


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			lowerCAmelCase	=							'platform'

			import jax
			import jax.numpy as jnp

			from transformers import BlenderbotTokenizer
			from transformers.models.blenderbot.modeling_flax_blenderbot import (
			    FlaxBlenderbotForConditionalGeneration,
			    FlaxBlenderbotModel,
			    shift_tokens_right,
			)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    ):


		"""simple docstring"""




		if attention_mask is None:
				lowercase__						=				np.where(input_ids != config.pad_token_id						,    1						,    0					)
		if decoder_attention_mask is None:
				lowercase__						=				np.where(decoder_input_ids != config.pad_token_id						,    1						,    0					)
		if head_mask is None:
				lowercase__						=				np.ones((config.encoder_layers, config.encoder_attention_heads)					)
		if decoder_head_mask is None:
				lowercase__						=				np.ones((config.decoder_layers, config.decoder_attention_heads)					)
		if cross_attn_head_mask is None:
				lowercase__						=				np.ones((config.decoder_layers, config.decoder_attention_heads)					)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}


class 	_a							:

							def __init__(							self:     Dict   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     str=13   ,						UpperCamelCase_:     Any=7   ,						UpperCamelCase_:     List[Any]=True   ,						UpperCamelCase_:     Dict=False   ,						UpperCamelCase_:     Dict=99   ,						UpperCamelCase_:     Tuple=16   ,						UpperCamelCase_:     Dict=2   ,						UpperCamelCase_:     Tuple=4   ,						UpperCamelCase_:     Optional[int]=4   ,						UpperCamelCase_:     Optional[int]="gelu"   ,						UpperCamelCase_:     Optional[Any]=0.1   ,						UpperCamelCase_:     List[Any]=0.1   ,						UpperCamelCase_:     Tuple=32   ,						UpperCamelCase_:     Dict=2   ,						UpperCamelCase_:     List[str]=1   ,						UpperCamelCase_:     Tuple=0   ,						UpperCamelCase_:     str=0.02   ,						)      ->       Optional[int]:





									"""simple docstring"""







									lowercase__						=				parent
									lowercase__						=				batch_size
									lowercase__						=				seq_length
									lowercase__						=				is_training
									lowercase__						=				use_labels
									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_act
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				eos_token_id
									lowercase__						=				pad_token_id
									lowercase__						=				bos_token_id
									lowercase__						=				initializer_range

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,						self.vocab_size							)   ,						3   ,						self.vocab_size							)
									lowercase__						=				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,						dtype=np.intaa							))   ,						-1							)

									lowercase__						=				shift_tokens_right(UpperCamelCase_   ,						1   ,						2							)

									lowercase__						=				BlenderbotConfig(
									    vocab_size=self.vocab_size   ,						d_model=self.hidden_size   ,						encoder_layers=self.num_hidden_layers   ,						decoder_layers=self.num_hidden_layers   ,						encoder_attention_heads=self.num_attention_heads   ,						decoder_attention_heads=self.num_attention_heads   ,						encoder_ffn_dim=self.intermediate_size   ,						decoder_ffn_dim=self.intermediate_size   ,						dropout=self.hidden_dropout_prob   ,						attention_dropout=self.attention_probs_dropout_prob   ,						max_position_embeddings=self.max_position_embeddings   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.bos_token_id   ,						pad_token_id=self.pad_token_id   ,						initializer_range=self.initializer_range   ,						use_cache=UpperCamelCase_   ,						)
									lowercase__						=				prepare_blenderbot_inputs_dict(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)
									return config, inputs_dict

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__					,		lowercase__						=				self.prepare_config_and_inputs()
									return config, inputs_dict

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     List[str]							)      ->       int:





