


"""simple docstring"""


class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self	):
										__lowerCAmelCase				    =			{}  # Mapping from char to TrieNode
										__lowerCAmelCase				    =			False

							def 			snake_case				(     self    ,   __a	):
										for word in words:
													self.insert(__a	)

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			self
										for char in word:
													if char not in curr.nodes:
																__lowerCAmelCase				    =			TrieNode()
													__lowerCAmelCase				    =			curr.nodes[char]
										__lowerCAmelCase				    =			True

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			self
										for char in word:
													if char not in curr.nodes:
																return False
													__lowerCAmelCase				    =			curr.nodes[char]
										return curr.is_leaf



							def 			snake_case				(     self    ,   __a	):

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

										_delete(self    ,   __a    ,   0	)


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if node.is_leaf:
						print(a_    , end=" "      )

			for key, value in node.nodes.items():
						print_words(a_    , word + key      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"banana bananas bandana band apple all beast".split()
			__lowerCAmelCase				    =			TrieNode()
			root.insert_many(a_      )
			# print_words(root, "")
			assert all(root.find(a_      ) 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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			print(str(a_      )    , "works!" if passes else "doesn't work :("      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert test_trie()


def _lowerCamelCase	(		):

			'''simple docstring'''




			print_results("Testing trie functionality"    , test_trie()      )


if __name__ == "__main__":
	main()






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: str							,A					: float = 0.02							,A					: float = 1_00							,A					: float = 1.0_07							,A					: float = 80							,A					: float = 0.05							,A					: float = 50							,):
					# standard deviation of the initial noise distribution
					__A   			= sigma_max

					# setable values
					__A   			= None
					__A   			= None
					__A   			= None  # sigma(t_i)






	def 				UpperCamelCase_  (       self					: str							,A					: torch.FloatTensor							,A					: Optional[int] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Union[str, torch.device] = None ):
					__A   			= num_inference_steps
					__A   			= np.arange(0							,self.num_inference_steps )[::-1].copy()
					__A   			= torch.from_numpy(A ).to(A )
					__A   			= [
					    (
					        self.config.sigma_max**2
					        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
					    )
					    for i in self.timesteps
					]
					__A   			= torch.tensor(A							,dtype=torch.floataa							,device=A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: float							,A					: Optional[torch.Generator] = None ):
					if self.config.s_min <= sigma <= self.config.s_max:
									__A   			= min(self.config.s_churn / self.num_inference_steps							,2**0.5 - 1 )
					else:
									__A   			= 0

					# sample eps ~ N(0, S_noise^2 * I)
					__A   			= self.config.s_noise * randn_tensor(sample.shape							,generator=A ).to(sample.device )
					__A   			= sigma + gamma * sigma
					__A   			= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






	def 				UpperCamelCase_  (       self					: Dict							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_hat + sigma_hat * model_output
					__A   			= (sample_hat - pred_original_sample) / sigma_hat
					__A   			= sample_hat + (sigma_prev - sigma_hat) * derivative

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_prev + sigma_prev * model_output
					__A   			= (sample_prev - pred_original_sample) / sigma_prev
					__A   			= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: str ):
					raise NotImplementedError()
