




"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	List[str]						):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



				@property
				def       snake_case    (     self					:	Optional[int]						):
						__lowercase				:		Union[str, Any]												=				1
						__lowercase				:		Dict												=				3
						__lowercase				:		str												=				(3_2, 3_2)

						__lowercase				:		List[Any]												=				floats_tensor((batch_size, num_channels) + sizes    ,   rng=random.Random(0						)						).to(lowercase__						)
						return image



				@property
				def       snake_case    (     self					:	List[str]						):
						torch.manual_seed(0						)
						__lowercase				:		Union[str, Any]												=				UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)    ,   layers_per_block=2    ,   sample_size=3_2    ,   in_channels=4    ,   out_channels=4    ,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,   cross_attention_dim=3_2    ,   )
						return model



				@property
				def       snake_case    (     self					:	Dict						):
						torch.manual_seed(0						)
						__lowercase				:		Union[str, Any]												=				AutoencoderKL(
						    block_out_channels=[3_2, 6_4]    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,   latent_channels=4    ,   )
						return model



				@property
				def       snake_case    (     self					:	int						):
						torch.manual_seed(0						)
						__lowercase				:		str												=				RobertaSeriesConfig(
						    hidden_size=3_2    ,   project_dim=3_2    ,   intermediate_size=3_7    ,   layer_norm_eps=1e-05    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=5_0_0_6    ,   )
						return RobertaSeriesModelWithTransformation(lowercase__						)



				@property
				def       snake_case    (     self					:	Any						):
						def extract(*lowercase__					:	Any    ,   **lowercase__					:	List[Any]						):




								class       lowerCAmelCase__       :


												"""simple docstring"""





												def __init__(     self					:	Tuple						):
														__lowercase				:		Dict												=				torch.ones([0]						)



												def       snake_case    (     self					:	Tuple    ,   lowercase__					:	Optional[int]						):
														self.pixel_values.to(lowercase__						)
														return self





								return Out()

						return extract



				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		Dict												=				"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowercase				:		Optional[Any]												=				self.dummy_cond_unet
						__lowercase				:		int												=				PNDMScheduler(skip_prk_steps=lowercase__						)
						__lowercase				:		Union[str, Any]												=				self.dummy_vae
						__lowercase				:		List[Any]												=				self.dummy_text_encoder
						__lowercase				:		str												=				XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"						)
						__lowercase				:		Optional[Any]												=				7_7

						__lowercase				:		Optional[Any]												=				self.dummy_image.to(lowercase__						)
						__lowercase				:		List[Any]												=				init_image / 2 + 0.5

						# make sure here that pndm scheduler skips prk
						__lowercase				:		Dict												=				AltDiffusionImgaImgPipeline(
						    unet=lowercase__    ,   scheduler=lowercase__    ,   vae=lowercase__    ,   text_encoder=lowercase__    ,   tokenizer=lowercase__    ,   safety_checker=lowercase__    ,   feature_extractor=self.dummy_extractor    ,   )
						__lowercase				:		str												=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor    ,   do_normalize=lowercase__						)
						__lowercase				:		str												=				alt_pipe.to(lowercase__						)
						alt_pipe.set_progress_bar_config(disable=lowercase__						)

						__lowercase				:		int												=				"A painting of a squirrel eating a burger"
						__lowercase				:		str												=				torch.Generator(device=lowercase__						).manual_seed(0						)
						__lowercase				:		List[Any]												=				alt_pipe(
						    [prompt]    ,   generator=lowercase__    ,   guidance_scale=6.0    ,   num_inference_steps=2    ,   output_type="np"    ,   image=lowercase__    ,   )

						__lowercase				:		List[str]												=				output.images

						__lowercase				:		int												=				torch.Generator(device=lowercase__						).manual_seed(0						)
						__lowercase				:		str												=				alt_pipe(
						    [prompt]    ,   generator=lowercase__    ,   guidance_scale=6.0    ,   num_inference_steps=2    ,   output_type="np"    ,   image=lowercase__    ,   return_dict=lowercase__    ,   )[0]

						__lowercase				:		Optional[int]												=				image[0, -3:, -3:, -1]
						__lowercase				:		Any												=				image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						__lowercase				:		Any												=				np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]						)

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



				@unittest.skipIf(torch_device != "cuda"    ,   "This test requires a GPU"						)
				def       snake_case    (     self					:	Any						):
						__lowercase				:		Optional[Any]												=				self.dummy_cond_unet
						__lowercase				:		Tuple												=				PNDMScheduler(skip_prk_steps=lowercase__						)
						__lowercase				:		Union[str, Any]												=				self.dummy_vae
						__lowercase				:		Any												=				self.dummy_text_encoder
						__lowercase				:		List[Any]												=				XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"						)
						__lowercase				:		List[str]												=				7_7

