



'''simple docstring'''




import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:





							if model.config.model_type == "gpt2":
														return model.transformer.h[0].mlp.c_fc
							return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
							import torch
							import torch.nn as nn

							class 			UpperCamelCase_ (							nn.Module			):





								def __init__(  self		,		A		,		A    )			->			Tuple:
															super().__init__()
															UpperCAmelCase     :		Tuple							     =		module
															UpperCAmelCase     :		Optional[Any]							     =		nn.Sequential(
															    nn.Linear(module.in_features		,		__snake_case		,		bias=__snake_case    )		,		nn.Linear(__snake_case		,		module.out_features		,		bias=__snake_case    )		,		)
															UpperCAmelCase     :		Union[str, Any]							     =		(2.0 / (5 * min(module.in_features		,		module.out_features    ))) ** 0.5
															nn.init.normal_(self.adapter[0].weight		,		std=__snake_case    )
															nn.init.zeros_(self.adapter[1].weight    )
															self.adapter.to(module.weight.device    )






								def  _lowercase(  self		,		A		,		*A		,		**A    )			->			Union[str, Any]:
															return self.module(__snake_case		,		*__snake_case		,		**__snake_case    ) + self.adapter(__snake_case    )

@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 			UpperCamelCase_ (							unittest.TestCase			):
	lowercase          = """bigscience/bloom-1b7"""

	# Constant values
	lowercase          = 2.109_6595_5269_2574

	lowercase          = """Hello my name is"""
	lowercase          = set()
	EXPECTED_OUTPUTS.add('Hello my name is John and I am a professional photographer. I'			)
	EXPECTED_OUTPUTS.add('Hello my name is John.\nI am a friend of your father.\n'			)
	EXPECTED_OUTPUTS.add('Hello my name is John Doe, I am a student at the University'			)
	lowercase          = 10





	def  _lowercase(  self    )			->			Optional[Any]:
								# Models and tokenizer
								UpperCAmelCase     :		Any							     =		AutoTokenizer.from_pretrained(self.model_name    )




class 			UpperCamelCase_ (							_snake_case			):





	def  _lowercase(  self    )			->			Optional[Any]:
								super().setUp()

								# Models and tokenizer
								UpperCAmelCase     :		Tuple							     =		AutoModelForCausalLM.from_pretrained(
								    self.model_name		,		torch_dtype=torch.floataa		,		device_map="""auto"""    )
								UpperCAmelCase     :		str							     =		AutoModelForCausalLM.from_pretrained(self.model_name		,		load_in_abit=__snake_case		,		device_map="""auto"""    )





	def  _lowercase(  self    )			->			int:
								del self.model_fpaa
								del self.model_abit

								gc.collect()
								torch.cuda.empty_cache()





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		str							     =		self.model_abit.config

								self.assertTrue(hasattr(__snake_case		,		"""quantization_config"""    )    )

								UpperCAmelCase     :		List[str]							     =		config.to_dict()
								UpperCAmelCase     :		List[str]							     =		config.to_diff_dict()

								UpperCAmelCase     :		Dict							     =		config.to_json_string()





	def  _lowercase(  self    )			->			List[str]:
								from bitsandbytes.nn import Paramsabit

								UpperCAmelCase     :		List[Any]							     =		self.model_fpaa.get_memory_footprint()
								UpperCAmelCase     :		Dict							     =		self.model_abit.get_memory_footprint()

								self.assertAlmostEqual(mem_fpaa / mem_abit		,		self.EXPECTED_RELATIVE_DIFFERENCE    )
								UpperCAmelCase     :		List[Any]							     =		get_some_linear_layer(self.model_abit    )
								self.assertTrue(linear.weight.__class__ == Paramsabit    )





	def  _lowercase(  self    )			->			List[str]:
								from transformers import TaPreTrainedModel

								self.model_fpaa.get_memory_footprint()
								self.model_abit.get_memory_footprint()

								for name, module in self.model_abit.named_modules():
															if isinstance(__snake_case		,		torch.nn.Linear    ):
																						if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																													# 4-bit parameters are packed in uint8 variables
																													self.assertTrue(module.weight.dtype == torch.uinta    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[int]							     =		self.tokenizer(self.input_text		,		return_tensors="""pt"""    )
								UpperCAmelCase     :		Optional[int]							     =		self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0    )		,		max_new_tokens=10    )

								self.assertIn(self.tokenizer.decode(output_sequences[0]		,		skip_special_tokens=__snake_case    )		,		self.EXPECTED_OUTPUTS    )