									"""simple docstring"""







									lowercase__						=				20
									lowercase__						=				model_class_name(UpperCamelCase_							)

									lowercase__						=				model.encode(inputs_dict['''input_ids''']							)

									lowercase__					,		lowercase__						=				(
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

									lowercase__						=				model.init_cache(decoder_input_ids.shape[0]   ,						UpperCamelCase_   ,						UpperCamelCase_							)
									lowercase__						=				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,						dtype='''i4'''							)

									lowercase__						=				jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]   ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,						)
									lowercase__						=				model.decode(
									    decoder_input_ids[:, :-1]   ,						UpperCamelCase_   ,						decoder_attention_mask=UpperCamelCase_   ,						past_key_values=UpperCamelCase_   ,						decoder_position_ids=UpperCamelCase_   ,						)

									lowercase__						=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,						dtype='''i4'''							)
									lowercase__						=				model.decode(
									    decoder_input_ids[:, -1:]   ,						UpperCamelCase_   ,						decoder_attention_mask=UpperCamelCase_   ,						past_key_values=outputs_cache.past_key_values   ,						decoder_position_ids=UpperCamelCase_   ,						)

									lowercase__						=				model.decode(UpperCamelCase_   ,						UpperCamelCase_							)

									lowercase__						=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
									self.parent.assertTrue(diff < 1E-3   ,						msg=f'Max diff is {diff}'							)


							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Optional[Any]							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				20
									lowercase__						=				model_class_name(UpperCamelCase_							)

									lowercase__						=				model.encode(inputs_dict['''input_ids''']							)

									lowercase__					,		lowercase__						=				(
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

									lowercase__						=				jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
									    ]   ,						axis=-1   ,						)

									lowercase__						=				model.init_cache(decoder_input_ids.shape[0]   ,						UpperCamelCase_   ,						UpperCamelCase_							)
									lowercase__						=				jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]   ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,						)

									lowercase__						=				model.decode(
									    decoder_input_ids[:, :-1]   ,						UpperCamelCase_   ,						decoder_attention_mask=UpperCamelCase_   ,						past_key_values=UpperCamelCase_   ,						decoder_position_ids=UpperCamelCase_   ,						)
									lowercase__						=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,						dtype='''i4'''							)
									lowercase__						=				model.decode(
									    decoder_input_ids[:, -1:]   ,						UpperCamelCase_   ,						past_key_values=outputs_cache.past_key_values   ,						decoder_attention_mask=UpperCamelCase_   ,						decoder_position_ids=UpperCamelCase_   ,						)

									lowercase__						=				model.decode(UpperCamelCase_   ,						UpperCamelCase_   ,						decoder_attention_mask=UpperCamelCase_							)

									lowercase__						=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
									self.parent.assertTrue(diff < 1E-3   ,						msg=f'Max diff is {diff}'							)


@require_flax
class 	_a							(      unittest.TestCase      ):
							_lowercase     :      List[Any]  				=			99

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ]   ,						dtype=np.intaa   ,						)

									lowercase__						=				input_ids.shape[0]
									lowercase__						=				BlenderbotConfig(
									    vocab_size=self.vocab_size   ,						d_model=24   ,						encoder_layers=2   ,						decoder_layers=2   ,						encoder_attention_heads=2   ,						decoder_attention_heads=2   ,						encoder_ffn_dim=32   ,						decoder_ffn_dim=32   ,						max_position_embeddings=48   ,						eos_token_id=2   ,						pad_token_id=1   ,						bos_token_id=0   ,						)
									return config, input_ids, batch_size

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







									lowercase__					,		lowercase__					,		lowercase__						=				self._get_config_and_data()
									lowercase__						=				FlaxBlenderbotForConditionalGeneration(UpperCamelCase_							)
									lowercase__						=				lm_model(input_ids=UpperCamelCase_							)
									lowercase__						=				(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				BlenderbotConfig(
									    vocab_size=self.vocab_size   ,						d_model=14   ,						encoder_layers=2   ,						decoder_layers=2   ,						encoder_attention_heads=2   ,						decoder_attention_heads=2   ,						encoder_ffn_dim=8   ,						decoder_ffn_dim=8   ,						max_position_embeddings=48   ,						)
									lowercase__						=				FlaxBlenderbotForConditionalGeneration(UpperCamelCase_							)
									lowercase__						=				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,						dtype=np.intaa							)
									lowercase__						=				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,						dtype=np.intaa							)
									lowercase__						=				lm_model(input_ids=UpperCamelCase_   ,						decoder_input_ids=UpperCamelCase_							)
									lowercase__						=				(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape   ,						UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Dict							)      ->       str:





