
"""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


UpperCamelCase_					:						Tuple				= logging.get_logger(__name__)



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"linear"
			snake_case	      =				"cosine"
			snake_case	      =				"cosine_with_restarts"
			snake_case	      =				"polynomial"
			snake_case	      =				"constant"
			snake_case	      =				"constant_with_warmup"
			snake_case	      =				"piecewise_constant"





def 							A_     (__a				,   __a = -1				):

					'''simple docstring'''





					return LambdaLR(__a				,   lambda __a				: 1				,   last_epoch=__a				)
def 							A_     (__a				,   __a				,   __a = -1				):

					'''simple docstring'''





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

					return LambdaLR(__a				,   __a				,   last_epoch=__a				)
def 							A_     (__a				,   __a				,   __a = -1				):

					'''simple docstring'''





					A_				 =						{}
					A_				 =						step_rules.split(","				)
					for rule_str in rule_list[:-1]:
										A_ ,    A_				 =						rule_str.split(":"				)
										A_				 =						int(__a				)
										A_				 =						float(__a				)
										A_				 =						value
					A_				 =						float(rule_list[-1]				)

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

										return rule_func

					A_				 =						create_rules_function(__a				,   __a				)

					return LambdaLR(__a				,   __a				,   last_epoch=__a				)
def 							A_     (__a				,   __a				,   __a				,   __a=-1				):

					'''simple docstring'''





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

					return LambdaLR(__a				,   __a				,   __a				)
def 							A_     (__a				,   __a				,   __a				,   __a = 0.5				,   __a = -1				):

					'''simple docstring'''





					def lr_lambda(__a				):
										if current_step < num_warmup_steps:
															return float(__a				) / float(max(1				,   __a				)				)
										A_				 =						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(__a				) * 2.0 * progress				))				)

					return LambdaLR(__a				,   __a				,   __a				)
def 							A_     (__a				,   __a				,   __a				,   __a = 1				,   __a = -1				):

					'''simple docstring'''





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

					return LambdaLR(__a				,   __a				,   __a				)
def 							A_     (__a				,   __a				,   __a				,   __a=1e-7				,   __a=1.0				,   __a=-1				):

					'''simple docstring'''





					A_				 =						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(__a				):
										if current_step < num_warmup_steps:
															return float(__a				) / float(max(1				,   __a				)				)
										elif current_step > num_training_steps:
															return lr_end / lr_init  # as LambdaLR multiplies by lr_init
										else:
															A_				 =						lr_init - lr_end
															A_				 =						num_training_steps - num_warmup_steps
															A_				 =						1 - (current_step - num_warmup_steps) / decay_steps
															A_				 =						lr_range * pct_remaining**power + lr_end
															return decay / lr_init  # as LambdaLR multiplies by lr_init

					return LambdaLR(__a				,   __a				,   __a				)


UpperCamelCase_					:						Dict				= {
    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 							A_     (__a				,   __a				,   __a = None				,   __a = None				,   __a = None				,   __a = 1				,   __a = 1.0				,   __a = -1				,   ):

					'''simple docstring'''





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

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

					# 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(__a				,   num_warmup_steps=__a				,   last_epoch=__a				)

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

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

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






"""simple docstring"""





from __future__ import annotations

from collections import Counter
from random import random



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					str	)      -> None:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					float	)      -> None:







								"""simple docstring"""
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								A_				 =						probability




			def      lowerCamelCase__     (				self						:					Tuple	)      -> list[str]:







								"""simple docstring"""
								return list(self.connections	)






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str	)      -> str:







								"""simple docstring"""
								A_				 =						0
								A_				 =						random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(__a				,   __a				,   __a				)

					A_				 =						Counter(graph.get_nodes()				)
					A_				 =						start

					for _ in range(__a				):
										A_				 =						graph.transition(__a				)
										visited[node] += 1

					return visited


if __name__ == "__main__":
		import doctest

		doctest.testmod()




