



"""simple docstring"""

from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any




@dataclass
class SCREAMING_SNAKE_CASE__					:
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					None
						_lowerCAmelCase							=					None







def       __magic_name__    (   ):
							__a		:		Optional[Any]									=       Node(1					)
							__a		:		List[str]									=       Node(2					)
							__a		:		Union[str, Any]									=       Node(3					)
							__a		:		Tuple									=       Node(4					)
							__a		:		int									=       Node(5					)
							return tree





def       __magic_name__    (   _lowerCamelCase				: Node | None					):
							return [root.data, *preorder(root.left					), *preorder(root.right					)] if root else []





def       __magic_name__    (   _lowerCamelCase				: Node | None					):
							return postorder(root.left					) + postorder(root.right					) + [root.data] if root else []





def       __magic_name__    (   _lowerCamelCase				: Node | None					):
							return [*inorder(root.left					), root.data, *inorder(root.right					)] if root else []





def       __magic_name__    (   _lowerCamelCase				: Node | None					):
							return (max(height(root.left					)  ,       height(root.right					)					) + 1) if root else 0





def       __magic_name__    (   _lowerCamelCase				: Node | None					):
							__a		:		list[Any]									=       []

							if root is None:
														return output

							__a		:		Tuple									=       deque([root]					)

							while process_queue:
														__a		:		Optional[Any]									=       process_queue.popleft()
														output.append(node.data					)

														if node.left:
																					process_queue.append(node.left					)
														if node.right:
																					process_queue.append(node.right					)
							return output





def       __magic_name__    (   _lowerCamelCase				: Node | None  ,       _lowerCamelCase				: int					):
							__a		:		list[Any]									=       []

							def populate_output(_lowerCamelCase				: Node | None  ,       _lowerCamelCase				: int					) -> None:
														if not root:
																					return
														if level == 1:
																					output.append(root.data					)
														elif level > 1:
																					populate_output(root.left  ,       level - 1					)
																					populate_output(root.right  ,       level - 1					)

							populate_output(_lowerCamelCase  ,       _lowerCamelCase					)
							return output





def       __magic_name__    (   _lowerCamelCase				: Node | None  ,       _lowerCamelCase				: int					):
							__a		:		list[Any]									=       []

							def populate_output(_lowerCamelCase				: Node | None  ,       _lowerCamelCase				: int					) -> None:
														if root is None:
																					return
														if level == 1:
																					output.append(root.data					)
														elif level > 1:
																					populate_output(root.right  ,       level - 1					)
																					populate_output(root.left  ,       level - 1					)

							populate_output(_lowerCamelCase  ,       _lowerCamelCase					)
							return output





def       __magic_name__    (   _lowerCamelCase				: Node | None					):
							if root is None:
														return []

							__a		:		list[Sequence[Node | None]]									=       []

							__a		:		List[str]									=       0
							__a		:		Optional[Any]									=       height(_lowerCamelCase					)

							for h in range(1  ,       height_tree + 1					):
														if not flag:
																					output.append(get_nodes_from_left_to_right(_lowerCamelCase  ,       _lowerCamelCase					)					)
																					__a		:		Any									=       1
														else:
																					output.append(get_nodes_from_right_to_left(_lowerCamelCase  ,       _lowerCamelCase					)					)
																					__a		:		Optional[Any]									=       0

							return output





def       __magic_name__    (   ):  # Main function for testing.
							__a		:		Union[str, Any]									=       make_tree()
							print(F'''In-order Traversal: {inorder(_lowerCamelCase					)}'''					)
							print(F'''Pre-order Traversal: {preorder(_lowerCamelCase					)}'''					)
							print(F'''Post-order Traversal: {postorder(_lowerCamelCase					)}'''  ,       """\n"""					)

							print(F'''Height of Tree: {height(_lowerCamelCase					)}'''  ,       """\n"""					)

							print("""Complete Level Order Traversal: """					)
							print(level_order(_lowerCamelCase					)  ,       """\n"""					)

							print("""Level-wise order Traversal: """					)

							for level in range(1  ,       height(_lowerCamelCase					) + 1					):
														print(F'''Level {level}:'''  ,       get_nodes_from_left_to_right(_lowerCamelCase  ,       level=_lowerCamelCase					)					)

							print("""\nZigZag order Traversal: """					)
							print(zigzag(_lowerCamelCase					)					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()






