





'''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


lowercase__		:     Tuple			=							logging.get_logger(__name__)
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'linear'
       _snake_case      :	Dict									=       'cosine'
       _snake_case      :	Optional[Any]									=       'cosine_with_restarts'
       _snake_case      :	List[Any]									=       'polynomial'
       _snake_case      :	Optional[int]									=       'constant'
       _snake_case      :	Tuple									=       'constant_with_warmup'
       _snake_case      :	int									=       'piecewise_constant'







def    a__							(							lowercase   :	Optimizer,   lowercase   :	int = -1 )						->						List[str]:





     """simple docstring"""

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







def    a__							(							lowercase   :	Optimizer,   lowercase   :	int,   lowercase   :	int = -1 )						->						Optional[Any]:





     """simple docstring"""

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

     return LambdaLR(lowercase,   lowercase,   last_epoch=lowercase )







def    a__							(							lowercase   :	Optimizer,   lowercase   :	str,   lowercase   :	int = -1 )						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			{}
     _UpperCamelCase							       =			step_rules.split(''',''' )
     for rule_str in rule_list[:-1]:
          _UpperCamelCase       ,							_UpperCamelCase							       =			rule_str.split(''':''' )
          _UpperCamelCase							       =			int(lowercase )
          _UpperCamelCase							       =			float(lowercase )
          _UpperCamelCase							       =			value
     _UpperCamelCase							       =			float(rule_list[-1] )

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

          return rule_func

     _UpperCamelCase							       =			create_rules_function(lowercase,   lowercase )

     return LambdaLR(lowercase,   lowercase,   last_epoch=lowercase )







def    a__							(							lowercase   :	Optional[int],   lowercase   :	Union[str, Any],   lowercase   :	str,   lowercase   :	List[str]=-1 )						->						Optional[Any]:





     """simple docstring"""

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

     return LambdaLR(lowercase,   lowercase,   lowercase )







def    a__							(							lowercase   :	Optimizer,   lowercase   :	int,   lowercase   :	int,   lowercase   :	float = 0.5,   lowercase   :	int = -1 )						->						Tuple:





     """simple docstring"""

     def lr_lambda(lowercase   :	str ):
          if current_step < num_warmup_steps:
               return float(lowercase ) / float(max(1,   lowercase ) )
          _UpperCamelCase							       =			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(lowercase ) * 2.0 * progress )) )

     return LambdaLR(lowercase,   lowercase,   lowercase )







def    a__							(							lowercase   :	Optimizer,   lowercase   :	int,   lowercase   :	int,   lowercase   :	int = 1,   lowercase   :	int = -1 )						->						Optional[int]:





     """simple docstring"""

     def lr_lambda(lowercase   :	List[str] ):
          if current_step < num_warmup_steps:
               return float(lowercase ) / float(max(1,   lowercase ) )
          _UpperCamelCase							       =			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(lowercase ) * progress) % 1.0) )) )

     return LambdaLR(lowercase,   lowercase,   lowercase )







def    a__							(							lowercase   :	int,   lowercase   :	List[Any],   lowercase   :	Any,   lowercase   :	List[str]=1e-7,   lowercase   :	List[Any]=1.0,   lowercase   :	Any=-1 )						->						List[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			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(lowercase   :	int ):
          if current_step < num_warmup_steps:
               return float(lowercase ) / float(max(1,   lowercase ) )
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               _UpperCamelCase							       =			lr_init - lr_end
               _UpperCamelCase							       =			num_training_steps - num_warmup_steps
               _UpperCamelCase							       =			1 - (current_step - num_warmup_steps) / decay_steps
               _UpperCamelCase							       =			lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(lowercase,   lowercase,   lowercase )


lowercase__		:     List[str]			=							{
    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    a__							(							lowercase   :	Union[str, SchedulerType],   lowercase   :	Optimizer,   lowercase   :	Optional[str] = None,   lowercase   :	Optional[int] = None,   lowercase   :	Optional[int] = None,   lowercase   :	int = 1,   lowercase   :	float = 1.0,   lowercase   :	int = -1,   )						->						Any:





     """simple docstring"""

     _UpperCamelCase							       =			SchedulerType(lowercase )
     _UpperCamelCase							       =			TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(lowercase,   last_epoch=lowercase )

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

     # 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(lowercase,   num_warmup_steps=lowercase,   last_epoch=lowercase )

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

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

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










"""simple docstring"""





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 ):
				"""simple docstring"""




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Union[str, Any]       =inspect.getfile(accelerate.test_utils      )
											__lowerCamelCase   :   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

											__lowerCamelCase   :   List[str]       =test_metrics




				@require_cpu
				def        __lowercase	(  self	:Optional[Any]      ):
											debug_launcher(self.test_metrics.main    ,     num_processes=1      )




				@require_cpu
				def        __lowercase	(  self	:int      ):
											debug_launcher(self.test_metrics.main      )




				@require_single_gpu
				def        __lowercase	(  self	:int      ):
											self.test_metrics.main()




				@require_multi_gpu
				def        __lowercase	(  self	:Dict      ):
											print(f'Found {torch.cuda.device_count()} devices.'      )
											__lowerCamelCase   :   Union[str, Any]       =['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
											with patch_environment(omp_num_threads=1      ):
																		execute_subprocess_async(__lowercase    ,     env=os.environ.copy()      )




