import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @property
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Union[str, Any]:
          torch.manual_seed(0      )
          lowerCamelCase__       :						int        =							UNetaDModel(
              block_out_channels=(3_2, 6_4)		, layers_per_block=2		, sample_size=3_2		, in_channels=3		, out_channels=3		, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		, up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		, )
          return model







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Tuple:
          lowerCamelCase__       :						Optional[Any]        =							self.dummy_uncond_unet
          lowerCamelCase__       :						Tuple        =							KarrasVeScheduler()

          lowerCamelCase__       :						Tuple        =							KarrasVePipeline(unet=_snake_case		, scheduler=_snake_case      )
          pipe.to(_snake_case      )
          pipe.set_progress_bar_config(disable=_snake_case      )

          lowerCamelCase__       :						Optional[int]        =							torch.manual_seed(0      )
          lowerCamelCase__       :						List[Any]        =							pipe(num_inference_steps=2		, generator=_snake_case		, output_type='''numpy'''      ).images

          lowerCamelCase__       :						str        =							torch.manual_seed(0      )
          lowerCamelCase__       :						Any        =							pipe(num_inference_steps=2		, generator=_snake_case		, output_type='''numpy'''		, return_dict=_snake_case      )[0]

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

          assert image.shape == (1, 3_2, 3_2, 3)
          lowerCamelCase__       :						Union[str, Any]        =							np.array([0.0, 1.0, 0.0, 0.0, 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        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						List[str]        =							"google/ncsnpp-celebahq-256"
          lowerCamelCase__       :						List[str]        =							UNetaDModel.from_pretrained(_snake_case      )
          lowerCamelCase__       :						Optional[int]        =							KarrasVeScheduler()

          lowerCamelCase__       :						Optional[int]        =							KarrasVePipeline(unet=_snake_case		, scheduler=_snake_case      )
          pipe.to(_snake_case      )
          pipe.set_progress_bar_config(disable=_snake_case      )

          lowerCamelCase__       :						Tuple        =							torch.manual_seed(0      )
          lowerCamelCase__       :						str        =							pipe(num_inference_steps=2_0		, generator=_snake_case		, output_type='''numpy'''      ).images

          lowerCamelCase__       :						int        =							image[0, -3:, -3:, -1]
          assert image.shape == (1, 2_5_6, 2_5_6, 3)
          lowerCamelCase__       :						Optional[int]        =							np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]      )

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










from __future__ import annotations

_A :   List[str]  			=  '#'


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]      )       ->None:
          lowerCamelCase__       :						dict        =							{}







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     str      )       ->None:
          lowerCamelCase__       :						Any        =							self._trie
          for char in text:
             if char not in trie:
                lowerCamelCase__       :						Any        =							{}
             lowerCamelCase__       :						Any        =							trie[char]
          lowerCamelCase__       :						List[str]        =							True







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     str      )       ->tuple | list:
          lowerCamelCase__       :						Dict        =							self._trie
          for char in prefix:
             if char in trie:
                lowerCamelCase__       :						List[Any]        =							trie[char]
             else:
                return []
          return self._elements(A      )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     dict      )       ->tuple:
          lowerCamelCase__       :						Optional[Any]        =							[]
          for c, v in d.items():
             lowerCamelCase__       :						Any        =							[''' '''] if c == END else [(c + s) for s in self._elements(A      )]
             result.extend(A      )
          return tuple(A      )


_A :   str  			=  Trie()
_A :   List[Any]  			=  ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
       trie.insert_word(word)



def 	_a						(   UpperCAmelCase					)    ->    tuple:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							trie.find_word(UpperCAmelCase					)
   return tuple(string + word for word in suffixes					)



def 	_a						(   )    ->    None:







   """simple docstring"""
   print(autocomplete_using_trie('''de'''					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()