"""simple docstring"""

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowercase__			   =  logging.get_logger(__name__)




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"linear"
						_lowerCAmelCase							=					"cosine"
						_lowerCAmelCase							=					"cosine_with_restarts"
						_lowerCAmelCase							=					"polynomial"
						_lowerCAmelCase							=					"constant"
						_lowerCAmelCase							=					"constant_with_warmup"
						_lowerCAmelCase							=					"piecewise_constant"







def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int = -1					):
							return LambdaLR(_lowerCamelCase  ,       lambda _lowerCamelCase					: 1  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: int					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1.0  ,       _lowerCamelCase					)					)
														return 1.0

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int = -1					):
							__a		:		Optional[int]									=       {}
							__a		:		Any									=       step_rules.split(""","""					)
							for rule_str in rule_list[:-1]:
														__a							,     __a		:		int									=       rule_str.split(""":"""					)
														__a		:		Optional[int]									=       int(_lowerCamelCase					)
														__a		:		str									=       float(_lowerCamelCase					)
														__a		:		int									=       value
							__a		:		Dict									=       float(rule_list[-1]					)

							def create_rules_function(_lowerCamelCase				: str  ,       _lowerCamelCase				: Tuple					):
														def rule_func(_lowerCamelCase				: int					) -> float:
																					__a		:		Optional[Any]									=       sorted(rules_dict.keys()					)
																					for i, sorted_step in enumerate(_lowerCamelCase					):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							__a		:		Optional[int]									=       create_rules_function(_lowerCamelCase  ,       _lowerCamelCase					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: str=-1					):

							def lr_lambda(_lowerCamelCase				: int					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														return max(
														    0.0  ,       float(num_training_steps - current_step					) / float(max(1  ,       num_training_steps - num_warmup_steps					)					)					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: float = 0.5  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Any					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		Dict									=       float(current_step - num_warmup_steps					) / float(max(1  ,       num_training_steps - num_warmup_steps					)					)
														return max(0.0  ,       0.5 * (1.0 + math.cos(math.pi * float(_lowerCamelCase					) * 2.0 * progress					))					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Optional[int]					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		Dict									=       float(current_step - num_warmup_steps					) / float(max(1  ,       num_training_steps - num_warmup_steps					)					)
														if progress >= 1.0:
																					return 0.0
														return max(0.0  ,       0.5 * (1.0 + math.cos(math.pi * ((float(_lowerCamelCase					) * progress) % 1.0)					))					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[Any]=1E-7  ,       _lowerCamelCase				: Optional[int]=1.0  ,       _lowerCamelCase				: Optional[int]=-1					):
							__a		:		Union[str, Any]									=       optimizer.defaults["""lr"""]
							if not (lr_init > lr_end):
														raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''					)

							def lr_lambda(_lowerCamelCase				: int					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					__a		:		Tuple									=       lr_init - lr_end
																					__a		:		int									=       num_training_steps - num_warmup_steps
																					__a		:		Optional[int]									=       1 - (current_step - num_warmup_steps) / decay_steps
																					__a		:		List[str]									=       lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)


lowercase__			   =  {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def       __magic_name__    (   _lowerCamelCase				: Union[str, SchedulerType]  ,       _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: Optional[str] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: float = 1.0  ,       _lowerCamelCase				: int = -1  ,       ):
							__a		:		int									=       SchedulerType(_lowerCamelCase					)
							__a		:		Optional[int]									=       TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(_lowerCamelCase  ,       step_rules=_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

							# All other schedulers require `num_warmup_steps`
							if num_warmup_steps is None:
														raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''					)

							if name == SchedulerType.CONSTANT_WITH_WARMUP:
														return schedule_func(_lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

							# All other schedulers require `num_training_steps`
							if num_training_steps is None:
														raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''					)

							if name == SchedulerType.COSINE_WITH_RESTARTS:
														return schedule_func(
														    _lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       num_training_steps=_lowerCamelCase  ,       num_cycles=_lowerCamelCase  ,       last_epoch=_lowerCamelCase  ,       )

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    _lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       num_training_steps=_lowerCamelCase  ,       power=_lowerCamelCase  ,       last_epoch=_lowerCamelCase  ,       )

							return schedule_func(
							    _lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       num_training_steps=_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

