


'''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__						:   Union[str, Any]				=    logging.get_logger(__name__)





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






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







def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optimizer ,       UpperCAmelCase_       :							int = -1				)	->						Dict:
     return LambdaLR(UpperCAmelCase_ ,       lambda UpperCAmelCase_				: 1 ,       last_epoch=UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optimizer ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int = -1				)	->						str:

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

     return LambdaLR(UpperCAmelCase_ ,       UpperCAmelCase_ ,       last_epoch=UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optimizer ,       UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							int = -1				)	->						Tuple:
     __lowerCamelCase  :      Dict														=						{}
     __lowerCamelCase  :      Optional[int]														=						step_rules.split(','				)
     for rule_str in rule_list[:-1]:
          __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						rule_str.split(':'				)
          __lowerCamelCase  :      Dict														=						int(UpperCAmelCase_				)
          __lowerCamelCase  :      Tuple														=						float(UpperCAmelCase_				)
          __lowerCamelCase  :      int														=						value
     __lowerCamelCase  :      str														=						float(rule_list[-1]				)

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

          return rule_func

     __lowerCamelCase  :      Optional[Any]														=						create_rules_function(UpperCAmelCase_ ,       UpperCAmelCase_				)

     return LambdaLR(UpperCAmelCase_ ,       UpperCAmelCase_ ,       last_epoch=UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							Dict=-1				)	->						Optional[int]:

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

     return LambdaLR(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optimizer ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							float = 0.5 ,       UpperCAmelCase_       :							int = -1				)	->						Any:

     def lr_lambda(UpperCAmelCase_       :							Dict				):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase_				) / float(max(1 ,       UpperCAmelCase_				)				)
          __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(UpperCAmelCase_				) * 2.0 * progress				))				)

     return LambdaLR(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optimizer ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int = 1 ,       UpperCAmelCase_       :							int = -1				)	->						List[str]:

     def lr_lambda(UpperCAmelCase_       :							Union[str, Any]				):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase_				) / float(max(1 ,       UpperCAmelCase_				)				)
          __lowerCamelCase  :      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(UpperCAmelCase_				) * progress) % 1.0)				))				)

     return LambdaLR(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							Tuple=1e-7 ,       UpperCAmelCase_       :							List[Any]=1.0 ,       UpperCAmelCase_       :							Tuple=-1				)	->						List[str]:
     __lowerCamelCase  :      List[Any]														=						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_       :							int				):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase_				) / float(max(1 ,       UpperCAmelCase_				)				)
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               __lowerCamelCase  :      List[Any]														=						lr_init - lr_end
               __lowerCamelCase  :      List[str]														=						num_training_steps - num_warmup_steps
               __lowerCamelCase  :      str														=						1 - (current_step - num_warmup_steps) / decay_steps
               __lowerCamelCase  :      List[Any]														=						lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_				)


A__						:   Any				=    {
    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__   (      UpperCAmelCase_       :							Union[str, SchedulerType] ,       UpperCAmelCase_       :							Optimizer ,       UpperCAmelCase_       :							Optional[str] = None ,       UpperCAmelCase_       :							Optional[int] = None ,       UpperCAmelCase_       :							Optional[int] = None ,       UpperCAmelCase_       :							int = 1 ,       UpperCAmelCase_       :							float = 1.0 ,       UpperCAmelCase_       :							int = -1 ,       )	->						List[Any]:
     __lowerCamelCase  :      List[str]														=						SchedulerType(UpperCAmelCase_				)
     __lowerCamelCase  :      List[Any]														=						TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(UpperCAmelCase_ ,       last_epoch=UpperCAmelCase_				)

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

     # 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(UpperCAmelCase_ ,       num_warmup_steps=UpperCAmelCase_ ,       last_epoch=UpperCAmelCase_				)

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

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

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










'''simple docstring'''



from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

A__						:   Dict				=    [
    """python""",
    """tqdm""",
    """regex""",
    """requests""",
    """packaging""",
    """filelock""",
    """numpy""",
    """tokenizers""",
    """huggingface-hub""",
    """safetensors""",
    """accelerate""",
    """pyyaml""",
]

for pkg in pkgs_to_check_at_runtime:
      if pkg in deps:
            if pkg == "tokenizers":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_tokenizers_available

                  if not is_tokenizers_available():
                        continue  # not required, check version only if installed
            elif pkg == "accelerate":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_accelerate_available

                  # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
                  # Transformers with PyTorch
                  if not is_accelerate_available():
                        continue  # not required, check version only if installed

            require_version_core(deps[pkg])
      else:
            raise ValueError(f'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							List[Any]=None				)	->						List[Any]:
     require_version(deps[pkg] ,       UpperCAmelCase_				)






