class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       Any  ,     __magic_name__ :       str = ""  ,     __magic_name__ :       bool = False   )  ->	None:
        # Mapping from the first character of the prefix of the node
        SCREAMING_SNAKE_CASE_          =  {}

        # A node will be a leaf if the tree contains its word
        SCREAMING_SNAKE_CASE_          =  is_leaf

        SCREAMING_SNAKE_CASE_          =  prefix







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       str   )  ->	tuple[str, str, str]:
        SCREAMING_SNAKE_CASE_          =  0
        for q, w in zip(self.prefix  ,     __magic_name__   ):
              if q != w:
                    break

              x += 1

        return self.prefix[:x], self.prefix[x:], word[x:]







  def    __A     (							self :       Any  ,     __magic_name__ :       list[str]   )  ->	None:
        for word in words:
              self.insert(__magic_name__   )







  def    __A     (							self :       str  ,     __magic_name__ :       str   )  ->	None:
        # Case 1: If the word is the prefix of the node
        # Solution: We set the current node as leaf
        if self.prefix == word:
              SCREAMING_SNAKE_CASE_          =  True

        # Case 2: The node has no edges that have a prefix to the word
        # Solution: We create an edge from the current node to a new one
        # containing the word
        elif word[0] not in self.nodes:
              SCREAMING_SNAKE_CASE_          =  RadixNode(prefix=__magic_name__  ,     is_leaf=__magic_name__   )

        else:
              SCREAMING_SNAKE_CASE_          =  self.nodes[word[0]]
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  incoming_node.match(
                  __magic_name__   )

              # Case 3: The node prefix is equal to the matching
              # Solution: We insert remaining word on the next node
              if remaining_prefix == "":
                    self.nodes[matching_string[0]].insert(__magic_name__   )

              # Case 4: The word is greater equal to the matching
              # Solution: Create a node in between both nodes, change
              # prefixes and add the new node for the remaining word
              else:
                    SCREAMING_SNAKE_CASE_          =  remaining_prefix

                    SCREAMING_SNAKE_CASE_          =  self.nodes[matching_string[0]]
                    SCREAMING_SNAKE_CASE_          =  RadixNode(__magic_name__  ,     __magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  aux_node

                    if remaining_word == "":
                          SCREAMING_SNAKE_CASE_          =  True
                    else:
                          self.nodes[matching_string[0]].insert(__magic_name__   )







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       str   )  ->	bool:
        SCREAMING_SNAKE_CASE_          =  self.nodes.get(word[0]  ,     __magic_name__   )
        if not incoming_node:
              return False
        else:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  incoming_node.match(
                  __magic_name__   )
              # If there is remaining prefix, the word can't be on the tree
              if remaining_prefix != "":
                    return False
              # This applies when the word and the prefix are equal
              elif remaining_word == "":
                    return incoming_node.is_leaf
              # We have word remaining so we check the next node
              else:
                    return incoming_node.find(__magic_name__   )







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       str   )  ->	bool:
        SCREAMING_SNAKE_CASE_          =  self.nodes.get(word[0]  ,     __magic_name__   )
        if not incoming_node:
              return False
        else:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  incoming_node.match(
                  __magic_name__   )
              # If there is remaining prefix, the word can't be on the tree
              if remaining_prefix != "":
                    return False
              # We have word remaining so we check the next node
              elif remaining_word != "":
                    return incoming_node.delete(__magic_name__   )
              else:
                    # If it is not a leaf, we don't have to delete
                    if not incoming_node.is_leaf:
                          return False
                    else:
                          # We delete the nodes if no edges go from it
                          if len(incoming_node.nodes   ) == 0:
                                del self.nodes[word[0]]
                                # We merge the current node with its only child
                                if len(self.nodes   ) == 1 and not self.is_leaf:
                                      SCREAMING_SNAKE_CASE_          =  list(self.nodes.values()   )[0]
                                      SCREAMING_SNAKE_CASE_          =  merging_node.is_leaf
                                      self.prefix += merging_node.prefix
                                      SCREAMING_SNAKE_CASE_          =  merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                          elif len(incoming_node.nodes   ) > 1:
                                SCREAMING_SNAKE_CASE_          =  False
                          # If there is 1 edge, we merge it with its child
                          else:
                                SCREAMING_SNAKE_CASE_          =  list(incoming_node.nodes.values()   )[0]
                                SCREAMING_SNAKE_CASE_          =  merging_node.is_leaf
                                incoming_node.prefix += merging_node.prefix
                                SCREAMING_SNAKE_CASE_          =  merging_node.nodes

                          return True







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       int = 0   )  ->	None:
        if self.prefix != "":
              print("-" * height  ,     self.prefix  ,     "  (leaf)" if self.is_leaf else ""   )

        for value in self.nodes.values():
              value.print_tree(height + 1   )



def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "banana bananas bandana band apple all beast".split()
      SCREAMING_SNAKE_CASE_          =  RadixNode()
      root.insert_many(__UpperCamelCase				)

      assert all(root.find(__UpperCamelCase				) for word in words				)
      assert not root.find("bandanas"				)
      assert not root.find("apps"				)
      root.delete("all"				)
      assert not root.find("all"				)
      root.delete("banana"				)
      assert not root.find("banana"				)
      assert root.find("bananas"				)

      return True


def      a__ (		):
      assert test_trie()


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  RadixNode()
      SCREAMING_SNAKE_CASE_          =  "banana bananas bandanas bandana band apple all beast".split()
      root.insert_many(__UpperCamelCase				)

      print("Words:"     ,      __UpperCamelCase				)
      print("Tree:"				)
      root.print_tree()


if __name__ == "__main__":
      main()



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A     :  List[str]					     =       False


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  pass



@nightly
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       str   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"   )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=2  ,     output_type="numpy"   ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  generator.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=2  ,     output_type="numpy"   ).images

        assert np.abs(image - new_image   ).sum() < 1e-5, "Models don't have the same forward pass"







  def    __A     (							self :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(
            "shi-labs/versatile-diffusion"  ,     torch_dtype=torch.floataa   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=50  ,     output_type="numpy"   ).images

        SCREAMING_SNAKE_CASE_          =  image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        SCREAMING_SNAKE_CASE_          =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]   )

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


