






'''simple docstring'''




import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
		import jax.numpy as jnp







class       __lowercase					(__lowerCamelCase    ):

	def __get__(      self   :						Any  ,					UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						Any=None):
			# See docs.python.org/3/howto/descriptor.html#properties
			if obj is None:
					return self
			if self.fget is None:
					raise AttributeError('unreadable attribute')
			UpperCamelCase__				:					List[str]					=					'__cached_' + self.fget.__name__
			UpperCamelCase__				:					Dict					=					getattr(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)
			if cached is None:
					UpperCamelCase__				:					Union[str, Any]					=					self.fget(UpperCAmelCase_)
					setattr(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)
			return cached







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[int]:
		UpperCamelCase__				:					str					=					val.lower()
		if val in {"y", "yes", "t", "true", "on", "1"}:
				return 1
		if val in {"n", "no", "f", "false", "off", "0"}:
				return 0
		raise ValueError(f'invalid truth value {val!r}')







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		if is_torch_fx_proxy(lowerCamelCase_):
				return True
		if is_torch_available():
				import torch

				if isinstance(lowerCamelCase_     ,      torch.Tensor):
						return True
		if is_tf_available():
				import tensorflow as tf

				if isinstance(lowerCamelCase_     ,      tf.Tensor):
						return True

		if is_flax_available():
				import jax.numpy as jnp
				from jax.core import Tracer

				if isinstance(lowerCamelCase_     ,      (jnp.ndarray, Tracer)):
						return True

		return isinstance(lowerCamelCase_     ,      np.ndarray)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		return isinstance(lowerCamelCase_     ,      np.ndarray)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[Any]:
		return _is_numpy(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		import torch

		return isinstance(lowerCamelCase_     ,      torch.Tensor)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		return False if not is_torch_available() else _is_torch(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[Any]:
		import torch

		return isinstance(lowerCamelCase_     ,      torch.device)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Dict:
		return False if not is_torch_available() else _is_torch_device(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Any:
		import torch

		if isinstance(lowerCamelCase_     ,      lowerCamelCase_):
				if hasattr(lowerCamelCase_     ,      lowerCamelCase_):
						UpperCamelCase__				:					Optional[Any]					=					getattr(lowerCamelCase_     ,      lowerCamelCase_)
				else:
						return False
		return isinstance(lowerCamelCase_     ,      torch.dtype)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Dict:
		return False if not is_torch_available() else _is_torch_dtype(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Dict:
		import tensorflow as tf

		return isinstance(lowerCamelCase_     ,      tf.Tensor)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		return False if not is_tf_available() else _is_tensorflow(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		import tensorflow as tf

		# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
		if hasattr(lowerCamelCase_     ,      'is_symbolic_tensor'):
				return tf.is_symbolic_tensor(lowerCamelCase_)
		return type(lowerCamelCase_) == tf.Tensor







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		import jax.numpy as jnp  # noqa: F811

		return isinstance(lowerCamelCase_     ,      jnp.ndarray)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[int]:
		return False if not is_flax_available() else _is_jax(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		if isinstance(lowerCamelCase_     ,      (dict, UserDict)):
				return {k: to_py_obj(lowerCamelCase_) for k, v in obj.items()}
		elif isinstance(lowerCamelCase_     ,      (list, tuple)):
				return [to_py_obj(lowerCamelCase_) for o in obj]
		elif is_tf_tensor(lowerCamelCase_):
				return obj.numpy().tolist()
		elif is_torch_tensor(lowerCamelCase_):
				return obj.detach().cpu().tolist()
		elif is_jax_tensor(lowerCamelCase_):
				return np.asarray(lowerCamelCase_).tolist()
		elif isinstance(lowerCamelCase_     ,      (np.ndarray, np.number)):  # tolist also works on 0d np arrays
				return obj.tolist()
		else:
				return obj







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		if isinstance(lowerCamelCase_     ,      (dict, UserDict)):
				return {k: to_numpy(lowerCamelCase_) for k, v in obj.items()}
		elif isinstance(lowerCamelCase_     ,      (list, tuple)):
				return np.array(lowerCamelCase_)
		elif is_tf_tensor(lowerCamelCase_):
				return obj.numpy()
		elif is_torch_tensor(lowerCamelCase_):
				return obj.detach().cpu().numpy()
		elif is_jax_tensor(lowerCamelCase_):
				return np.asarray(lowerCamelCase_)
		else:
				return obj







class       __lowercase					(__lowerCamelCase    ):

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Dict					=					fields(self)

			# Safety and consistency checks
			if not len(UpperCAmelCase_):
					raise ValueError(F'{self.__class__.__name__} has no fields.')
			if not all(field.default is None for field in class_fields[1:]):
					raise ValueError(F'{self.__class__.__name__} should not have more than one required field.')

			UpperCamelCase__				:					Optional[int]					=					getattr(self  ,					class_fields[0].name)
			UpperCamelCase__				:					Dict					=					all(getattr(self  ,					field.name) is None for field in class_fields[1:])

			if other_fields_are_none and not is_tensor(UpperCAmelCase_):
					if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
							UpperCamelCase__				:					Tuple					=					first_field.items()
							UpperCamelCase__				:					Tuple					=					True
					else:
							try:
									UpperCamelCase__				:					List[str]					=					iter(UpperCAmelCase_)
									UpperCamelCase__				:					List[Any]					=					True
							except TypeError:
									UpperCamelCase__				:					Any					=					False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
					if first_field_iterator:
							for idx, element in enumerate(UpperCAmelCase_):
									if (
									    not isinstance(UpperCAmelCase_  ,					(list, tuple))
									    or not len(UpperCAmelCase_) == 2
									    or not isinstance(element[0]  ,					UpperCAmelCase_)
									):
											if idx == 0:
													# If we do not have an iterator of key/values, set it as attribute
													UpperCamelCase__				:					int					=					first_field
											else:
													# If we have a mixed iterator, raise an error
													raise ValueError(
													    F'Cannot set key/value for {element}. It needs to be a tuple (key, value).')
											break
									setattr(self  ,					element[0]  ,					element[1])
									if element[1] is not None:
											UpperCamelCase__				:					Optional[int]					=					element[1]
					elif first_field is not None:
							UpperCamelCase__				:					Optional[int]					=					first_field
			else:
					for field in class_fields:
							UpperCamelCase__				:					List[str]					=					getattr(self  ,					field.name)
							if v is not None:
									UpperCamelCase__				:					List[str]					=					v

	def __delitem__(      self   :						int  ,					*UpperCAmelCase_   :						List[str]  ,					**UpperCAmelCase_   :						Dict):
			raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.')

	def      __UpperCamelCase							(      self   :						List[str]  ,					*UpperCAmelCase_   :						str  ,					**UpperCAmelCase_   :						Union[str, Any]):
			raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.')

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					*UpperCAmelCase_   :						Any  ,					**UpperCAmelCase_   :						List[Any]):
			raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.')

	def      __UpperCamelCase							(      self   :						Dict  ,					*UpperCAmelCase_   :						Optional[int]  ,					**UpperCAmelCase_   :						List[str]):
			raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.')

	def __getitem__(      self   :						Any  ,					UpperCAmelCase_   :						str):
			if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					UpperCamelCase__				:					Union[str, Any]					=					dict(self.items())
					return inner_dict[k]
			else:
					return self.to_tuple()[k]

	def __setattr__(      self   :						Any  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Any):
			if name in self.keys() and value is not None:
					# Don't call self.__setitem__ to avoid recursion errors
					super().__setitem__(UpperCAmelCase_  ,					UpperCAmelCase_)
			super().__setattr__(UpperCAmelCase_  ,					UpperCAmelCase_)

	def __setitem__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[Any]):
			# Will raise a KeyException if needed
			super().__setitem__(UpperCAmelCase_  ,					UpperCAmelCase_)
			# Don't call self.__setattr__ to avoid recursion errors
			super().__setattr__(UpperCAmelCase_  ,					UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						List[str]):
			return tuple(self[k] for k in self.keys())







class       __lowercase					(__lowerCamelCase   ,				__lowerCamelCase    ):

	@classmethod
	def      __UpperCamelCase							(      cls   :						Union[str, Any]  ,					UpperCAmelCase_   :						Dict):
			raise ValueError(
			    F'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys())}')







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  '''longest'''
	_lowerCamelCase              =  '''max_length'''
	_lowerCamelCase              =  '''do_not_pad'''







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  '''pt'''
	_lowerCamelCase              =  '''tf'''
	_lowerCamelCase              =  '''np'''
	_lowerCamelCase              =  '''jax'''