									"""simple docstring"""







									lowercase__						=				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,						dtype=np.intaa							)
									lowercase__						=				shift_tokens_right(UpperCamelCase_   ,						1   ,						2							)
									lowercase__						=				np.equal(UpperCamelCase_   ,						1							).astype(np.floataa							).sum()
									lowercase__						=				np.equal(UpperCamelCase_   ,						1							).astype(np.floataa							).sum()
									self.assertEqual(shifted.shape   ,						input_ids.shape							)
									self.assertEqual(UpperCamelCase_   ,						n_pad_before - 1							)
									self.assertTrue(np.equal(shifted[:, 0]   ,						2							).all()							)


@require_flax
class 	_a							(      UpperCamelCase__					,     unittest.TestCase					,     UpperCamelCase__      ):
							_lowercase     :      List[str]  				=			True
							_lowercase     :      List[Any]  				=			(
							    (
							        FlaxBlenderbotModel,
							        FlaxBlenderbotForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							_lowercase     :      Optional[int]  				=			(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Optional[int]:





									"""simple docstring"""







									lowercase__						=				FlaxBlenderbotModelTester(self							)

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__					,		lowercase__						=				self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     str							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__					,		lowercase__						=				self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Optional[int]:





									"""simple docstring"""







									lowercase__					,		lowercase__						=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__							):
													lowercase__						=				self._prepare_for_class(UpperCamelCase_   ,						UpperCamelCase_							)
													lowercase__						=				model_class(UpperCamelCase_							)

													@jax.jit
													def encode_jitted(UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Any=None   ,						**UpperCamelCase_:     Optional[Any]							):
															return model.encode(input_ids=UpperCamelCase_   ,						attention_mask=UpperCamelCase_							)

													with self.subTest('''JIT Enabled'''							):
															lowercase__						=				encode_jitted(**UpperCamelCase_							).to_tuple()

													with self.subTest('''JIT Disabled'''							):
															with jax.disable_jit():
																	lowercase__						=				encode_jitted(**UpperCamelCase_							).to_tuple()

													self.assertEqual(len(UpperCamelCase_							)   ,						len(UpperCamelCase_							)							)
													for jitted_output, output in zip(UpperCamelCase_   ,						UpperCamelCase_							):
															self.assertEqual(jitted_output.shape   ,						output.shape							)

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Dict:





									"""simple docstring"""







									lowercase__					,		lowercase__						=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__							):
													lowercase__						=				model_class(UpperCamelCase_							)
													lowercase__						=				model.encode(inputs_dict['''input_ids''']   ,						inputs_dict['''attention_mask''']							)

													lowercase__						=				{
													    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
													    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
													    '''encoder_outputs''': encoder_outputs,
													}

													@jax.jit
													def decode_jitted(UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Any							):
															return model.decode(
															    decoder_input_ids=UpperCamelCase_   ,						decoder_attention_mask=UpperCamelCase_   ,						encoder_outputs=UpperCamelCase_   ,						)

													with self.subTest('''JIT Enabled'''							):
															lowercase__						=				decode_jitted(**UpperCamelCase_							).to_tuple()

													with self.subTest('''JIT Disabled'''							):
															with jax.disable_jit():
																	lowercase__						=				decode_jitted(**UpperCamelCase_							).to_tuple()

													self.assertEqual(len(UpperCamelCase_							)   ,						len(UpperCamelCase_							)							)
													for jitted_output, output in zip(UpperCamelCase_   ,						UpperCamelCase_							):
															self.assertEqual(jitted_output.shape   ,						output.shape							)

							@slow
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       List[Any]:





									"""simple docstring"""







									for model_class_name in self.all_model_classes:
											lowercase__						=				model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''							)
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											lowercase__						=				np.ones((1, 1)							) * model.config.eos_token_id
											lowercase__						=				model(UpperCamelCase_							)
											self.assertIsNotNone(UpperCamelCase_							)