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Optional[int]							     =		BitsAndBytesConfig()
								UpperCAmelCase     :		Tuple							     =		True

								UpperCAmelCase     :		Optional[Any]							     =		AutoModelForCausalLM.from_pretrained(
								    self.model_name		,		quantization_config=__snake_case		,		device_map="""auto"""    )

								UpperCAmelCase     :		List[str]							     =		self.tokenizer(self.input_text		,		return_tensors="""pt"""    )
								UpperCAmelCase     :		Dict							     =		model_abit_from_config.generate(
								    input_ids=encoded_input["""input_ids"""].to(0    )		,		max_new_tokens=10    )

								self.assertIn(self.tokenizer.decode(output_sequences[0]		,		skip_special_tokens=__snake_case    )		,		self.EXPECTED_OUTPUTS    )





	def  _lowercase(  self    )			->			Any:
								with self.assertRaises(__snake_case    ), tempfile.TemporaryDirectory() as tmpdirname:
															self.model_abit.save_pretrained(__snake_case    )





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Union[str, Any]							     =		BitsAndBytesConfig()

								with self.assertRaises(__snake_case    ):
															UpperCAmelCase     :		Optional[int]							     =		AutoModelForCausalLM.from_pretrained(
															    self.model_name		,		quantization_config=__snake_case		,		load_in_abit=__snake_case		,		device_map="""auto"""		,		bnb_abit_quant_type="""nf4"""		,		)





	def  _lowercase(  self    )			->			Tuple:
								with self.assertRaises(__snake_case    ):
															# Tries with `str`
															self.model_abit.to("""cpu"""    )

								with self.assertRaises(__snake_case    ):
															# Tries with a `dtype``
															self.model_abit.to(torch.floataa    )

								with self.assertRaises(__snake_case    ):
															# Tries with a `device`
															self.model_abit.to(torch.device("""cuda:0"""    )    )

								with self.assertRaises(__snake_case    ):
															# Tries with a `device`
															self.model_abit.float()

								with self.assertRaises(__snake_case    ):
															# Tries with a `device`
															self.model_abit.half()

								# Test if we did not break anything
								UpperCAmelCase     :		Optional[Any]							     =		self.tokenizer(self.input_text		,		return_tensors="""pt"""    )

								UpperCAmelCase     :		int							     =		self.model_fpaa.to(torch.floataa    )
								UpperCAmelCase     :		List[Any]							     =		self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0    )		,		max_new_tokens=10    )

								# Check this does not throw an error
								UpperCAmelCase     :		str							     =		self.model_fpaa.to("""cpu"""    )

								# Check this does not throw an error
								UpperCAmelCase     :		List[str]							     =		self.model_fpaa.half()

								# Check this does not throw an error
								UpperCAmelCase     :		Any							     =		self.model_fpaa.float()






	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		List[str]							     =		AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""		,		load_in_abit=__snake_case		,		device_map="""auto"""    )
								self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa    )




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 			UpperCamelCase_ (							unittest.TestCase			):





	@classmethod
	def  _lowercase(  cls    )			->			Any:
								UpperCAmelCase     :		Any							     =		"""t5-small"""
								UpperCAmelCase     :		Optional[int]							     =		"""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
								UpperCAmelCase     :		Optional[int]							     =		AutoTokenizer.from_pretrained(cls.model_name    )
								UpperCAmelCase     :		Optional[int]							     =		"""Translate in German: Hello, my dog is cute"""





	def  _lowercase(  self    )			->			Dict:
								gc.collect()
								torch.cuda.empty_cache()





	def  _lowercase(  self    )			->			Optional[int]:
								from transformers import TaForConditionalGeneration

								UpperCAmelCase     :		Optional[int]							     =		TaForConditionalGeneration._keep_in_fpaa_modules
								UpperCAmelCase     :		List[str]							     =		None

								# test with `t5-small`
								UpperCAmelCase     :		Any							     =		TaForConditionalGeneration.from_pretrained(self.model_name		,		load_in_abit=__snake_case		,		device_map="""auto"""    )
								UpperCAmelCase     :		Union[str, Any]							     =		self.tokenizer(self.input_text		,		return_tensors="""pt"""    ).to(0    )
								UpperCAmelCase     :		Optional[Any]							     =		model.generate(**__snake_case    )