						__lowercase				:		Any												=				self.dummy_image.to(lowercase__						)

						# put models in fp16
						__lowercase				:		Optional[Any]												=				unet.half()
						__lowercase				:		Dict												=				vae.half()
						__lowercase				:		List[str]												=				bert.half()

						# make sure here that pndm scheduler skips prk
						__lowercase				:		Tuple												=				AltDiffusionImgaImgPipeline(
						    unet=lowercase__    ,   scheduler=lowercase__    ,   vae=lowercase__    ,   text_encoder=lowercase__    ,   tokenizer=lowercase__    ,   safety_checker=lowercase__    ,   feature_extractor=self.dummy_extractor    ,   )
						__lowercase				:		Optional[int]												=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor    ,   do_normalize=lowercase__						)
						__lowercase				:		Optional[int]												=				alt_pipe.to(lowercase__						)
						alt_pipe.set_progress_bar_config(disable=lowercase__						)

						__lowercase				:		Dict												=				"A painting of a squirrel eating a burger"
						__lowercase				:		Tuple												=				torch.manual_seed(0						)
						__lowercase				:		str												=				alt_pipe(
						    [prompt]    ,   generator=lowercase__    ,   num_inference_steps=2    ,   output_type="np"    ,   image=lowercase__    ,   ).images

						assert image.shape == (1, 3_2, 3_2, 3)



				@unittest.skipIf(torch_device != "cuda"    ,   "This test requires a GPU"						)
				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Any												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/img2img/sketch-mountains-input.jpg"						)
						# resize to resolution that is divisible by 8 but not 16 or 32
						__lowercase				:		Optional[Any]												=				init_image.resize((7_6_0, 5_0_4)						)

						__lowercase				:		List[str]												=				"BAAI/AltDiffusion"
						__lowercase				:		Tuple												=				AltDiffusionImgaImgPipeline.from_pretrained(
						    lowercase__    ,   safety_checker=lowercase__    ,   )
						pipe.to(lowercase__						)
						pipe.set_progress_bar_config(disable=lowercase__						)
						pipe.enable_attention_slicing()

						__lowercase				:		List[str]												=				"A fantasy landscape, trending on artstation"

						__lowercase				:		str												=				torch.manual_seed(0						)
						__lowercase				:		List[str]												=				pipe(
						    prompt=lowercase__    ,   image=lowercase__    ,   strength=0.7_5    ,   guidance_scale=7.5    ,   generator=lowercase__    ,   output_type="np"    ,   )
						__lowercase				:		int												=				output.images[0]

						__lowercase				:		Dict												=				image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert image.shape == (5_0_4, 7_6_0, 3)
						__lowercase				:		Union[str, Any]												=				np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]						)

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





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


				"""simple docstring"""





				def       snake_case    (     self					:	Optional[int]						):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		List[Any]												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/img2img/sketch-mountains-input.jpg"						)
						__lowercase				:		str												=				init_image.resize((7_6_8, 5_1_2)						)
						__lowercase				:		Optional[int]												=				load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"						)

						__lowercase				:		str												=				"BAAI/AltDiffusion"
						__lowercase				:		int												=				AltDiffusionImgaImgPipeline.from_pretrained(
						    lowercase__    ,   safety_checker=lowercase__    ,   )
						pipe.to(lowercase__						)
						pipe.set_progress_bar_config(disable=lowercase__						)
						pipe.enable_attention_slicing()

						__lowercase				:		int												=				"A fantasy landscape, trending on artstation"

						__lowercase				:		str												=				torch.manual_seed(0						)
						__lowercase				:		str												=				pipe(
						    prompt=lowercase__    ,   image=lowercase__    ,   strength=0.7_5    ,   guidance_scale=7.5    ,   generator=lowercase__    ,   output_type="np"    ,   )
						__lowercase				:		Tuple												=				output.images[0]

						assert image.shape == (5_1_2, 7_6_8, 3)
						# img2img is flaky across GPUs even in fp32, so using MAE here
						assert np.abs(expected_image - image						).max() < 1e-2






"""simple docstring"""







from __future__ import annotations

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->list[list[str]]:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				word_bank or []
		# create a table
		__lowercase				:		int												=				len(_lowerCamelCase				) + 1

		__lowercase				:		list[list[list[str]]]												=				[]
		for _ in range(_lowerCamelCase				):
				table.append([]				)
		# seed value
		__lowercase				:		Any												=				[[]]  # 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:
										__lowercase				:		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'],
						    )
						)
