
"""simple docstring"""




import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)



def        _lowerCamelCase ( __a,							__a,							__a,							__a=False    ):
		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
				    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
				    ''' instructions.'''    )
				raise

		if not is_sharded:
				SCREAMING_SNAKE_CASE_						 = os.path.abspath(snake_case_    )
				logger.info(F'Loading PyTorch weights from {pt_path}'    )

				SCREAMING_SNAKE_CASE_						 = torch.load(snake_case_,							map_location='''cpu'''    )
				logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()    ):,} parameters.'    )

				SCREAMING_SNAKE_CASE_						 = convert_pytorch_state_dict_to_flax(snake_case_,							snake_case_    )
		else:
				# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
				SCREAMING_SNAKE_CASE_						 = convert_pytorch_sharded_state_dict_to_flax(snake_case_,							snake_case_    )
		return flax_state_dict



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							):

		def is_key_or_prefix_key_in_dict(__a    ) -> bool:
				return len(set(snake_case_    ) & {key, (model_prefix,) + key}    ) > 0

		# layer norm
		SCREAMING_SNAKE_CASE_						 = pt_tuple_key[:-1] + ("""scale""",)
		if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(snake_case_    ):
				return renamed_pt_tuple_key, pt_tensor

		# batch norm layer mean
		SCREAMING_SNAKE_CASE_						 = pt_tuple_key[:-1] + ("""mean""",)
		if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(snake_case_    ):
				return renamed_pt_tuple_key, pt_tensor

		# batch norm layer var
		SCREAMING_SNAKE_CASE_						 = pt_tuple_key[:-1] + ("""var""",)
		if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(snake_case_    ):
				return renamed_pt_tuple_key, pt_tensor

		# embedding
		SCREAMING_SNAKE_CASE_						 = pt_tuple_key[:-1] + ("""embedding""",)
		if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case_    ):
				return renamed_pt_tuple_key, pt_tensor

		# conv layer
		SCREAMING_SNAKE_CASE_						 = pt_tuple_key[:-1] + ("""kernel""",)
		if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(snake_case_    ):
				SCREAMING_SNAKE_CASE_						 = pt_tensor.transpose(2,							3,							1,							0    )
				return renamed_pt_tuple_key, pt_tensor

		# linear layer
		SCREAMING_SNAKE_CASE_						 = pt_tuple_key[:-1] + ("""kernel""",)
		if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case_    ):
				SCREAMING_SNAKE_CASE_						 = pt_tensor.T
				return renamed_pt_tuple_key, pt_tensor

		# old PyTorch layer norm weight
		SCREAMING_SNAKE_CASE_						 = pt_tuple_key[:-1] + ("""weight""",)
		if pt_tuple_key[-1] == "gamma":
				return renamed_pt_tuple_key, pt_tensor

		# old PyTorch layer norm bias
		SCREAMING_SNAKE_CASE_						 = pt_tuple_key[:-1] + ("""bias""",)
		if pt_tuple_key[-1] == "beta":
				return renamed_pt_tuple_key, pt_tensor

		# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
		SCREAMING_SNAKE_CASE_						 = None
		if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
				SCREAMING_SNAKE_CASE_						 = pt_tuple_key[-2] + """_g"""
		elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
				SCREAMING_SNAKE_CASE_						 = pt_tuple_key[-2] + """_v"""
		if name is not None:
				SCREAMING_SNAKE_CASE_						 = pt_tuple_key[:-3] + (name,)
				return renamed_pt_tuple_key, pt_tensor

		return pt_tuple_key, pt_tensor



def        _lowerCamelCase ( __a,							__a    ):
		# convert pytorch tensor to numpy
		SCREAMING_SNAKE_CASE_						 = {k: v.numpy() for k, v in pt_state_dict.items()}

		SCREAMING_SNAKE_CASE_						 = flax_model.base_model_prefix

		# use params dict if the model contains batch norm layers
		if "params" in flax_model.params:
				SCREAMING_SNAKE_CASE_						 = flax_model.params["""params"""]
		else:
				SCREAMING_SNAKE_CASE_						 = flax_model.params
		SCREAMING_SNAKE_CASE_						 = flatten_dict(snake_case_    )