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					UpperCAmelCase_   :						List[ContextManager]):
			UpperCamelCase__				:					Optional[Any]					=					context_managers
			UpperCamelCase__				:					List[str]					=					ExitStack()

	def __enter__(      self   :						int):
			for context_manager in self.context_managers:
					self.stack.enter_context(UpperCAmelCase_)





	def __exit__(      self   :						List[Any]  ,					*UpperCAmelCase_   :						Any  ,					**UpperCAmelCase_   :						Optional[int]):
			self.stack.__exit__(*UpperCAmelCase_  ,					**UpperCAmelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		UpperCamelCase__				:					Dict					=					infer_framework(lowerCamelCase_)
		if framework == "tf":
				UpperCamelCase__				:					Union[str, Any]					=					inspect.signature(model_class.call)  # TensorFlow models
		elif framework == "pt":
				UpperCamelCase__				:					Dict					=					inspect.signature(model_class.forward)  # PyTorch models
		else:
				UpperCamelCase__				:					Optional[int]					=					inspect.signature(model_class.__call__)  # Flax models

		for p in signature.parameters:
				if p == "return_loss" and signature.parameters[p].default is True:
						return True

		return False







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		UpperCamelCase__				:					Dict					=					model_class.__name__
		UpperCamelCase__				:					Union[str, Any]					=					infer_framework(lowerCamelCase_)
		if framework == "tf":
				UpperCamelCase__				:					Optional[Any]					=					inspect.signature(model_class.call)  # TensorFlow models
		elif framework == "pt":
				UpperCamelCase__				:					Tuple					=					inspect.signature(model_class.forward)  # PyTorch models
		else:
				UpperCamelCase__				:					Tuple					=					inspect.signature(model_class.__call__)  # Flax models

		if "QuestionAnswering" in model_name:
				return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
		else:
				return [p for p in signature.parameters if "label" in p]







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = ""     ,      lowerCamelCase_ = ".")							->     Optional[Any]:

		def _flatten_dict(lowerCamelCase_     ,      lowerCamelCase_=""     ,      lowerCamelCase_="."):
				for k, v in d.items():
						UpperCamelCase__				:					Any					=					str(lowerCamelCase_) + delimiter + str(lowerCamelCase_) if parent_key else k
						if v and isinstance(lowerCamelCase_     ,      lowerCamelCase_):
								yield from flatten_dict(lowerCamelCase_     ,      lowerCamelCase_     ,      delimiter=lowerCamelCase_).items()
						else:
								yield key, v

		return dict(_flatten_dict(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_))







@contextmanager
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = False)							->     List[Any]:
		if use_temp_dir:
				with tempfile.TemporaryDirectory() as tmp_dir:
						yield tmp_dir
		else:
				yield working_dir







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None)							->     str:
		if is_numpy_array(lowerCamelCase_):
				return np.transpose(lowerCamelCase_     ,      axes=lowerCamelCase_)
		elif is_torch_tensor(lowerCamelCase_):
				return array.T if axes is None else array.permute(*lowerCamelCase_)
		elif is_tf_tensor(lowerCamelCase_):
				import tensorflow as tf

				return tf.transpose(lowerCamelCase_     ,      perm=lowerCamelCase_)
		elif is_jax_tensor(lowerCamelCase_):
				return jnp.transpose(lowerCamelCase_     ,      axes=lowerCamelCase_)
		else:
				raise ValueError(f'Type not supported for transpose: {type(lowerCamelCase_)}.')







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Optional[int]:
		if is_numpy_array(lowerCamelCase_):
				return np.reshape(lowerCamelCase_     ,      lowerCamelCase_)
		elif is_torch_tensor(lowerCamelCase_):
				return array.reshape(*lowerCamelCase_)
		elif is_tf_tensor(lowerCamelCase_):
				import tensorflow as tf

				return tf.reshape(lowerCamelCase_     ,      lowerCamelCase_)
		elif is_jax_tensor(lowerCamelCase_):
				return jnp.reshape(lowerCamelCase_     ,      lowerCamelCase_)
		else:
				raise ValueError(f'Type not supported for reshape: {type(lowerCamelCase_)}.')







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None)							->     str:
		if is_numpy_array(lowerCamelCase_):
				return np.squeeze(lowerCamelCase_     ,      axis=lowerCamelCase_)
		elif is_torch_tensor(lowerCamelCase_):
				return array.squeeze() if axis is None else array.squeeze(dim=lowerCamelCase_)
		elif is_tf_tensor(lowerCamelCase_):
				import tensorflow as tf

				return tf.squeeze(lowerCamelCase_     ,      axis=lowerCamelCase_)
		elif is_jax_tensor(lowerCamelCase_):
				return jnp.squeeze(lowerCamelCase_     ,      axis=lowerCamelCase_)
		else:
				raise ValueError(f'Type not supported for squeeze: {type(lowerCamelCase_)}.')







