




"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class       lowerCAmelCase__       (			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      KandinskyInpaintPipeline
				__UpperCAmelCase :     int       =      ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
				__UpperCAmelCase :     Union[str, Any]       =      [
				    "prompt",
				    "negative_prompt",
				    "image_embeds",
				    "negative_image_embeds",
				    "image",
				    "mask_image",
				]
				__UpperCAmelCase :     Optional[Any]       =      [
				    "generator",
				    "height",
				    "width",
				    "latents",
				    "guidance_scale",
				    "negative_prompt",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				__UpperCAmelCase :     Optional[int]       =      False



				@property
				def       snake_case    (     self					:	Tuple						):
						return 3_2



				@property
				def       snake_case    (     self					:	Tuple						):
						return 3_2



				@property
				def       snake_case    (     self					:	Union[str, Any]						):
						return self.time_input_dim



				@property
				def       snake_case    (     self					:	Any						):
						return self.time_input_dim * 4



				@property
				def       snake_case    (     self					:	List[Any]						):
						return 1_0_0



				@property
				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Optional[Any]												=				XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"						)
						return tokenizer



				@property
				def       snake_case    (     self					:	int						):
						torch.manual_seed(0						)
						__lowercase				:		Union[str, Any]												=				MCLIPConfig(
						    numDims=self.cross_attention_dim    ,   transformerDimensions=self.text_embedder_hidden_size    ,   hidden_size=self.text_embedder_hidden_size    ,   intermediate_size=3_7    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   vocab_size=1_0_0_5    ,   )

						__lowercase				:		Optional[Any]												=				MultilingualCLIP(lowercase__						)
						__lowercase				:		Dict												=				text_encoder.eval()

						return text_encoder



				@property
				def       snake_case    (     self					:	int						):
						torch.manual_seed(0						)

						__lowercase				:		str												=				{
						    "in_channels": 9,
						    # Out channels is double in channels because predicts mean and variance
						    "out_channels": 8,
						    "addition_embed_type": "text_image",
						    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
						    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
						    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "layers_per_block": 1,
						    "encoder_hid_dim": self.text_embedder_hidden_size,
						    "encoder_hid_dim_type": "text_image_proj",
						    "cross_attention_dim": self.cross_attention_dim,
						    "attention_head_dim": 4,
						    "resnet_time_scale_shift": "scale_shift",
						    "class_embed_type": None,
						}

						__lowercase				:		Any												=				UNetaDConditionModel(**lowercase__						)
						return model



				@property
				def       snake_case    (     self					:	Union[str, Any]						):
						return {
						    "block_out_channels": [3_2, 6_4],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}



				@property
				def       snake_case    (     self					:	List[Any]						):
						torch.manual_seed(0						)
						__lowercase				:		Tuple												=				VQModel(**self.dummy_movq_kwargs						)
						return model



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Optional[Any]												=				self.dummy_text_encoder
						__lowercase				:		Dict												=				self.dummy_tokenizer
						__lowercase				:		List[str]												=				self.dummy_unet
						__lowercase				:		Tuple												=				self.dummy_movq

						__lowercase				:		Optional[int]												=				DDIMScheduler(
						    num_train_timesteps=1_0_0_0    ,   beta_schedule="linear"    ,   beta_start=0.0_0_0_8_5    ,   beta_end=0.0_1_2    ,   clip_sample=lowercase__    ,   set_alpha_to_one=lowercase__    ,   steps_offset=1    ,   prediction_type="epsilon"    ,   thresholding=lowercase__    ,   )

