
from __future__ import annotations





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = None    ):
				lowerCamelCase_						=			word_bank or []
				# create a table
				lowerCamelCase_						=			len(lowerCamelCase__    ) + 1

				lowerCamelCase_						=			[]
				for _ in range(lowerCamelCase__    ):
								table.append([]    )
				# seed value
				lowerCamelCase_						=			[[]]  # 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:
																				lowerCamelCase_						=			[
																				    [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'''],
			    )
			)



'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  UNetaDModel(
					    block_out_channels=(32, 64)       ,    layers_per_block=2       ,    sample_size=32       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")       ,    up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")       ,    )
					return model







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_uncond_unet
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy"""       ,    return_dict=__lowerCAmelCase)[0]

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]
					lowerCAmelCase   					=  image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])

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







@slow
@require_torch
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """google/ddpm-cifar10-32"""

					lowerCAmelCase   					=  UNetaDModel.from_pretrained(__lowerCAmelCase)
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)
					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])

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




