




class 		__A   :

						def __init__(		self :Any     ,			__snake_case :str = ""     ,			__snake_case :bool = False			):
										'''simple docstring'''

										__magic_name__						:						dict[str, RadixNode]										={}

										# A node will be a leaf if the tree contains its word
										__magic_name__						:						Tuple										=is_leaf

										__magic_name__						:						Optional[Any]										=prefix

						def      A__    (		self :Tuple     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=0
										for q, w in zip(self.prefix     ,			__snake_case			):
														if q != w:
																		break

														x += 1

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

						def      A__    (		self :Optional[int]     ,			__snake_case :list[str]			):
										'''simple docstring'''

										for word in words:
														self.insert(__snake_case			)

						def      A__    (		self :List[Any]     ,			__snake_case :str			):
										'''simple docstring'''

										if self.prefix == word:
														__magic_name__						:						str										=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:
														__magic_name__						:						Optional[int]										=RadixNode(prefix=__snake_case     ,			is_leaf=__snake_case			)

										else:
														__magic_name__						:						Union[str, Any]										=self.nodes[word[0]]
														__magic_name__					,			__magic_name__					,			__magic_name__						:						Optional[int]										=incoming_node.match(
														    __snake_case			)

														# 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(__snake_case			)

														# 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:
																		__magic_name__						:						Tuple										=remaining_prefix

																		__magic_name__						:						Optional[Any]										=self.nodes[matching_string[0]]
																		__magic_name__						:						Optional[Any]										=RadixNode(__snake_case     ,			__snake_case			)
																		__magic_name__						:						int										=aux_node

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

						def      A__    (		self :Any     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.nodes.get(word[0]     ,			__snake_case			)
										if not incoming_node:
														return False
										else:
														__magic_name__					,			__magic_name__					,			__magic_name__						:						Dict										=incoming_node.match(
														    __snake_case			)
														# 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(__snake_case			)

						def      A__    (		self :Optional[Any]     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.nodes.get(word[0]     ,			__snake_case			)
										if not incoming_node:
														return False
										else:
														__magic_name__					,			__magic_name__					,			__magic_name__						:						int										=incoming_node.match(
														    __snake_case			)
														# 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(__snake_case			)
														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:
																														__magic_name__						:						List[Any]										=list(self.nodes.values()			)[0]
																														__magic_name__						:						List[str]										=merging_node.is_leaf
																														self.prefix += merging_node.prefix
																														__magic_name__						:						List[str]										=merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																						elif len(incoming_node.nodes			) > 1:
																										__magic_name__						:						str										=False
																						# If there is 1 edge, we merge it with its child
																						else:
																										__magic_name__						:						Optional[Any]										=list(incoming_node.nodes.values()			)[0]
																										__magic_name__						:						List[Any]										=merging_node.is_leaf
																										incoming_node.prefix += merging_node.prefix
																										__magic_name__						:						int										=merging_node.nodes

																						return True


						def      A__    (		self :Tuple     ,			__snake_case :int = 0			):
										'''simple docstring'''

										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 		lowerCAmelCase_ (						):
				__magic_name__						:						List[Any]										="""banana bananas bandana band apple all beast""".split()
				__magic_name__						:						Dict										=RadixNode()
				root.insert_many(lowerCamelCase )

				assert all(root.find(lowerCamelCase ) 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 		lowerCAmelCase_ (						):
				assert test_trie()




def 		lowerCAmelCase_ (						):
				__magic_name__						:						Optional[int]										=RadixNode()
				__magic_name__						:						List[str]										="""banana bananas bandanas bandana band apple all beast""".split()
				root.insert_many(lowerCamelCase )

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


if __name__ == "__main__":
							main()










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