								# test with `flan-t5-small`
								UpperCAmelCase     :		Optional[Any]							     =		TaForConditionalGeneration.from_pretrained(
								    self.dense_act_model_name		,		load_in_abit=__snake_case		,		device_map="""auto"""    )
								UpperCAmelCase     :		Any							     =		self.tokenizer(self.input_text		,		return_tensors="""pt"""    ).to(0    )
								UpperCAmelCase     :		List[str]							     =		model.generate(**__snake_case    )
								UpperCAmelCase     :		Optional[int]							     =		modules






	def  _lowercase(  self    )			->			Any:
								import bitsandbytes as bnb

								from transformers import TaForConditionalGeneration

								# test with `t5-small`
								UpperCAmelCase     :		Dict							     =		TaForConditionalGeneration.from_pretrained(self.model_name		,		load_in_abit=__snake_case		,		device_map="""auto"""    )

								# there was a bug with decoders - this test checks that it is fixed
								self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q		,		bnb.nn.Linearabit    )    )

								UpperCAmelCase     :		str							     =		self.tokenizer(self.input_text		,		return_tensors="""pt"""    ).to(0    )
								UpperCAmelCase     :		Tuple							     =		model.generate(**__snake_case    )

								# test with `flan-t5-small`
								UpperCAmelCase     :		Optional[int]							     =		TaForConditionalGeneration.from_pretrained(
								    self.dense_act_model_name		,		load_in_abit=__snake_case		,		device_map="""auto"""    )
								UpperCAmelCase     :		List[Any]							     =		self.tokenizer(self.input_text		,		return_tensors="""pt"""    ).to(0    )
								UpperCAmelCase     :		int							     =		model.generate(**__snake_case    )




class 			UpperCamelCase_ (							_snake_case			):





	def  _lowercase(  self    )			->			Tuple:
								super().setUp()
								# model_name
								UpperCAmelCase     :		List[Any]							     =		"""bigscience/bloom-560m"""
								UpperCAmelCase     :		Optional[Any]							     =		"""t5-small"""

								# Different types of model

								UpperCAmelCase     :		Dict							     =		AutoModel.from_pretrained(self.model_name		,		load_in_abit=__snake_case		,		device_map="""auto"""    )
								# Sequence classification model
								UpperCAmelCase     :		Any							     =		AutoModelForSequenceClassification.from_pretrained(
								    self.model_name		,		load_in_abit=__snake_case		,		device_map="""auto"""    )
								# CausalLM model
								UpperCAmelCase     :		Union[str, Any]							     =		AutoModelForCausalLM.from_pretrained(self.model_name		,		load_in_abit=__snake_case		,		device_map="""auto"""    )
								# Seq2seq model
								UpperCAmelCase     :		List[str]							     =		AutoModelForSeqaSeqLM.from_pretrained(
								    self.seq_to_seq_name		,		load_in_abit=__snake_case		,		device_map="""auto"""    )





	def  _lowercase(  self    )			->			List[str]:
								del self.base_model
								del self.sequence_model
								del self.model_abit
								del self.seq_to_seq_model

								gc.collect()
								torch.cuda.empty_cache()






	def  _lowercase(  self    )			->			Optional[int]:
								from bitsandbytes.nn import Paramsabit

								self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit    )

								# Other heads should be nn.Parameter
								self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter    )
								self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter    )
								self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter    )




class 			UpperCamelCase_ (							_snake_case			):





	def  _lowercase(  self    )			->			Dict:
								super().setUp()





	def  _lowercase(  self    )			->			List[Any]:
								del self.pipe

								gc.collect()
								torch.cuda.empty_cache()






	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		List[Any]							     =		pipeline(
								    """text-generation"""		,		model=self.model_name		,		model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}		,		max_new_tokens=self.MAX_NEW_TOKENS		,		)

								# Real second forward pass
								UpperCAmelCase     :		Union[str, Any]							     =		self.pipe(self.input_text    )
								self.assertIn(pipeline_output[0]["""generated_text"""]		,		self.EXPECTED_OUTPUTS    )




@require_torch_multi_gpu
class 			UpperCamelCase_ (							_snake_case			):





	def  _lowercase(  self    )			->			Union[str, Any]:
								super().setUp()






	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		str							     =		AutoModelForCausalLM.from_pretrained(
								    self.model_name		,		load_in_abit=__snake_case		,		device_map="""balanced"""    )

								# Check correct device map
								self.assertEqual(set(model_parallel.hf_device_map.values()    )		,		{0, 1}    )

								# Check that inference pass works on the model
								UpperCAmelCase     :		int							     =		self.tokenizer(self.input_text		,		return_tensors="""pt"""    )