							@unittest.skipUnless(jax_device != '''cpu'''   ,						'''3B test too slow on CPU.'''							)
							@slow
							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
									lowercase__						=				{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

									lowercase__						=				FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''   ,						from_pt=UpperCamelCase_							)
									lowercase__						=				BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''							)

									lowercase__						=				['''Sam''']
									lowercase__						=				tokenizer(UpperCamelCase_   ,						return_tensors='''jax'''							)

									lowercase__						=				model.generate(**UpperCamelCase_   ,						**UpperCamelCase_							)
									lowercase__						=				'''Sam is a great name. It means "sun" in Gaelic.'''

									lowercase__						=				tokenizer.batch_decode(UpperCamelCase_   ,						**UpperCamelCase_							)
									assert generated_txt[0].strip() == tgt_text






from collections.abc import Sequence
from queue import Queue


class 	_a							:

							def __init__(							self:     Tuple   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Union[str, Any]=None   ,						UpperCamelCase_:     Dict=None							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				start
									lowercase__						=				end
									lowercase__						=				val
									lowercase__						=				(start + end) // 2
									lowercase__						=				left
									lowercase__						=				right


							def __repr__(							self:     Optional[int]							)      ->       Optional[Any]:





									"""simple docstring"""







									return f'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'


class 	_a							:

							def __init__(							self:     Any   ,						UpperCamelCase_:     Sequence   ,						UpperCamelCase_:     Any							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				collection
									lowercase__						=				function
									if self.collection:
											lowercase__						=				self._build_tree(0   ,						len(UpperCamelCase_							) - 1							)

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Union[str, Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									self._update_tree(self.root   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     List[str]							)      ->       Optional[Any]:





									"""simple docstring"""







									return self._query_range(self.root   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Dict							)      ->       str:





									"""simple docstring"""







									if start == end:
											return SegmentTreeNode(UpperCamelCase_   ,						UpperCamelCase_   ,						self.collection[start]							)
									lowercase__						=				(start + end) // 2
									lowercase__						=				self._build_tree(UpperCamelCase_   ,						UpperCamelCase_							)
									lowercase__						=				self._build_tree(mid + 1   ,						UpperCamelCase_							)
									return SegmentTreeNode(UpperCamelCase_   ,						UpperCamelCase_   ,						self.fn(left.val   ,						right.val							)   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[Any]   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Union[str, Any]							)      ->       Dict:





									"""simple docstring"""







									if node.start == i and node.end == i:
											lowercase__						=				val
											return
									if i <= node.mid:
											self._update_tree(node.left   ,						UpperCamelCase_   ,						UpperCamelCase_							)
									else:
											self._update_tree(node.right   ,						UpperCamelCase_   ,						UpperCamelCase_							)
									lowercase__						=				self.fn(node.left.val   ,						node.right.val							)

							def 				lowerCamelCase_			(							self:     List[Any]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Dict							)      ->       List[Any]:





									"""simple docstring"""







									if node.start == i and node.end == j:
											return node.val

									if i <= node.mid:
											if j <= node.mid:
													# range in left child tree
													return self._query_range(node.left   ,						UpperCamelCase_   ,						UpperCamelCase_							)
											else:
													# range in left child tree and right child tree
													return self.fn(
													    self._query_range(node.left   ,						UpperCamelCase_   ,						node.mid							)   ,						self._query_range(node.right   ,						node.mid + 1   ,						UpperCamelCase_							)   ,						)
									else:
											# range in right child tree
											return self._query_range(node.right   ,						UpperCamelCase_   ,						UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       str:





									"""simple docstring"""







									if self.root is not None:
											lowercase__						=				Queue()
											queue.put(self.root							)
											while not queue.empty():
													lowercase__						=				queue.get()
													yield node

													if node.left is not None:
															queue.put(node.left							)

													if node.right is not None:
															queue.put(node.right							)


if __name__ == "__main__":
			import operator

			for fn in [operator.add, max, min]:
						print('*' * 50)
						lowerCAmelCase	=							SegmentTree([2, 1, 5, 3, 4], fn)
						for node in arr.traverse():
									print(node)
						print()

						arr.update(1, 5)
						for node in arr.traverse():
									print(node)
						print()

						print(arr.query_range(3, 4))  # 7
						print(arr.query_range(2, 2))  # 5
						print(arr.query_range(1, 3))  # 13
						print()


