"""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


__A			   = logging.get_logger(__name__)


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase = -1 )  ->  Dict:
   return LambdaLR(a__       ,    lambda __UpperCAmelCase : 1       ,    last_epoch=a__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase = -1 )  ->  Any:


   def lr_lambda(__UpperCAmelCase ):
      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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase = -1 )  ->  Optional[Any]:
   lowercase__:   List[str]					=      {}
   lowercase__:   str					=      step_rules.split(''',''' )
   for rule_str in rule_list[:-1]:
      lowercase__, lowercase__:   str					=      rule_str.split(''':''' )
      lowercase__:   List[str]					=      int(a__ )
      lowercase__:   int					=      float(a__ )
      lowercase__:   Optional[int]					=      value
   lowercase__:   Optional[int]					=      float(rule_list[-1] )

   def create_rules_function(__UpperCAmelCase       ,    __UpperCAmelCase ):
      def rule_func(__UpperCAmelCase ) -> float:
         lowercase__:   Optional[int]					=      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

   lowercase__:   List[str]					=      create_rules_function(a__       ,    a__ )

   return LambdaLR(a__       ,    a__       ,    last_epoch=a__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=-1 )  ->  Union[str, Any]:


   def lr_lambda(__UpperCAmelCase ):
      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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase = 0.5       ,    __UpperCAmelCase = -1 )  ->  Optional[Any]:


   def lr_lambda(__UpperCAmelCase ):
      if current_step < num_warmup_steps:
         return float(a__ ) / float(max(1       ,    a__ ) )
      lowercase__:   List[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(a__ ) * 2.0 * progress )) )

   return LambdaLR(a__       ,    a__       ,    a__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase = 1       ,    __UpperCAmelCase = -1 )  ->  Optional[int]:


   def lr_lambda(__UpperCAmelCase ):
      if current_step < num_warmup_steps:
         return float(a__ ) / float(max(1       ,    a__ ) )
      lowercase__:   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(a__ ) * progress) % 1.0) )) )

   return LambdaLR(a__       ,    a__       ,    a__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=1e-7       ,    __UpperCAmelCase=1.0       ,    __UpperCAmelCase=-1 )  ->  Any:
   lowercase__:   Optional[int]					=      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(a__ ) / float(max(1       ,    a__ ) )
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         lowercase__:   List[str]					=      lr_init - lr_end
         lowercase__:   Optional[Any]					=      num_training_steps - num_warmup_steps
         lowercase__:   Union[str, Any]					=      1 - (current_step - num_warmup_steps) / decay_steps
         lowercase__:   List[str]					=      lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(a__       ,    a__       ,    a__ )


__A			   = {
    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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase = None       ,    __UpperCAmelCase = None       ,    __UpperCAmelCase = None       ,    __UpperCAmelCase = 1       ,    __UpperCAmelCase = 1.0       ,    __UpperCAmelCase = -1       ,    )  ->  List[str]:
   lowercase__:   Optional[int]					=      SchedulerType(a__ )
   lowercase__:   int					=      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :       Union[str, Any]			   =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase :       Union[str, Any]			   =     [
      'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SEWForCTC',
      'SEWForSequenceClassification',
      'SEWModel',
      'SEWPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase :       int			   =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






