

'''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 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    StableDiffusionInstructPixaPixPipeline
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowerCamelCase_		       =    IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowerCamelCase_		       =    IMAGE_TO_IMAGE_IMAGE_PARAMS







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							Any    		=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 :							Optional[int]    		=PNDMScheduler(skip_prk_steps=UpperCAmelCase__   )
										torch.manual_seed(0   )
										lowercase :							Union[str, Any]    		=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 :							Optional[int]    		=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		,					)
										lowercase :							int    		=CLIPTextModel(UpperCAmelCase__   )
										lowercase :							List[Any]    		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										lowercase :							List[str]    		={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Optional[int]=0   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=floats_tensor((1, 3, 32, 32)		,					rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
										lowercase :							str    		=image.cpu().permute(0		,					2		,					3		,					1   )[0]
										lowercase :							Any    		=Image.fromarray(np.uinta(UpperCAmelCase__   )   ).convert('''RGB'''   )
										if str(UpperCAmelCase__   ).startswith('''mps'''   ):
																lowercase :							Dict    		=torch.manual_seed(UpperCAmelCase__   )
										else:
																lowercase :							List[Any]    		=torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )
										lowercase :							Dict    		={
										    '''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     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							str    		=self.get_dummy_components()
										lowercase :							str    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Tuple    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Any    		=sd_pipe(**UpperCAmelCase__   ).images
										lowercase :							int    		=image[0, -3:, -3:, -1]
										assert image.shape == (1, 32, 32, 3)
										lowercase :							Union[str, Any]    		=np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]   )

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							str    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							Tuple    		=self.get_dummy_components()
										lowercase :							Optional[int]    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Optional[Any]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Tuple    		='''french fries'''
										lowercase :							Dict    		=sd_pipe(**UpperCAmelCase__		,					negative_prompt=UpperCAmelCase__   )
										lowercase :							List[str]    		=output.images
										lowercase :							Tuple    		=image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowercase :							Any    		=np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]   )

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







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							List[str]    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							str    		=self.get_dummy_components()
										lowercase :							Any    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Optional[Any]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Any    		=[inputs['''prompt''']] * 2

										lowercase :							int    		=np.array(inputs['''image''']   ).astype(np.floataa   ) / 2_55.0
										lowercase :							Tuple    		=torch.from_numpy(UpperCAmelCase__   ).unsqueeze(0   ).to(UpperCAmelCase__   )
										lowercase :							List[Any]    		=image / 2 + 0.5
										lowercase :							List[str]    		=image.permute(0		,					3		,					1		,					2   )
										lowercase :							List[str]    		=image.repeat(2		,					1		,					1		,					1   )

										lowercase :							List[Any]    		=sd_pipe(**UpperCAmelCase__   ).images
										lowercase :							Optional[int]    		=image[-1, -3:, -3:, -1]

										assert image.shape == (2, 32, 32, 3)
										lowercase :							Union[str, Any]    		=np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]   )

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Any    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							Union[str, Any]    		=self.get_dummy_components()
										lowercase :							Any    		=EulerAncestralDiscreteScheduler(
										    beta_start=0.0_00_85		,					beta_end=0.0_12		,					beta_schedule='''scaled_linear'''   )
										lowercase :							Any    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Dict    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Any    		=sd_pipe(**UpperCAmelCase__   ).images
										lowercase :							Any    		=image[0, -3:, -3:, -1]

										lowercase :							List[str]    		=[round(UpperCAmelCase__		,					4   ) for x in image_slice.flatten().tolist()]
										print(''','''.join([str(UpperCAmelCase__   ) for x in slice]   )   )

										assert image.shape == (1, 32, 32, 3)
										lowercase :							Dict    		=np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]   )

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







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.get_dummy_components()
										lowercase :							Union[str, Any]    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=VaeImageProcessor(do_resize=UpperCAmelCase__		,					do_normalize=UpperCAmelCase__   )
										lowercase :							str    		=pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							List[str]    		=pipe(**self.get_dummy_inputs_by_type(UpperCAmelCase__		,					input_image_type='''pt'''   )   )[0]

										lowercase :							List[str]    		=components['''vae''']
										lowercase :							str    		=self.get_dummy_inputs_by_type(UpperCAmelCase__		,					input_image_type='''pt'''   )