		# add batch_stats keys,values to dict
		if "batch_stats" in flax_model.params:
				SCREAMING_SNAKE_CASE_						 = flatten_dict(flax_model.params['''batch_stats''']    )
				random_flax_state_dict.update(snake_case_    )

		SCREAMING_SNAKE_CASE_						 = {}

		SCREAMING_SNAKE_CASE_						 = (model_prefix not in flax_model_params) and (
		    model_prefix in {k.split('''.'''    )[0] for k in pt_state_dict.keys()}
		)
		SCREAMING_SNAKE_CASE_						 = (model_prefix in flax_model_params) and (
		    model_prefix not in {k.split('''.'''    )[0] for k in pt_state_dict.keys()}
		)

		# Need to change some parameters name to match Flax names
		for pt_key, pt_tensor in pt_state_dict.items():
				SCREAMING_SNAKE_CASE_						 = tuple(pt_key.split('''.'''    )    )

				# remove base model prefix if necessary
				SCREAMING_SNAKE_CASE_						 = pt_tuple_key[0] == model_prefix
				if load_model_with_head_into_base_model and has_base_model_prefix:
						SCREAMING_SNAKE_CASE_						 = pt_tuple_key[1:]

				# Correctly rename weight parameters
				SCREAMING_SNAKE_CASE_						 = rename_key_and_reshape_tensor(
				    snake_case_,							snake_case_,							snake_case_,							snake_case_    )

				# add model prefix if necessary
				SCREAMING_SNAKE_CASE_						 = (model_prefix,) + flax_key in random_flax_state_dict
				if load_base_model_into_model_with_head and require_base_model_prefix:
						SCREAMING_SNAKE_CASE_						 = (model_prefix,) + flax_key

				if flax_key in random_flax_state_dict:
						if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
								raise ValueError(
								    F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
								    F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'    )

        # add batch stats if the model contains batchnorm layers
				if "batch_stats" in flax_model.params:
						if "mean" in flax_key[-1] or "var" in flax_key[-1]:
								SCREAMING_SNAKE_CASE_						 = jnp.asarray(snake_case_    )
								continue
						# remove num_batches_tracked key
						if "num_batches_tracked" in flax_key[-1]:
								flax_state_dict.pop(snake_case_,							snake_case_    )
								continue

						# also add unexpected weight so that warning is thrown
						SCREAMING_SNAKE_CASE_						 = jnp.asarray(snake_case_    )

				else:
						# also add unexpected weight so that warning is thrown
						SCREAMING_SNAKE_CASE_						 = jnp.asarray(snake_case_    )

		return unflatten_dict(snake_case_    )



def        _lowerCamelCase ( __a,							__a    ):
		import torch

		# Load the index
		SCREAMING_SNAKE_CASE_						 = {}
		for shard_file in shard_filenames:
				# load using msgpack utils
				SCREAMING_SNAKE_CASE_						 = torch.load(snake_case_    )
				SCREAMING_SNAKE_CASE_						 = {k: v.numpy() for k, v in pt_state_dict.items()}

				SCREAMING_SNAKE_CASE_						 = flax_model.base_model_prefix

				# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
				if "batch_stats" in flax_model.params:
						SCREAMING_SNAKE_CASE_						 = flax_model.params["""params"""]

						SCREAMING_SNAKE_CASE_						 = flatten_dict(snake_case_    )
						random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']    )    )
				else:
						SCREAMING_SNAKE_CASE_						 = flax_model.params
						SCREAMING_SNAKE_CASE_						 = flatten_dict(snake_case_    )

				SCREAMING_SNAKE_CASE_						 = (model_prefix not in flax_model_params) and (
				    model_prefix in {k.split('''.'''    )[0] for k in pt_state_dict.keys()}
				)
				SCREAMING_SNAKE_CASE_						 = (model_prefix in flax_model_params) and (
				    model_prefix not in {k.split('''.'''    )[0] for k in pt_state_dict.keys()}
				)
				# Need to change some parameters name to match Flax names
				for pt_key, pt_tensor in pt_state_dict.items():
						SCREAMING_SNAKE_CASE_						 = tuple(pt_key.split('''.'''    )    )