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Any:
		if is_numpy_array(lowerCamelCase_):
				return np.expand_dims(lowerCamelCase_     ,      lowerCamelCase_)
		elif is_torch_tensor(lowerCamelCase_):
				return array.unsqueeze(dim=lowerCamelCase_)
		elif is_tf_tensor(lowerCamelCase_):
				import tensorflow as tf

				return tf.expand_dims(lowerCamelCase_     ,      axis=lowerCamelCase_)
		elif is_jax_tensor(lowerCamelCase_):
				return jnp.expand_dims(lowerCamelCase_     ,      axis=lowerCamelCase_)
		else:
				raise ValueError(f'Type not supported for expand_dims: {type(lowerCamelCase_)}.')







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Union[str, Any]:
		if is_numpy_array(lowerCamelCase_):
				return np.size(lowerCamelCase_)
		elif is_torch_tensor(lowerCamelCase_):
				return array.numel()
		elif is_tf_tensor(lowerCamelCase_):
				import tensorflow as tf

				return tf.size(lowerCamelCase_)
		elif is_jax_tensor(lowerCamelCase_):
				return array.size
		else:
				raise ValueError(f'Type not supported for expand_dims: {type(lowerCamelCase_)}.')







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Any:
		for key, value in auto_map.items():
				if isinstance(lowerCamelCase_     ,      (tuple, list)):
						UpperCamelCase__				:					Tuple					=					[f'{repo_id}--{v}' if (v is not None and '--' not in v) else v for v in value]
				elif value is not None and "--" not in value:
						UpperCamelCase__				:					Optional[Any]					=					f'{repo_id}--{value}'

		return auto_map







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Any:
		for base_class in inspect.getmro(lowerCamelCase_):
				UpperCamelCase__				:					Optional[Any]					=					base_class.__module__
				UpperCamelCase__				:					Optional[int]					=					base_class.__name__
				if module.startswith('tensorflow') or module.startswith('keras') or name == "TFPreTrainedModel":
						return "tf"
				elif module.startswith('torch') or name == "PreTrainedModel":
						return "pt"
				elif module.startswith('flax') or module.startswith('jax') or name == "FlaxPreTrainedModel":
						return "flax"
		else:
				raise TypeError(f'Could not infer framework from class {model_class}.')












'''simple docstring'''




class       __lowercase					:

	def __init__(      self   :						List[str]  ,					UpperCAmelCase_   :						str = ""  ,					UpperCAmelCase_   :						bool = False):
			# Mapping from the first character of the prefix of the node
			UpperCamelCase__				:					dict[str, RadixNode]					=					{}

			# A node will be a leaf if the tree contains its word
			UpperCamelCase__				:					List[Any]					=					is_leaf

			UpperCamelCase__				:					Optional[Any]					=					prefix

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Optional[int]					=					0
			for q, w in zip(self.prefix  ,					UpperCAmelCase_):
					if q != w:
							break

					x += 1

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

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						list[str]):
			for word in words:
					self.insert(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						str):
			# Case 1: If the word is the prefix of the node
			# Solution: We set the current node as leaf
			if self.prefix == word:
					UpperCamelCase__				:					Optional[Any]					=					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:
					UpperCamelCase__				:					Optional[Any]					=					RadixNode(prefix=UpperCAmelCase_  ,					is_leaf=UpperCAmelCase_)

			else:
					UpperCamelCase__				:					int					=					self.nodes[word[0]]
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[Any]					=					incoming_node.match(
					    UpperCAmelCase_)

					# 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(UpperCAmelCase_)

					# 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:
							UpperCamelCase__				:					Tuple					=					remaining_prefix

							UpperCamelCase__				:					str					=					self.nodes[matching_string[0]]
							UpperCamelCase__				:					Optional[Any]					=					RadixNode(UpperCAmelCase_  ,					UpperCAmelCase_)
							UpperCamelCase__				:					str					=					aux_node

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

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Optional[Any]					=					self.nodes.get(word[0]  ,					UpperCAmelCase_)
			if not incoming_node:
					return False
			else:
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					int					=					incoming_node.match(
					    UpperCAmelCase_)
					# 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(UpperCAmelCase_)

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

									return True





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						int = 0):
			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       __UpperCAmelCase						(  )							->     bool:
		UpperCamelCase__				:					Union[str, Any]					=					'banana bananas bandana band apple all beast'.split()
		UpperCamelCase__				:					List[Any]					=					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       __UpperCAmelCase						(  )							->     None:
		assert test_trie()







def       __UpperCAmelCase						(  )							->     None:
		UpperCamelCase__				:					List[Any]					=					RadixNode()
		UpperCamelCase__				:					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()