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

										lowercase :							List[Any]    		=pipe(**UpperCAmelCase__   )[0]

										lowercase :							Optional[int]    		=np.abs(out - out_latents_inputs   ).max()
										self.assertLess(UpperCAmelCase__		,					1E-4		,					'''passing latents as image input generate different result from passing image'''   )





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







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		str=0   ):
										'''simple docstring'''
										lowercase :							Any    		=torch.manual_seed(UpperCAmelCase__   )
										lowercase :							List[Any]    		=load_image(
										    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''   )
										lowercase :							Optional[int]    		={
										    '''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     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Optional[Any]    		=self.get_inputs()
										lowercase :							Any    		=pipe(**UpperCAmelCase__   ).images
										lowercase :							Union[str, Any]    		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase :							Any    		=np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]   )

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







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__   )
										lowercase :							int    		=LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Tuple    		=self.get_inputs()
										lowercase :							Optional[Any]    		=pipe(**UpperCAmelCase__   ).images
										lowercase :							Any    		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase :							Any    		=np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]   )

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







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__   )
										lowercase :							Dict    		=DDIMScheduler.from_config(pipe.scheduler.config   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							List[Any]    		=self.get_inputs()
										lowercase :							Dict    		=pipe(**UpperCAmelCase__   ).images
										lowercase :							List[str]    		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase :							Optional[int]    		=np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]   )

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							str    		=0

										def callback_fn(UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		torch.FloatTensor   ) -> None:
																lowercase :							str    		=True
																nonlocal number_of_steps
																number_of_steps += 1
																if step == 1:
																						lowercase :							Optional[int]    		=latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						lowercase :							Any    		=latents[0, -3:, -3:, -1]
																						lowercase :							int    		=np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]   )

																						assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
																elif step == 2:
																						lowercase :							Union[str, Any]    		=latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						lowercase :							Dict    		=latents[0, -3:, -3:, -1]
																						lowercase :							Any    		=np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]   )

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

										lowercase :							Union[str, Any]    		=False

										lowercase :							Union[str, Any]    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__		,					torch_dtype=torch.floataa   )
										lowercase :							List[str]    		=pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Optional[int]    		=self.get_inputs()
										pipe(**UpperCAmelCase__		,					callback=UpperCAmelCase__		,					callback_steps=1   )
										assert callback_fn.has_been_called
										assert number_of_steps == 3







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowercase :							Tuple    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__		,					torch_dtype=torch.floataa   )
										lowercase :							List[str]    		=pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing(1   )
										pipe.enable_sequential_cpu_offload()

										lowercase :							Dict    		=self.get_inputs()
										lowercase :							List[Any]    		=pipe(**UpperCAmelCase__   )

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

										lowercase :							Any    		='''timbrooks/instruct-pix2pix'''
										lowercase :							str    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    UpperCAmelCase__		,					safety_checker=UpperCAmelCase__		,					)
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Optional[Any]    		=pipe(**UpperCAmelCase__   )
										lowercase :							Dict    		=output.images[0]

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

										assert image.shape == (504, 504, 3)
										lowercase :							Optional[Any]    		=np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]   )

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










from __future__ import annotations

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase = None   )     ->     list[list[str]]:
					SCREAMING_SNAKE_CASE							:     Dict						=				word_bank or []
					# create a table
					SCREAMING_SNAKE_CASE							:     int						=				len(__lowerCAmelCase   ) + 1

					SCREAMING_SNAKE_CASE							:     list[list[list[str]]]						=				[]
					for _ in range(__lowerCAmelCase   ):
										table.append([]   )
					# seed value
					SCREAMING_SNAKE_CASE							:     Tuple						=				[[]]  # because empty string has empty combination

					# iterate through the indices
					for i in range(__lowerCAmelCase   ):
										# condition
										if table[i] != []:
															for word in word_bank:
																				# slice condition
																				if target[i : i + len(__lowerCAmelCase   )] == word:
																									SCREAMING_SNAKE_CASE							:     list[list[str]]						=				[
																									    [word, *way] for way in table[i]
																									]
																									# adds the word to every combination the current position holds
																									# now,push that combination to the table[i+len(word)]
																									table[i + len(__lowerCAmelCase   )] += new_combinations

    # combinations are in reverse order so reverse for better output
					for combination in table[len(__lowerCAmelCase   )]:
										combination.reverse()

					return table[len(__lowerCAmelCase   )]


if __name__ == "__main__":
			print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
			print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
			print(
			    all_construct(
			        """hexagonosaurus""",
			        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
			    )
			)