						# remove base model prefix if necessary
						SCREAMING_SNAKE_CASE_						 = pt_tuple_key[0] == model_prefix
						if load_model_with_head_into_base_model and has_base_model_prefix:
								SCREAMING_SNAKE_CASE_						 = pt_tuple_key[1:]

						# Correctly rename weight parameters
						SCREAMING_SNAKE_CASE_						 = rename_key_and_reshape_tensor(
						    snake_case_,							snake_case_,							snake_case_,							snake_case_    )
						# add model prefix if necessary
						SCREAMING_SNAKE_CASE_						 = (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
								SCREAMING_SNAKE_CASE_						 = (model_prefix,) + flax_key

						if flax_key in random_flax_state_dict:
								if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
										raise ValueError(
										    F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
										    F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'    )

            # add batch stats if the model contains batchnorm layers
						if "batch_stats" in flax_model.params:
								if "mean" in flax_key[-1]:
										SCREAMING_SNAKE_CASE_						 = jnp.asarray(snake_case_    )
										continue
								if "var" in flax_key[-1]:
										SCREAMING_SNAKE_CASE_						 = jnp.asarray(snake_case_    )
										continue
								# remove num_batches_tracked key
								if "num_batches_tracked" in flax_key[-1]:
										flax_state_dict.pop(snake_case_,							snake_case_    )
										continue

								# also add unexpected weight so that warning is thrown
								SCREAMING_SNAKE_CASE_						 = jnp.asarray(snake_case_    )

						else:
								# also add unexpected weight so that warning is thrown
								SCREAMING_SNAKE_CASE_						 = jnp.asarray(snake_case_    )
		return unflatten_dict(snake_case_    )



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = os.path.abspath(snake_case_    )
		logger.info(F'Loading Flax weights from {flax_checkpoint_path}'    )

		# import correct flax class
		SCREAMING_SNAKE_CASE_						 = getattr(snake_case_,							'''Flax''' + model.__class__.__name__    )

		# load flax weight dict
		with open(snake_case_,							'''rb'''    ) as state_f:
				try:
						SCREAMING_SNAKE_CASE_						 = from_bytes(snake_case_,							state_f.read()    )
				except UnpicklingError:
						raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '    )

		return load_flax_weights_in_pytorch_model(snake_case_,							snake_case_    )



def        _lowerCamelCase ( __a,							__a    ):

		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
				    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
				    ''' instructions.'''    )
				raise

		# check if we have bf16 weights
		SCREAMING_SNAKE_CASE_						 = flatten_dict(jax.tree_util.tree_map(lambda __a    : x.dtype == jnp.bfloataa,							snake_case_    )    ).values()
		if any(snake_case_    ):
				# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
				    '''before loading those in PyTorch model.'''    )
				SCREAMING_SNAKE_CASE_						 = jax.tree_util.tree_map(
				    lambda __a    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params,							snake_case_    )

		SCREAMING_SNAKE_CASE_						 = flatten_dict(snake_case_    )
		SCREAMING_SNAKE_CASE_						 = pt_model.state_dict()

		SCREAMING_SNAKE_CASE_						 = (pt_model.base_model_prefix in flax_state) and (
		    pt_model.base_model_prefix not in {k.split('''.'''    )[0] for k in pt_model_dict.keys()}
		)
		SCREAMING_SNAKE_CASE_						 = (pt_model.base_model_prefix not in flax_state) and (
		    pt_model.base_model_prefix in {k.split('''.'''    )[0] for k in pt_model_dict.keys()}
		)

		# keep track of unexpected & missing keys
		SCREAMING_SNAKE_CASE_						 = []
		SCREAMING_SNAKE_CASE_						 = set(pt_model_dict.keys()    )

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				SCREAMING_SNAKE_CASE_						 = flax_key_tuple[0] == pt_model.base_model_prefix
				SCREAMING_SNAKE_CASE_						 = """.""".join((pt_model.base_model_prefix,) + flax_key_tuple    ) in pt_model_dict

				# adapt flax_key to prepare for loading from/to base model only
				if load_model_with_head_into_base_model and has_base_model_prefix:
						SCREAMING_SNAKE_CASE_						 = flax_key_tuple[1:]
				elif load_base_model_into_model_with_head and require_base_model_prefix:
						SCREAMING_SNAKE_CASE_						 = (pt_model.base_model_prefix,) + flax_key_tuple

