





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_   :				Union[str, Any]						      = logging.get_logger(__name__)
class 				SCREAMING_SNAKE_CASE__						(       a__    ):
      snake_case__       :  str       =		"linear"
      snake_case__       :  List[str]       =		"cosine"
      snake_case__       :  List[str]       =		"cosine_with_restarts"
      snake_case__       :  List[str]       =		"polynomial"
      snake_case__       :  int       =		"constant"
      snake_case__       :  Optional[int]       =		"constant_with_warmup"
      snake_case__       :  List[Any]       =		"piecewise_constant"







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Dict = -1       )			->	Optional[int]:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict						,					__A				:    Union[str, Any]						,					__A				:    Optional[Any] = -1       )			->	Any:

       """simple docstring"""

       def lr_lambda(__A				:    Tuple       ):
              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_		(    __A				:    List[str]						,					__A				:    Union[str, Any]						,					__A				:    int = -1       )			->	Tuple:

       """simple docstring"""

       a_      :	List[str]							=						{}
       a_      :	Dict							=						step_rules.split(','       )
       for rule_str in rule_list[:-1]:
              a_							,	a_      :	List[str]							=						rule_str.split(':'       )
              a_      :	str							=						int(__A       )
              a_      :	str							=						float(__A       )
              a_      :	int							=						value
       a_      :	Tuple							=						float(rule_list[-1]       )

       def create_rules_function(__A				:    Optional[int]						,					__A				:    int       ):
              def rule_func(__A				:    Optional[int]       ) -> float:
                     a_      :	Dict							=						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

       a_      :	List[Any]							=						create_rules_function(__A						,					__A       )

       return LambdaLR(__A						,					__A						,					last_epoch=__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Dict						,					__A				:    Dict						,					__A				:    Tuple=-1       )			->	int:

       """simple docstring"""

       def lr_lambda(__A				:    Union[str, Any]       ):
              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_		(    __A				:    List[Any]						,					__A				:    List[str]						,					__A				:    Tuple						,					__A				:    List[Any] = 0.5						,					__A				:    List[Any] = -1       )			->	Optional[int]:

       """simple docstring"""

       def lr_lambda(__A				:    List[str]       ):
              if current_step < num_warmup_steps:
                     return float(__A       ) / float(max(1						,					__A       )       )
              a_      :	Dict							=						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_		(    __A				:    str						,					__A				:    List[Any]						,					__A				:    List[str]						,					__A				:    Optional[int] = 1						,					__A				:    List[Any] = -1       )			->	Dict:

       """simple docstring"""

       def lr_lambda(__A				:    str       ):
              if current_step < num_warmup_steps:
                     return float(__A       ) / float(max(1						,					__A       )       )
              a_      :	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_		(    __A				:    Optional[Any]						,					__A				:    List[str]						,					__A				:    str						,					__A				:    Any=1e-7						,					__A				:    Tuple=1.0						,					__A				:    Tuple=-1       )			->	List[Any]:

       """simple docstring"""

       a_      :	List[str]							=						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				:    str       ):
              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:
                     a_      :	Union[str, Any]							=						lr_init - lr_end
                     a_      :	List[Any]							=						num_training_steps - num_warmup_steps
                     a_      :	List[Any]							=						1 - (current_step - num_warmup_steps) / decay_steps
                     a_      :	int							=						lr_range * pct_remaining**power + lr_end
                     return decay / lr_init  # as LambdaLR multiplies by lr_init

       return LambdaLR(__A						,					__A						,					__A       )


UpperCAmelCase_   :				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  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    List[str]						,					__A				:    List[str] = None						,					__A				:    str = None						,					__A				:    Dict = None						,					__A				:    Optional[Any] = 1						,					__A				:    Any = 1.0						,					__A				:    Any = -1						,					)			->	Dict:

       """simple docstring"""

       a_      :	int							=						SchedulerType(__A       )
       a_      :	Union[str, Any]							=						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 .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

UpperCAmelCase_   :				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  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int=None       )			->	int:

       """simple docstring"""

       require_version(deps[pkg]						,					__A       )