								# Second real batch
								UpperCAmelCase     :		List[str]							     =		model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0    )		,		max_new_tokens=10    )
								self.assertIn(self.tokenizer.decode(output_parallel[0]		,		skip_special_tokens=__snake_case    )		,		self.EXPECTED_OUTPUTS    )




class 			UpperCamelCase_ (							_snake_case			):





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		int							     =		"""facebook/opt-350m"""
								super().setUp()






	def  _lowercase(  self    )			->			List[str]:
								if version.parse(importlib.metadata.version("""bitsandbytes"""    )    ) < version.parse("""0.37.0"""    ):
															return

								# Step 1: freeze all parameters
								UpperCAmelCase     :		List[str]							     =		AutoModelForCausalLM.from_pretrained(self.model_name		,		load_in_abit=__snake_case    )

								self.assertEqual(set(model.hf_device_map.values()    )		,		{torch.cuda.current_device()}    )

								for param in model.parameters():
															UpperCAmelCase     :		int							     =		False  # freeze the model - train adapters later
															if param.ndim == 1:
																						# cast the small parameters (e.g. layernorm) to fp32 for stability
																						UpperCAmelCase     :		List[Any]							     =		param.data.to(torch.floataa    )

        # Step 2: add adapters
								for _, module in model.named_modules():
															if "OPTAttention" in repr(type(__snake_case    )    ):
																						UpperCAmelCase     :		Any							     =		LoRALayer(module.q_proj		,		rank=16    )
																						UpperCAmelCase     :		Optional[int]							     =		LoRALayer(module.k_proj		,		rank=16    )
																						UpperCAmelCase     :		Union[str, Any]							     =		LoRALayer(module.v_proj		,		rank=16    )

        # Step 3: dummy batch
								UpperCAmelCase     :		int							     =		self.tokenizer("""Test batch """		,		return_tensors="""pt"""    ).to(0    )

								# Step 4: Check if the gradient is not None
								with torch.cuda.amp.autocast():
															UpperCAmelCase     :		Tuple							     =		model.forward(**__snake_case    )
															out.logits.norm().backward()

								for module in model.modules():
															if isinstance(__snake_case		,		__snake_case    ):
																						self.assertTrue(module.adapter[1].weight.grad is not None    )
																						self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0    )
															elif isinstance(__snake_case		,		nn.Embedding    ):
																						self.assertTrue(module.weight.grad is None    )




class 			UpperCamelCase_ (							_snake_case			):
	lowercase          = """gpt2-xl"""
	lowercase          = 3.3191_8548_5415_2187





'''simple docstring'''


class     lowercase__  :





							'''simple docstring'''


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

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

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      prefix






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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 UpperCAmelCase_	(    self    ,					__snake_case  ):
											for word in words:
															self.insert(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											# 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 :       List[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:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      RadixNode(prefix=__snake_case    ,					is_leaf=__snake_case  )

											else:
															_SCREAMING_SNAKE_CASE :       int     		=      self.nodes[word[0]]
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      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:
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      remaining_prefix

																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes[matching_string[0]]
																			_SCREAMING_SNAKE_CASE :       List[Any]     		=      RadixNode(__snake_case    ,					__snake_case  )
																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      aux_node

																			if remaining_word == "":
																							_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      True
																			else:
																							self.nodes[matching_string[0]].insert(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes.get(word[0]    ,					__snake_case  )
											if not incoming_node:
															return False
											else:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      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 UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.nodes.get(word[0]    ,					__snake_case  )
											if not incoming_node:
															return False
											else:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      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:
																															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      list(self.nodes.values()  )[0]
																															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      merging_node.is_leaf
																															self.prefix += merging_node.prefix
																															_SCREAMING_SNAKE_CASE :       Tuple     		=      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 :       List[str]     		=      False
																							# If there is 1 edge, we merge it with its child
																							else:
																											_SCREAMING_SNAKE_CASE :       int     		=      list(incoming_node.nodes.values()  )[0]
																											_SCREAMING_SNAKE_CASE :       Tuple     		=      merging_node.is_leaf
																											incoming_node.prefix += merging_node.prefix
																											_SCREAMING_SNAKE_CASE :       str     		=      merging_node.nodes

																							return True






							def UpperCAmelCase_	(    self    ,					__snake_case = 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 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      """banana bananas bandana band apple all beast""".split()
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      RadixNode()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

				assert all(root.find(SCREAMING_SNAKE_CASE__     ) 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 			snake_case_   (  ):







				"""simple docstring"""





				assert test_trie()







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      RadixNode()
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      """banana bananas bandanas bandana band apple all beast""".split()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

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


if __name__ == "__main__":
				main()




