






"""simple docstring"""




from collections.abc import Sequence
from queue import Queue



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Optional[int]							,		__A					:  Dict							,		__A					:  Tuple							,		__A					:  int							,		__A					:  Optional[Any]=None							,		__A					:  Dict=None	):
						snake_case__			:				int       =  start
						snake_case__			:				Optional[Any]       =  end
						snake_case__			:				List[str]       =  val
						snake_case__			:				str       =  (start + end) // 2
						snake_case__			:				Tuple       =  left
						snake_case__			:				Optional[Any]       =  right






					def __repr__( self					:  List[str]	):
						return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  List[str]							,		__A					:  Sequence							,		__A					:  Optional[int]	):
						snake_case__			:				Optional[int]       =  collection
						snake_case__			:				Union[str, Any]       =  function
						if self.collection:
							snake_case__			:				Tuple       =  self._build_tree(0							,		len(__A	) - 1	)



					def 	_lowercase					( self					:  str							,		__A					:  Union[str, Any]							,		__A					:  Optional[Any]	):
						self._update_tree(self.root							,		__A							,		__A	)



					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  int							,		__A					:  Optional[Any]	):
						return self._query_range(self.root							,		__A							,		__A	)



					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  Tuple							,		__A					:  Any	):
						if start == end:
							return SegmentTreeNode(__A							,		__A							,		self.collection[start]	)
						snake_case__			:				Dict       =  (start + end) // 2
						snake_case__			:				Tuple       =  self._build_tree(__A							,		__A	)
						snake_case__			:				Tuple       =  self._build_tree(mid + 1							,		__A	)
						return SegmentTreeNode(__A							,		__A							,		self.fn(left.val							,		right.val	)							,		__A							,		__A	)



					def 	_lowercase					( self					:  Any							,		__A					:  str							,		__A					:  int							,		__A					:  Any	):
						if node.start == i and node.end == i:
							snake_case__			:				Optional[Any]       =  val
							return
						if i <= node.mid:
							self._update_tree(node.left							,		__A							,		__A	)
						else:
							self._update_tree(node.right							,		__A							,		__A	)
						snake_case__			:				Union[str, Any]       =  self.fn(node.left.val							,		node.right.val	)



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  List[Any]							,		__A					:  List[str]							,		__A					:  List[str]	):
						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							,		__A							,		__A	)
							else:
								# range in left child tree and right child tree
								return self.fn(
								    self._query_range(node.left							,		__A							,		node.mid	)							,		self._query_range(node.right							,		node.mid + 1							,		__A	)							,		)
						else:
							# range in right child tree
							return self._query_range(node.right							,		__A							,		__A	)






					def 	_lowercase					( self					:  List[Any]	):
						if self.root is not None:
							snake_case__			:				Union[str, Any]       =  Queue()
							queue.put(self.root	)
							while not queue.empty():
								snake_case__			:				Union[str, Any]       =  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 :      Optional[Any]										=				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()











from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				Optional[Any]       =  SMALL_MODEL_IDENTIFIER
						snake_case__			:				Any       =  "pt"
						snake_case__			:				Any       =  "tf"



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[Any]	):
						snake_case__			:				int       =  AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(__A	)



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Tuple	):
						snake_case__			:				List[Any]       =  TFAutoModel.from_pretrained(self.test_model							,		from_pt=__A	)
						model_tf.save_pretrained(__A	)



					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  "mock_framework"

						# Framework provided - return whatever the user provides
						snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model							,		__A	)
						self.assertEqual(__A							,		__A	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				Optional[int]       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				int       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)



					def 	_lowercase					( self					:  Dict	):
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				List[str]       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				Tuple       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
							with self.assertRaises(__A	):
								snake_case__			:				int       =  FeaturesManager.determine_framework(__A	)






					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	):
							snake_case__			:				List[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						snake_case__			:				Tuple       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				int       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Both in environment -> use PyTorch
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[int]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# Both not in environment -> raise error
						snake_case__			:				List[str]       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[Any]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							with self.assertRaises(__A	):
								snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)