						__lowercase				:		List[str]												=				{
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}

						return components



				def       snake_case    (     self					:	Tuple    ,   lowercase__					:	Tuple    ,   lowercase__					:	Union[str, Any]=0						):
						__lowercase				:		List[str]												=				floats_tensor((1, self.cross_attention_dim)    ,   rng=random.Random(lowercase__						)						).to(lowercase__						)
						__lowercase				:		Tuple												=				floats_tensor((1, self.cross_attention_dim)    ,   rng=random.Random(seed + 1						)						).to(lowercase__						)
						# create init_image
						__lowercase				:		Union[str, Any]												=				floats_tensor((1, 3, 6_4, 6_4)    ,   rng=random.Random(lowercase__						)						).to(lowercase__						)
						__lowercase				:		Optional[int]												=				image.cpu().permute(0    ,   2    ,   3    ,   1						)[0]
						__lowercase				:		Optional[Any]												=				Image.fromarray(np.uinta(lowercase__						)						).convert("RGB"						).resize((2_5_6, 2_5_6)						)
						# create mask
						__lowercase				:		List[Any]												=				np.ones((6_4, 6_4)    ,   dtype=np.floataa						)
						__lowercase				:		Optional[int]												=				0

						if str(lowercase__						).startswith("mps"						):
								__lowercase				:		str												=				torch.manual_seed(lowercase__						)
						else:
								__lowercase				:		str												=				torch.Generator(device=lowercase__						).manual_seed(lowercase__						)
						__lowercase				:		Union[str, Any]												=				{
						    "prompt": "horse",
						    "image": init_image,
						    "mask_image": mask,
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "generator": generator,
						    "height": 6_4,
						    "width": 6_4,
						    "num_inference_steps": 2,
						    "guidance_scale": 4.0,
						    "output_type": "np",
						}
						return inputs



				def       snake_case    (     self					:	Optional[int]						):
						__lowercase				:		Any												=				"cpu"

						__lowercase				:		Optional[Any]												=				self.get_dummy_components()

						__lowercase				:		List[Any]												=				self.pipeline_class(**lowercase__						)
						__lowercase				:		Union[str, Any]												=				pipe.to(lowercase__						)

						pipe.set_progress_bar_config(disable=lowercase__						)

						__lowercase				:		List[str]												=				pipe(**self.get_dummy_inputs(lowercase__						)						)
						__lowercase				:		int												=				output.images

						__lowercase				:		Dict												=				pipe(
						    **self.get_dummy_inputs(lowercase__						)    ,   return_dict=lowercase__    ,   )[0]

						__lowercase				:		str												=				image[0, -3:, -3:, -1]
						__lowercase				:		List[str]												=				image_from_tuple[0, -3:, -3:, -1]

						print(f'image.shape {image.shape}'						)

						assert image.shape == (1, 6_4, 6_4, 3)

						__lowercase				:		Dict												=				np.array(
						    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]						)

						assert (
						    np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
						), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
						), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'



				def       snake_case    (     self					:	List[Any]						):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3						)





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


				"""simple docstring"""





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



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		str												=				load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"						)

						__lowercase				:		List[Any]												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"						)
						__lowercase				:		Optional[Any]												=				np.ones((7_6_8, 7_6_8)    ,   dtype=np.floataa						)
						__lowercase				:		Tuple												=				0

						__lowercase				:		Union[str, Any]												=				"a hat"

						__lowercase				:		Optional[Any]												=				KandinskyPriorPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1-prior"    ,   torch_dtype=torch.floataa						)
						pipe_prior.to(lowercase__						)

						__lowercase				:		Tuple												=				KandinskyInpaintPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1-inpaint"    ,   torch_dtype=torch.floataa						)
						__lowercase				:		Optional[Any]												=				pipeline.to(lowercase__						)
						pipeline.set_progress_bar_config(disable=lowercase__						)

						__lowercase				:		Any												=				torch.Generator(device="cpu"						).manual_seed(0						)
						__lowercase						,__lowercase				:		str												=				pipe_prior(
						    lowercase__    ,   generator=lowercase__    ,   num_inference_steps=5    ,   negative_prompt=""    ,   ).to_tuple()

						__lowercase				:		int												=				pipeline(
						    lowercase__    ,   image=lowercase__    ,   mask_image=lowercase__    ,   image_embeds=lowercase__    ,   negative_image_embeds=lowercase__    ,   generator=lowercase__    ,   num_inference_steps=1_0_0    ,   height=7_6_8    ,   width=7_6_8    ,   output_type="np"    ,   )

						__lowercase				:		str												=				output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(lowercase__    ,   lowercase__						)






"""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'],
						    )
						)