				# rename flax weights to PyTorch format
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(snake_case_    ) not in pt_model_dict:
						# conv layer
						SCREAMING_SNAKE_CASE_						 = flax_key_tuple[:-1] + ("""weight""",)
						SCREAMING_SNAKE_CASE_						 = jnp.transpose(snake_case_,							(3, 2, 0, 1)    )
				elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case_    ) not in pt_model_dict:
						# linear layer
						SCREAMING_SNAKE_CASE_						 = flax_key_tuple[:-1] + ("""weight""",)
						SCREAMING_SNAKE_CASE_						 = flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
						SCREAMING_SNAKE_CASE_						 = flax_key_tuple[:-1] + ("""weight""",)

				# adding batch stats from flax batch norm to pt
				elif "mean" in flax_key_tuple[-1]:
						SCREAMING_SNAKE_CASE_						 = flax_key_tuple[:-1] + ("""running_mean""",)
				elif "var" in flax_key_tuple[-1]:
						SCREAMING_SNAKE_CASE_						 = flax_key_tuple[:-1] + ("""running_var""",)

				if "batch_stats" in flax_state:
						SCREAMING_SNAKE_CASE_						 = """.""".join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
				else:
						SCREAMING_SNAKE_CASE_						 = """.""".join(snake_case_    )

				# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
				SCREAMING_SNAKE_CASE_						 = {}
				# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
				for key in pt_model_dict:
						SCREAMING_SNAKE_CASE_						 = key.split('''.'''    )
						SCREAMING_SNAKE_CASE_						 = None
						if key_components[-3::2] == ["parametrizations", "original0"]:
								SCREAMING_SNAKE_CASE_						 = key_components[-2] + """_g"""
						elif key_components[-3::2] == ["parametrizations", "original1"]:
								SCREAMING_SNAKE_CASE_						 = key_components[-2] + """_v"""
						if name is not None:
								SCREAMING_SNAKE_CASE_						 = key_components[:-3] + [name]
								SCREAMING_SNAKE_CASE_						 = """.""".join(snake_case_    )
								SCREAMING_SNAKE_CASE_						 = key

				if flax_key in special_pt_names:
						SCREAMING_SNAKE_CASE_						 = special_pt_names[flax_key]

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
								    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'    )
						else:
								# add weight to pytorch dict
								SCREAMING_SNAKE_CASE_						 = np.asarray(snake_case_    ) if not isinstance(snake_case_,							np.ndarray    ) else flax_tensor
								SCREAMING_SNAKE_CASE_						 = torch.from_numpy(snake_case_    )
								# remove from missing keys
								missing_keys.remove(snake_case_    )
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(snake_case_    )

		pt_model.load_state_dict(snake_case_    )

		# re-transform missing_keys to list
		SCREAMING_SNAKE_CASE_						 = list(snake_case_    )

		if len(snake_case_    ) > 0:
				logger.warning(
				    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
				    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
				    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
				    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
				    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
				    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
				    ''' FlaxBertForSequenceClassification model).'''    )
		else:
				logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'    )
		if len(snake_case_    ) > 0:
				logger.warning(
				    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
				    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
				    ''' use it for predictions and inference.'''    )
		else:
				logger.warning(
				    F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
				    '''If your task is similar to the task the model of the checkpoint was trained on, '''
				    F'you can already use {pt_model.__class__.__name__} for predictions without further training.'    )

		return pt_model

"""simple docstring"""




from collections.abc import Sequence
from queue import Queue





class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







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







							def __repr__(self    ):

									"""simple docstring"""







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







class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self._update_tree(self.root       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""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       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											else:
													# range in left child tree and right child tree
													return self.fn(
													    self._query_range(node.left       ,       SCREAMING_SNAKE_CASE_       ,       node.mid    )       ,       self._query_range(node.right       ,       node.mid + 1       ,       SCREAMING_SNAKE_CASE_    )       ,       )
									else:
											# range in right child tree
											return self._query_range(node.right       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									if self.root is not None:
											SCREAMING_SNAKE_CASE_						 = Queue()
											queue.put(self.root    )
											while not queue.empty():
													SCREAMING_SNAKE_CASE_						 = 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()