




# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")




class 		__A   :

						def __init__(		self :Any     ,			__snake_case :Optional[Any]     ,			__snake_case :Optional[int]     ,			__snake_case :bool = True     ,			__snake_case :bool = False			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=scheduler
										__magic_name__						:						Optional[Any]										=optimizers if isinstance(__snake_case     ,			(list, tuple)			) else [optimizers]
										__magic_name__						:						Any										=split_batches
										__magic_name__						:						List[str]										=step_with_optimizer
										__magic_name__						:						Dict										=GradientState()

						def      A__    (		self :List[str]     ,			*__snake_case :Optional[Any]     ,			**__snake_case :Any			):
										'''simple docstring'''

										if not self.step_with_optimizer:
														# No link between scheduler and optimizer -> just step
														self.scheduler.step(*__snake_case     ,			**__snake_case			)
														return

										# Otherwise, first make sure the optimizer was stepped.
										if not self.gradient_state.sync_gradients:
														if self.gradient_state.adjust_scheduler:
																		self.scheduler._step_count += 1
														return

										for opt in self.optimizers:
														if opt.step_was_skipped:
																		return
										if self.split_batches:
														# Split batches -> the training dataloader batch size is not changed so one step per training step
														self.scheduler.step(*__snake_case     ,			**__snake_case			)
										else:
														# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
														# num_processes steps per training step
														__magic_name__						:						str										=AcceleratorState().num_processes
														for _ in range(__snake_case			):
																		# Special case when using OneCycle and `drop_last` was not used
																		if hasattr(self.scheduler     ,			"""total_steps"""			):
																						if self.scheduler._step_count <= self.scheduler.total_steps:
																										self.scheduler.step(*__snake_case     ,			**__snake_case			)
																		else:
																						self.scheduler.step(*__snake_case     ,			**__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return self.scheduler.get_last_lr()

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										return self.scheduler.state_dict()

						def      A__    (		self :Union[str, Any]     ,			__snake_case :Dict			):
										'''simple docstring'''

										self.scheduler.load_state_dict(__snake_case			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										return self.scheduler.get_lr()


						def      A__    (		self :Any     ,			*__snake_case :int     ,			**__snake_case :Union[str, Any]			):
										'''simple docstring'''

										return self.scheduler.print_lr(*__snake_case     ,			**__snake_case			)










import math

import tensorflow as tf
from packaging import version




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						str										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						List[str]										=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 )						,  x.dtype ) ))

				return x * cdf




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						str										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						Optional[Any]										=tf.cast(math.pi						,  x.dtype )
				__magic_name__						:						int										=tf.cast(0.0_4_4_7_1_5						,  x.dtype )
				__magic_name__						:						Tuple										=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase						,  3 )) ))

				return x * cdf




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Any										=tf.convert_to_tensor(lowerCamelCase )

				return x * tf.tanh(tf.math.softplus(lowerCamelCase ) )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Optional[Any]										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						Union[str, Any]										=tf.cast(0.0_4_4_7_1_5						,  x.dtype )
				__magic_name__						:						Tuple										=tf.cast(0.7_9_7_8_8_4_5_6_0_8						,  x.dtype )

				return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						List[str]										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						Dict										=tf.cast(1.7_0_2						,  x.dtype )
				return x * tf.math.sigmoid(coeff * x )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				return tf.clip_by_value(_gelu(lowerCamelCase )						,  -10						,  10 )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=-1 ):
				__magic_name__					,			__magic_name__						:						List[Any]										=tf.split(lowerCamelCase						,  2						,  axis=lowerCamelCase )
				return a * tf.math.sigmoid(lowerCamelCase )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):




							def 		lowerCAmelCase_ (						lowerCamelCase ):
											return tf.keras.activations.gelu(lowerCamelCase						,  approximate=lowerCamelCase )

							UpperCAmelCase_		:					List[str]          =  tf.keras.activations.gelu



							UpperCAmelCase_		:					Dict          =  approximate_gelu_wrap
else:
							UpperCAmelCase_		:					Dict          =  _gelu
							UpperCAmelCase_		:					str          =  _gelu_new


UpperCAmelCase_		:					Any          =  {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if activation_string in ACTaFN:
								return ACTaFN[activation_string]
				else:
								raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )




