



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        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Any      =  '''linear'''
						UpperCamelCase__      :			Any      =  '''cosine'''
						UpperCamelCase__      :			Optional[int]      =  '''cosine_with_restarts'''
						UpperCamelCase__      :			Union[str, Any]      =  '''polynomial'''
						UpperCamelCase__      :			List[str]      =  '''constant'''
						UpperCamelCase__      :			str      =  '''constant_with_warmup'''
						UpperCamelCase__      :			Dict      =  '''piecewise_constant'''







def      __lowercase       (			a__							,       a__ = -1       )    ->			Optional[Any]:
							return LambdaLR(a__							,       lambda a__       : 1							,       last_epoch=a__       )






def      __lowercase       (			a__							,       a__							,       a__ = -1       )    ->			List[Any]:

							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      __lowercase       (			a__							,       a__							,       a__ = -1       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= {}
							__SCREAMING_SNAKE_CASE									= step_rules.split(','       )
							for rule_str in rule_list[:-1]:
														__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= rule_str.split(':'       )
														__SCREAMING_SNAKE_CASE									= int(a__       )
														__SCREAMING_SNAKE_CASE									= float(a__       )
														__SCREAMING_SNAKE_CASE									= value
							__SCREAMING_SNAKE_CASE									= float(rule_list[-1]       )

							def create_rules_function(a__							,       a__       ):
														def rule_func(a__       ) -> float:
																					__SCREAMING_SNAKE_CASE									= 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

							__SCREAMING_SNAKE_CASE									= create_rules_function(a__							,       a__       )

							return LambdaLR(a__							,       a__							,       last_epoch=a__       )






def      __lowercase       (			a__							,       a__							,       a__							,       a__=-1       )    ->			Optional[Any]:

							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      __lowercase       (			a__							,       a__							,       a__							,       a__ = 0.5							,       a__ = -1       )    ->			Optional[Any]:

							def lr_lambda(a__       ):
														if current_step < num_warmup_steps:
																					return float(a__       ) / float(max(1							,       a__       )       )
														__SCREAMING_SNAKE_CASE									= 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      __lowercase       (			a__							,       a__							,       a__							,       a__ = 1							,       a__ = -1       )    ->			Union[str, Any]:

							def lr_lambda(a__       ):
														if current_step < num_warmup_steps:
																					return float(a__       ) / float(max(1							,       a__       )       )
														__SCREAMING_SNAKE_CASE									= 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      __lowercase       (			a__							,       a__							,       a__							,       a__=1E-7							,       a__=1.0							,       a__=-1       )    ->			Any:
							__SCREAMING_SNAKE_CASE									= 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:
																					__SCREAMING_SNAKE_CASE									= lr_init - lr_end
																					__SCREAMING_SNAKE_CASE									= num_training_steps - num_warmup_steps
																					__SCREAMING_SNAKE_CASE									= 1 - (current_step - num_warmup_steps) / decay_steps
																					__SCREAMING_SNAKE_CASE									= lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(a__							,       a__							,       a__       )


lowerCAmelCase__		:					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      __lowercase       (			a__							,       a__							,       a__ = None							,       a__ = None							,       a__ = None							,       a__ = 1							,       a__ = 1.0							,       a__ = -1							,       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= SchedulerType(a__       )
							__SCREAMING_SNAKE_CASE									= 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__       )











from ..utils import DummyObject, requires_backends

class        UpperCAmelCase_  (       metaclass=UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  ['''speech''']






						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													requires_backends(self				,							['speech']		)







class        UpperCAmelCase_  (       metaclass=UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  ['''speech''']






						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													requires_backends(self				,							['speech']		)






