'''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 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @property
    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       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       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_uncond_unet
         SCREAMING_SNAKE_CASE					:			Tuple    	=       PNDMScheduler()

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       PNDMPipeline(unet=lowercase__    ,							scheduler=lowercase__ )
         pndm.to(lowercase__ )
         pndm.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       pndm(generator=lowercase__    ,							num_inference_steps=20    ,							output_type='numpy' ).images

         SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       pndm(generator=lowercase__    ,							num_inference_steps=20    ,							output_type='numpy'    ,							return_dict=lowercase__ )[0]

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

         assert image.shape == (1, 32, 32, 3)
         SCREAMING_SNAKE_CASE					:			str    	=       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 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Dict    	=       'google/ddpm-cifar10-32'

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       UNetaDModel.from_pretrained(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       PNDMScheduler()

         SCREAMING_SNAKE_CASE					:			Any    	=       PNDMPipeline(unet=lowercase__    ,							scheduler=lowercase__ )
         pndm.to(lowercase__ )
         pndm.set_progress_bar_config(disable=lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pndm(generator=lowercase__    ,							output_type='numpy' ).images

         SCREAMING_SNAKE_CASE					:			int    	=       image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         SCREAMING_SNAKE_CASE					:			Tuple    	=       np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5] )

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




'''simple docstring'''





from __future__ import annotations


def     __lowerCAmelCase						(       a_       ,      a_ = None		)      ->	list[list[str]]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       word_bank or []
     # create a table
     SCREAMING_SNAKE_CASE					:			int    	=       len(a_		) + 1

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

     # iterate through the indices
     for i in range(a_		):
          # condition
          if table[i] != []:
               for word in word_bank:
                    # slice condition
                    if target[i : i + len(a_		)] == 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(a_		)] += new_combinations

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

     return table[len(a_		)]


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"""],
     )
 )



