




"""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


_lowerCamelCase  :			str        =       logging.get_logger(__name__)

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    List[str]         =   """linear"""
				__lowerCAmelCase    :    Dict         =   """cosine"""
				__lowerCAmelCase    :    Tuple         =   """cosine_with_restarts"""
				__lowerCAmelCase    :    int         =   """polynomial"""
				__lowerCAmelCase    :    Any         =   """constant"""
				__lowerCAmelCase    :    List[str]         =   """constant_with_warmup"""
				__lowerCAmelCase    :    Union[str, Any]         =   """piecewise_constant"""

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase = -1	):




	"""simple docstring"""






	return LambdaLR(_UpperCAmelCase  ,      lambda _UpperCAmelCase	: 1  ,      last_epoch=_UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase = -1	):




	"""simple docstring"""






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

	return LambdaLR(_UpperCAmelCase  ,      _UpperCAmelCase  ,      last_epoch=_UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase = -1	):




	"""simple docstring"""






	A_  :				str	   =		{}
	A_  :				Union[str, Any]	   =		step_rules.split(''','''	)
	for rule_str in rule_list[:-1]:
		A_   , A_  :				Union[str, Any]	   =		rule_str.split(''':'''	)
		A_  :				Union[str, Any]	   =		int(_UpperCAmelCase	)
		A_  :				int	   =		float(_UpperCAmelCase	)
		A_  :				Dict	   =		value
	A_  :				Any	   =		float(rule_list[-1]	)

	def create_rules_function(_UpperCAmelCase  ,      _UpperCAmelCase	):
		def rule_func(_UpperCAmelCase	) -> float:
			A_  :				int	   =		sorted(rules_dict.keys()	)
			for i, sorted_step in enumerate(_UpperCAmelCase	):
				if steps < sorted_step:
					return rules_dict[sorted_steps[i]]
			return last_lr_multiple

		return rule_func

	A_  :				Optional[Any]	   =		create_rules_function(_UpperCAmelCase  ,      _UpperCAmelCase	)

	return LambdaLR(_UpperCAmelCase  ,      _UpperCAmelCase  ,      last_epoch=_UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=-1	):




	"""simple docstring"""






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

	return LambdaLR(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase = 0.5  ,      _UpperCAmelCase = -1	):




	"""simple docstring"""






	def lr_lambda(_UpperCAmelCase	):
		if current_step < num_warmup_steps:
			return float(_UpperCAmelCase	) / float(max(1  ,      _UpperCAmelCase	)	)
		A_  :				List[str]	   =		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(_UpperCAmelCase	) * 2.0 * progress	))	)

	return LambdaLR(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase = 1  ,      _UpperCAmelCase = -1	):




	"""simple docstring"""






	def lr_lambda(_UpperCAmelCase	):
		if current_step < num_warmup_steps:
			return float(_UpperCAmelCase	) / float(max(1  ,      _UpperCAmelCase	)	)
		A_  :				Any	   =		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(_UpperCAmelCase	) * progress) % 1.0)	))	)

	return LambdaLR(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=1E-7  ,      _UpperCAmelCase=1.0  ,      _UpperCAmelCase=-1	):




	"""simple docstring"""






	A_  :				Dict	   =		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(_UpperCAmelCase	):
		if current_step < num_warmup_steps:
			return float(_UpperCAmelCase	) / float(max(1  ,      _UpperCAmelCase	)	)
		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_  :				str	   =		num_training_steps - num_warmup_steps
			A_  :				List[str]	   =		1 - (current_step - num_warmup_steps) / decay_steps
			A_  :				Optional[Any]	   =		lr_range * pct_remaining**power + lr_end
			return decay / lr_init  # as LambdaLR multiplies by lr_init

	return LambdaLR(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)


_lowerCamelCase  :			int        =       {
    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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = 1  ,      _UpperCAmelCase = 1.0  ,      _UpperCAmelCase = -1  ,      ):




	"""simple docstring"""






	A_  :				str	   =		SchedulerType(_UpperCAmelCase	)
	A_  :				List[str]	   =		TYPE_TO_SCHEDULER_FUNCTION[name]
	if name == SchedulerType.CONSTANT:
		return schedule_func(_UpperCAmelCase  ,      last_epoch=_UpperCAmelCase	)

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

	# 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(_UpperCAmelCase  ,      num_warmup_steps=_UpperCAmelCase  ,      last_epoch=_UpperCAmelCase	)

	# 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(
		    _UpperCAmelCase  ,      num_warmup_steps=_UpperCAmelCase  ,      num_training_steps=_UpperCAmelCase  ,      num_cycles=_UpperCAmelCase  ,      last_epoch=_UpperCAmelCase  ,      )

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

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











"""simple docstring"""

from queue import PriorityQueue
from typing import Any

import numpy as np

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      ):




	"""simple docstring"""






	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A_  :				Tuple	   =		cst_fwd.get(_UpperCAmelCase  ,      np.inf	)
		A_  :				Any	   =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt)	)
			A_  :				Tuple	   =		new_cost_f
			A_  :				Dict	   =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A_  :				Optional[Any]	   =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Tuple	   =		-1

	A_  :				Any	   =		set()
	A_  :				Tuple	   =		set()
	A_  :				Optional[int]	   =		{source: 0}
	A_  :				Dict	   =		{destination: 0}
	A_  :				Any	   =		{source: None}
	A_  :				Optional[Any]	   =		{destination: None}
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()

	A_  :				Union[str, Any]	   =		np.inf

	queue_forward.put((0, source)	)
	queue_backward.put((0, destination)	)

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A_   , A_  :				Union[str, Any]	   =		queue_forward.get()
		visited_forward.add(_UpperCAmelCase	)

		A_   , A_  :				Optional[int]	   =		queue_backward.get()
		visited_backward.add(_UpperCAmelCase	)

		A_  :				Any	   =		pass_and_relaxation(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      )

		A_  :				Dict	   =		pass_and_relaxation(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      )

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A_  :				Optional[Any]	   =		shortest_distance
	return shortest_path_distance


_lowerCamelCase  :			Optional[Any]        =       {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
_lowerCamelCase  :			Any        =       {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()





