
'''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_       :   Dict   =       logging.get_logger(__name__)

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'linear'
    lowerCAmelCase_				    =		'cosine'
    lowerCAmelCase_				    =		'cosine_with_restarts'
    lowerCAmelCase_				    =		'polynomial'
    lowerCAmelCase_				    =		'constant'
    lowerCAmelCase_				    =		'constant_with_warmup'
    lowerCAmelCase_				    =		'piecewise_constant'







def 		A_       (	_lowerCAmelCase   :    Optimizer				,	_lowerCAmelCase   :    int = -1							):



      """simple docstring"""




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







def 		A_       (	_lowerCAmelCase   :    Optimizer				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int = -1							):



      """simple docstring"""




      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 		A_       (	_lowerCAmelCase   :    Optimizer				,	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    int = -1							):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   {}
      _lowerCamelCase				:   str								=   step_rules.split(","							)
      for rule_str in rule_list[:-1]:
            _lowerCamelCase						,		_lowerCamelCase				:   Any								=   rule_str.split(":"							)
            _lowerCamelCase				:   List[Any]								=   int(_lowerCAmelCase							)
            _lowerCamelCase				:   int								=   float(_lowerCAmelCase							)
            _lowerCamelCase				:   Any								=   value
      _lowerCamelCase				:   Union[str, Any]								=   float(rule_list[-1]							)

      def create_rules_function(_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    Optional[Any]							):
            def rule_func(_lowerCAmelCase   :    int							) -> float:
                  _lowerCamelCase				:   Tuple								=   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

      _lowerCamelCase				:   Union[str, Any]								=   create_rules_function(_lowerCAmelCase				,	_lowerCAmelCase							)

      return LambdaLR(_lowerCAmelCase				,	_lowerCAmelCase				,	last_epoch=_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    Dict=-1							):



      """simple docstring"""




      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 		A_       (	_lowerCAmelCase   :    Optimizer				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    float = 0.5				,	_lowerCAmelCase   :    int = -1							):



      """simple docstring"""




      def lr_lambda(_lowerCAmelCase   :    Union[str, Any]							):
            if current_step < num_warmup_steps:
                  return float(_lowerCAmelCase							) / float(max(1				,	_lowerCAmelCase							)							)
            _lowerCamelCase				:   Optional[Any]								=   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 		A_       (	_lowerCAmelCase   :    Optimizer				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int = 1				,	_lowerCAmelCase   :    int = -1							):



      """simple docstring"""




      def lr_lambda(_lowerCAmelCase   :    Optional[Any]							):
            if current_step < num_warmup_steps:
                  return float(_lowerCAmelCase							) / float(max(1				,	_lowerCAmelCase							)							)
            _lowerCamelCase				:   List[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(_lowerCAmelCase							) * progress) % 1.0)							))							)

      return LambdaLR(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    Union[str, Any]=1E-7				,	_lowerCAmelCase   :    Tuple=1.0				,	_lowerCAmelCase   :    int=-1							):



      """simple docstring"""




      _lowerCamelCase				:   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(_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:
                  _lowerCamelCase				:   Any								=   lr_init - lr_end
                  _lowerCamelCase				:   List[Any]								=   num_training_steps - num_warmup_steps
                  _lowerCamelCase				:   Optional[int]								=   1 - (current_step - num_warmup_steps) / decay_steps
                  _lowerCamelCase				:   Dict								=   lr_range * pct_remaining**power + lr_end
                  return decay / lr_init  # as LambdaLR multiplies by lr_init

      return LambdaLR(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)


UpperCAmelCase_       :   Optional[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 		A_       (	_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				,	):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   SchedulerType(_lowerCAmelCase							)
      _lowerCamelCase				:   List[str]								=   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							)

'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase_       :   Tuple   =       {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
 from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
 import sys

 UpperCAmelCase_       :   Tuple   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)