

'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def __init__(       self  , snake_case_  , snake_case_   ):
   super().__init__()

   # make sure scheduler can always be converted to DDIM
   lowercase									=DDIMScheduler.from_config(scheduler.config   )

   self.register_modules(unet=snake_case_  , scheduler=snake_case_   )







 @torch.no_grad()
 def __call__(       self  , snake_case_ = 1  , snake_case_ = None  , snake_case_ = 0.0  , snake_case_ = 50  , snake_case_ = None  , snake_case_ = "pil"  , snake_case_ = True  , ):

   # Sample gaussian noise to begin loop
   if isinstance(self.unet.config.sample_size  , snake_case_   ):
     lowercase									=(
         batch_size,
         self.unet.config.in_channels,
         self.unet.config.sample_size,
         self.unet.config.sample_size,
     )
   else:
     lowercase									=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

   if isinstance(snake_case_  , snake_case_   ) and len(snake_case_   ) != batch_size:
     raise ValueError(
         f'You have passed a list of generators of length {len(snake_case_   )}, but requested an effective batch'
         f' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

   lowercase									=randn_tensor(snake_case_  , generator=snake_case_  , device=self.device  , dtype=self.unet.dtype   )

   # set step values
   self.scheduler.set_timesteps(snake_case_   )

   for t in self.progress_bar(self.scheduler.timesteps   ):
     # 1. predict noise model_output
     lowercase									=self.unet(snake_case_  , snake_case_   ).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     lowercase									=self.scheduler.step(
         snake_case_  , snake_case_  , snake_case_  , eta=snake_case_  , use_clipped_model_output=snake_case_  , generator=snake_case_   ).prev_sample

   lowercase									=(image / 2 + 0.5).clamp(0  , 1   )
   lowercase									=image.cpu().permute(0  , 2  , 3  , 1   ).numpy()
   if output_type == "pil":
     lowercase									=self.numpy_to_pil(snake_case_   )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=snake_case_   )

class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self ):
       lowerCAmelCase_:							dict[str, TrieNode]       =	{}  # Mapping from char to TrieNode
       lowerCAmelCase_:							str       =	False




    def      _a    (    self       ,						lowerCamelCase__ ):
       for word in words:
          self.insert(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	self
       for char in word:
          if char not in curr.nodes:
             lowerCAmelCase_:							List[Any]       =	TrieNode()
          lowerCAmelCase_:							Optional[Any]       =	curr.nodes[char]
       lowerCAmelCase_:							Dict       =	True




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	self
       for char in word:
          if char not in curr.nodes:
             return False
          lowerCAmelCase_:							Dict       =	curr.nodes[char]
       return curr.is_leaf




    def      _a    (    self       ,						lowerCamelCase__ ):

       def _delete(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ) -> bool:
          if index == len(lowerCamelCase__ ):
             # If word does not exist
             if not curr.is_leaf:
                return False
             lowerCAmelCase_:							List[str]       =	False
             return len(curr.nodes ) == 0
          lowerCAmelCase_:							Union[str, Any]       =	word[index]
          lowerCAmelCase_:							Optional[int]       =	curr.nodes.get(lowerCamelCase__ )
          # If char not in current trie node
          if not char_node:
             return False
          # Flag to check if node can be deleted
          lowerCAmelCase_:							List[Any]       =	_delete(lowerCamelCase__       ,						lowerCamelCase__       ,						index + 1 )
          if delete_curr:
             del curr.nodes[char]
             return len(curr.nodes ) == 0
          return delete_curr

       _delete(self       ,						lowerCamelCase__       ,						0 )





def 		snake_case__	(		lowercase							,				lowercase					):
   if node.is_leaf:
      print(lowercase							,				end=" "					)

   for key, value in node.nodes.items():
      print_words(lowercase							,				word + key					)
def 		snake_case__	(		):
   lowerCAmelCase_:							Any       =	"banana bananas bandana band apple all beast".split()
   lowerCAmelCase_:							Optional[Any]       =	TrieNode()
   root.insert_many(lowercase					)
   # print_words(root, "")
   assert all(root.find(lowercase					) for word in words					)
   assert root.find("banana"					)
   assert not root.find("bandanas"					)
   assert not root.find("apps"					)
   assert root.find("apple"					)
   assert root.find("all"					)
   root.delete("all"					)
   assert not root.find("all"					)
   root.delete("banana"					)
   assert not root.find("banana"					)
   assert root.find("bananas"					)
   return True
def 		snake_case__	(		lowercase							,				lowercase					):
   print(str(lowercase					)							,				"works!" if passes else "doesn't work :("					)
def 		snake_case__	(		):
   assert test_trie()
def 		snake_case__	(		):
   print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
 main()