



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       _SCREAMING_SNAKE_CASE	(__lowerCamelCase ):
     lowerCAmelCase					=			"""linear"""
     lowerCAmelCase					=			"""cosine"""
     lowerCAmelCase					=			"""cosine_with_restarts"""
     lowerCAmelCase					=			"""polynomial"""
     lowerCAmelCase					=			"""constant"""
     lowerCAmelCase					=			"""constant_with_warmup"""
     lowerCAmelCase					=			"""piecewise_constant"""






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Optional[Any] = -1     ):



 """simple docstring"""





 return LambdaLR(lowerCamelCase_		,	lambda __lowerCamelCase     : 1		,	last_epoch=lowerCamelCase_     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     List[Any] = -1     ):



 """simple docstring"""





 def lr_lambda(__lowerCamelCase   :     Union[str, Any]     ):
  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 	_lowerCAmelCase						(				__lowerCamelCase   :     Any		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Any = -1     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{}
 __SCREAMING_SNAKE_CASE       :    List[str]       =	step_rules.split(","     )
 for rule_str in rule_list[:-1]:
  __SCREAMING_SNAKE_CASE       :    str       =	rule_str.split(":"     )
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	int(lowerCamelCase_     )
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	float(lowerCamelCase_     )
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	value
 __SCREAMING_SNAKE_CASE       :    str       =	float(rule_list[-1]     )

 def create_rules_function(__lowerCamelCase   :     int		,	__lowerCamelCase   :     str     ):
  def rule_func(__lowerCamelCase   :     List[str]     ) -> float:
   __SCREAMING_SNAKE_CASE       :    Dict       =	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

 __SCREAMING_SNAKE_CASE       :    str       =	create_rules_function(lowerCamelCase_		,	lowerCamelCase_     )

 return LambdaLR(lowerCamelCase_		,	lowerCamelCase_		,	last_epoch=lowerCamelCase_     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[Any]=-1     ):



 """simple docstring"""





 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 	_lowerCAmelCase						(				__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     str = 0.5		,	__lowerCamelCase   :     List[Any] = -1     ):



 """simple docstring"""





 def lr_lambda(__lowerCamelCase   :     Any     ):
  if current_step < num_warmup_steps:
   return float(lowerCamelCase_     ) / float(max(1		,	lowerCamelCase_     )     )
  __SCREAMING_SNAKE_CASE       :    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(lowerCamelCase_     ) * 2.0 * progress     ))     )

 return LambdaLR(lowerCamelCase_		,	lowerCamelCase_		,	lowerCamelCase_     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Dict = 1		,	__lowerCamelCase   :     Optional[int] = -1     ):



 """simple docstring"""





 def lr_lambda(__lowerCamelCase   :     List[Any]     ):
  if current_step < num_warmup_steps:
   return float(lowerCamelCase_     ) / float(max(1		,	lowerCamelCase_     )     )
  __SCREAMING_SNAKE_CASE       :    str       =	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 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Any		,	__lowerCamelCase   :     Optional[Any]=1E-7		,	__lowerCamelCase   :     Optional[Any]=1.0		,	__lowerCamelCase   :     Optional[int]=-1     ):



 """simple docstring"""





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

 return LambdaLR(lowerCamelCase_		,	lowerCamelCase_		,	lowerCamelCase_     )


_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 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     Optional[int] = None		,	__lowerCamelCase   :     str = None		,	__lowerCamelCase   :     Tuple = None		,	__lowerCamelCase   :     Dict = 1		,	__lowerCamelCase   :     str = 1.0		,	__lowerCamelCase   :     str = -1		,	):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	SchedulerType(lowerCamelCase_     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	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_     )











import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment

@require_huggingface_suite
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	inspect.getfile(accelerate.test_utils				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	os.path.sep.join(
          mod_file.split(os.path.sep				)[:-1] + ["scripts", "external_deps", "test_metrics.py"]				)

      from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

      __SCREAMING_SNAKE_CASE       :    Tuple       =	test_metrics





     @require_cpu
     def 	__snake_case    (       self :							Union[str, Any]				)->List[Any]:
      debug_launcher(self.test_metrics.main     ,  num_processes=1				)





     @require_cpu
     def 	__snake_case    (       self :							str				)->List[str]:
      debug_launcher(self.test_metrics.main				)





     @require_single_gpu
     def 	__snake_case    (       self :							Union[str, Any]				)->str:
      self.test_metrics.main()






     @require_multi_gpu
     def 	__snake_case    (       self :							List[str]				)->int:
      print(F"""Found {torch.cuda.device_count()} devices."""				)
      __SCREAMING_SNAKE_CASE       :    Any       =	["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1				):
       execute_subprocess_async(UpperCamelCase     ,  env=os.environ.copy()				)






