



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						=       logging.get_logger(__name__)



class 		_snake_case   (      __a					):





 """simple docstring"""



 lowerCamelCase_        =      '''linear'''
 lowerCamelCase_        =      '''cosine'''
 lowerCamelCase_        =      '''cosine_with_restarts'''
 lowerCamelCase_        =      '''polynomial'''
 lowerCamelCase_        =      '''constant'''
 lowerCamelCase_        =      '''constant_with_warmup'''
 lowerCamelCase_        =      '''piecewise_constant'''







def      UpperCAmelCase__			(				__snake_case    ,					__snake_case = -1				) ->			int:
  return LambdaLR(__snake_case    ,					lambda __snake_case				: 1    ,					last_epoch=__snake_case				)




def      UpperCAmelCase__			(				__snake_case    ,					__snake_case    ,					__snake_case = -1				) ->			Union[str, Any]:

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

  return LambdaLR(__snake_case    ,					__snake_case    ,					last_epoch=__snake_case				)




def      UpperCAmelCase__			(				__snake_case    ,					__snake_case    ,					__snake_case = -1				) ->			Union[str, Any]:
  _A							     =     {}
  _A							     =     step_rules.split(''','''				)
  for rule_str in rule_list[:-1]:
    _A							     =     rule_str.split(''':'''				)
    _A							     =     int(__snake_case				)
    _A							     =     float(__snake_case				)
    _A							     =     value
  _A							     =     float(rule_list[-1]				)

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

    return rule_func

  _A							     =     create_rules_function(__snake_case    ,					__snake_case				)

  return LambdaLR(__snake_case    ,					__snake_case    ,					last_epoch=__snake_case				)




def      UpperCAmelCase__			(				__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case=-1				) ->			int:

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

  return LambdaLR(__snake_case    ,					__snake_case    ,					__snake_case				)




def      UpperCAmelCase__			(				__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case = 0.5    ,					__snake_case = -1				) ->			Tuple:

  def lr_lambda(__snake_case				):
    if current_step < num_warmup_steps:
      return float(__snake_case				) / float(max(1    ,					__snake_case				)				)
    _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(__snake_case				) * 2.0 * progress				))				)

  return LambdaLR(__snake_case    ,					__snake_case    ,					__snake_case				)




def      UpperCAmelCase__			(				__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case = 1    ,					__snake_case = -1				) ->			str:

  def lr_lambda(__snake_case				):
    if current_step < num_warmup_steps:
      return float(__snake_case				) / float(max(1    ,					__snake_case				)				)
    _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(__snake_case				) * progress) % 1.0)				))				)

  return LambdaLR(__snake_case    ,					__snake_case    ,					__snake_case				)




def      UpperCAmelCase__			(				__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case=1e-7    ,					__snake_case=1.0    ,					__snake_case=-1				) ->			str:
  _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(__snake_case				):
    if current_step < num_warmup_steps:
      return float(__snake_case				) / float(max(1    ,					__snake_case				)				)
    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(__snake_case    ,					__snake_case    ,					__snake_case				)


__lowerCamelCase						=       {
    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      UpperCAmelCase__			(				__snake_case    ,					__snake_case    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = 1    ,					__snake_case = 1.0    ,					__snake_case = -1    ,					) ->			Optional[Any]:
  _A							     =     SchedulerType(__snake_case				)
  _A							     =     TYPE_TO_SCHEDULER_FUNCTION[name]
  if name == SchedulerType.CONSTANT:
    return schedule_func(__snake_case    ,					last_epoch=__snake_case				)

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

  # 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(__snake_case    ,					num_warmup_steps=__snake_case    ,					last_epoch=__snake_case				)

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

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

  return schedule_func(
      __snake_case    ,					num_warmup_steps=__snake_case    ,					num_training_steps=__snake_case    ,					last_epoch=__snake_case				)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_           =				{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				[
      '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MegaForCausalLM''',
      '''MegaForMaskedLM''',
      '''MegaForMultipleChoice''',
      '''MegaForQuestionAnswering''',
      '''MegaForSequenceClassification''',
      '''MegaForTokenClassification''',
      '''MegaModel''',
      '''MegaPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mega import (
        MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegaForCausalLM,
        MegaForMaskedLM,
        MegaForMultipleChoice,
        MegaForQuestionAnswering,
        MegaForSequenceClassification,
        MegaForTokenClassification,
        MegaModel,
        MegaPreTrainedModel,
    )

else:
  import sys

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