"""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				:		str	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Any		 =		"linear"
     _UpperCamelCase							:      List[Any]		 =		"cosine"
     _UpperCamelCase							:      Dict		 =		"cosine_with_restarts"
     _UpperCamelCase							:      Optional[Any]		 =		"polynomial"
     _UpperCamelCase							:      Any		 =		"constant"
     _UpperCamelCase							:      List[Any]		 =		"constant_with_warmup"
     _UpperCamelCase							:      Tuple		 =		"piecewise_constant"







def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	int = -1     )	->				int:
  return LambdaLR(_lowerCamelCase      ,lambda _lowerCamelCase     : 1      ,last_epoch=_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	int = -1     )	->				List[str]:

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

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

  return LambdaLR(_lowerCamelCase      ,_lowerCamelCase      ,last_epoch=_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	Union[str, Any]=-1     )	->				Optional[int]:

  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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	float = 0.5      ,_lowerCamelCase						:	int = -1     )	->				Union[str, Any]:

  def lr_lambda(_lowerCamelCase						:	Optional[int]     ):
    if current_step < num_warmup_steps:
      return float(_lowerCamelCase     ) / float(max(1      ,_lowerCamelCase     )     )
    _lowerCAmelCase						:	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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	int = 1      ,_lowerCamelCase						:	int = -1     )	->				Tuple:

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

  return LambdaLR(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )


_a				:		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					(      _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      ,)	->				List[str]:
  _lowerCAmelCase						:	str        =					SchedulerType(_lowerCamelCase     )
  _lowerCAmelCase						:	Any        =					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"""




import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_a				:		List[Any]	   =       logging.get_logger(__name__)
class 						__A							:
     _UpperCamelCase							:      str		 =		None







@experimental
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Optional[int]     )	->				List[Any]:
  if ParallelBackendConfig.backend_name is None:
    return _map_with_multiprocessing_pool(
        _lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )

  return _map_with_joblib(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	str      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Tuple     )	->				Union[str, Any]:
  _lowerCAmelCase						:	int        =					num_proc if num_proc <= len(_lowerCamelCase     ) else len(_lowerCamelCase     )
  _lowerCAmelCase						:	Union[str, Any]        =					[]  # We organize the splits ourselve (contiguous splits)
  for index in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					len(_lowerCamelCase     ) // num_proc
    _lowerCAmelCase						:	str        =					len(_lowerCamelCase     ) % num_proc
    _lowerCAmelCase						:	Tuple        =					div * index + min(_lowerCamelCase      ,_lowerCamelCase     )
    _lowerCAmelCase						:	int        =					start + div + (1 if index < mod else 0)
    split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)     )

  if len(_lowerCamelCase     ) != sum(len(i[1]     ) for i in split_kwds     ):
    raise ValueError(
        f"Error dividing inputs iterable among processes. "
        f"Total number of objects {len(_lowerCamelCase     )}, "
        f"length: {sum(len(i[1]     ) for i in split_kwds     )}"     )

  logger.info(
      f"Spawning {num_proc} processes for {len(_lowerCamelCase     )} objects in slices of {[len(i[1]     ) for i in split_kwds]}"     )
  _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					None, None
  if not disable_tqdm:
    _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[Any]        =					(RLock(),), tqdm.set_lock
  with Pool(_lowerCamelCase      ,initargs=_lowerCamelCase      ,initializer=_lowerCamelCase     ) as pool:
    _lowerCAmelCase						:	str        =					pool.map(_lowerCamelCase      ,_lowerCamelCase     )
  logger.info(f"Finished {num_proc} processes"     )
  _lowerCAmelCase						:	int        =					[obj for proc_res in mapped for obj in proc_res]
  logger.info(f"Unpacked {len(_lowerCamelCase     )} objects"     )

  return mapped





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	Dict      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	str     )	->				Optional[Any]:
  # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
  # and it requires monkey-patching joblib internal classes which is subject to change
  import joblib

  with joblib.parallel_backend(ParallelBackendConfig.backend_name      ,n_jobs=_lowerCamelCase     ):
    return joblib.Parallel()(
        joblib.delayed(_lowerCamelCase     )((function, obj, types, None, True, None)     ) for obj in iterable     )






@experimental
@contextlib.contextmanager
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				Any:
  _lowerCAmelCase						:	List[Any]        =					backend_name

  if backend_name == "spark":
    from joblibspark import register_spark

    register_spark()

    # TODO: call create_cache_and_write_probe if "download" in steps
    # TODO: raise NotImplementedError when Dataset.map etc is called

  try:
    yield
  finally:
    _lowerCAmelCase						:	Optional[Any]        =					None




