowercase__     				=    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						)
								lowercase__     				=    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=1_000,						)
								lowercase__     				=    CLIPTextModel(lowerCamelCase						)
								lowercase__     				=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					List[Any],						lowerCamelCase		:					str=0						):

								'''simple docstring'''
								lowercase__     				=    floats_tensor((1, 3, 32, 32),						rng=random.Random(lowerCamelCase						)						).to(lowerCamelCase						)
								lowercase__     				=    image.cpu().permute(0,						2,						3,						1						)[0]
								lowercase__     				=    Image.fromarray(np.uinta(lowerCamelCase						)						).convert('''RGB'''						)
								if str(lowerCamelCase						).startswith('''mps'''						):
												lowercase__     				=    torch.manual_seed(lowerCamelCase						)
								else:
												lowercase__     				=    torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
								lowercase__     				=    {
								    '''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        lowercase__     (  self		:					str						):

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

								lowercase__     				=    self.get_dummy_inputs(lowerCamelCase						)
								lowercase__     				=    sd_pipe(**lowerCamelCase						).images
								lowercase__     				=    image[0, -3:, -3:, -1]
								assert image.shape == (1, 32, 32, 3)
								lowercase__     				=    np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]						)

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




				def        lowercase__     (  self		:					Union[str, Any]						):

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

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

								assert image.shape == (1, 32, 32, 3)
								lowercase__     				=    np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]						)

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




				def        lowercase__     (  self		:					Optional[int]						):

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

								lowercase__     				=    self.get_dummy_inputs(lowerCamelCase						)
								lowercase__     				=    [inputs['''prompt''']] * 2

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

								lowercase__     				=    sd_pipe(**lowerCamelCase						).images
								lowercase__     				=    image[-1, -3:, -3:, -1]

								assert image.shape == (2, 32, 32, 3)
								lowercase__     				=    np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]						)

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




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__     				=    self.get_dummy_components()
								lowercase__     				=    EulerAncestralDiscreteScheduler(
								    beta_start=0.00085,						beta_end=0.012,						beta_schedule='''scaled_linear'''						)
								lowercase__     				=    StableDiffusionInstructPixaPixPipeline(**lowerCamelCase						)
								lowercase__     				=    sd_pipe.to(lowerCamelCase						)
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase						)

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

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

								assert image.shape == (1, 32, 32, 3)
								lowercase__     				=    np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]						)

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




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								super().test_inference_batch_single_identical(expected_max_diff=3E-3						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.get_dummy_components()
								lowercase__     				=    StableDiffusionInstructPixaPixPipeline(**lowerCamelCase						)
								lowercase__     				=    VaeImageProcessor(do_resize=lowerCamelCase,						do_normalize=lowerCamelCase						)
								lowercase__     				=    pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)

								lowercase__     				=    pipe(**self.get_dummy_inputs_by_type(lowerCamelCase,						input_image_type='''pt'''						)						)[0]

								lowercase__     				=    components['''vae''']
								lowercase__     				=    self.get_dummy_inputs_by_type(lowerCamelCase,						input_image_type='''pt'''						)

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

								lowercase__     				=    pipe(**lowerCamelCase						)[0]

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



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

				"""simple docstring"""




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					Optional[Any]=0						):

								'''simple docstring'''
								lowercase__     				=    torch.manual_seed(lowerCamelCase						)
								lowercase__     				=    load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''						)
								lowercase__     				=    {
								    '''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        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''',						safety_checker=lowerCamelCase						)
								pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 512, 512, 3)
								lowercase__     				=    np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]						)

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




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''',						safety_checker=lowerCamelCase						)
								lowercase__     				=    LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
								pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 512, 512, 3)
								lowercase__     				=    np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]						)

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




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''',						safety_checker=lowerCamelCase						)
								lowercase__     				=    DDIMScheduler.from_config(pipe.scheduler.config						)
								pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 512, 512, 3)
								lowercase__     				=    np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]						)

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




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    0

								def callback_fn(lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor						) -> None:
												lowercase__     				=    True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																lowercase__     				=    latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowercase__     				=    latents[0, -3:, -3:, -1]
																lowercase__     				=    np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]						)

																assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
												elif step == 2:
																lowercase__     				=    latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowercase__     				=    latents[0, -3:, -3:, -1]
																lowercase__     				=    np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]						)

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

								lowercase__     				=    False

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

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




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

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

								lowercase__     				=    self.get_inputs()
								lowercase__     				=    pipe(**lowerCamelCase						)

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




				def        lowercase__     (  self		:					List[Any]						):

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

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

								lowercase__     				=    pipe(**lowerCamelCase						)
								lowercase__     				=    output.images[0]

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

								assert image.shape == (504, 504, 3)
								lowercase__     				=    np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]						)

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












class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								# we need a list not a string, so do something to change the type
								lowercase__     				=    arr.split(''','''						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								for i in range(1,						len(self.array						)						):
												lowercase__     				=    max(
												    int(self.array[i]						) + sum_value[i - 1],						int(self.array[i]						)						)
												lowercase__     				=    max(sum_value[i],						rear[i - 1]						)
								return rear[len(self.array						) - 1]


if __name__ == "__main__":
							A__			:    Dict         =				input('please input some numbers:')
							A__			:    Union[str, Any]         =				SubArray(whole_array)
							A__			:    int         =				array.solve_sub_array()
							print(('the results is:', re))




