


import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
	from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
	from tensorflow.keras.optimizers import Adam





class       A    (   tf.keras.optimizers.schedules.LearningRateSchedule ):


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase = 1.0      ,				lowerCAmelCase = None      ,				):
									super().__init__()
									__lowercase=			initial_learning_rate
									__lowercase=			warmup_steps
									__lowercase=			power
									__lowercase=			decay_schedule_fn
									__lowercase=			name


							def __call__(self      ,				lowerCAmelCase					):







									with tf.name_scope(self.name or 'WarmUp'					) as name:
											# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
											# learning rate will be `global_step/num_warmup_steps * init_lr`.
											__lowercase=			tf.cast(UpperCamelCase__      ,				tf.floataa					)
											__lowercase=			tf.cast(self.warmup_steps      ,				tf.floataa					)
											__lowercase=			global_step_float / warmup_steps_float
											__lowercase=			self.initial_learning_rate * tf.math.pow(UpperCamelCase__      ,				self.power					)
											return tf.cond(
											    global_step_float < warmup_steps_float      ,				lambda: warmup_learning_rate      ,				lambda: self.decay_schedule_fn(step - self.warmup_steps					)      ,				name=UpperCamelCase__      ,				)







							def 			_A       (self					):
									return {
									    "initial_learning_rate": self.initial_learning_rate,
									    "decay_schedule_fn": self.decay_schedule_fn,
									    "warmup_steps": self.warmup_steps,
									    "power": self.power,
									    "name": self.name,
									}







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__ = 0.0   , lowercase__ = 0.9   , lowercase__ = 0.999   , lowercase__ = 1E-8   , lowercase__ = None   , lowercase__ = None   , lowercase__ = 0.0   , lowercase__ = 1.0   , lowercase__ = None   , )						->	str:

		'''simple docstring'''







		__lowercase=			tf.keras.optimizers.schedules.PolynomialDecay(
		    initial_learning_rate=A__   , decay_steps=num_train_steps - num_warmup_steps   , end_learning_rate=init_lr * min_lr_ratio   , power=A__   , )
		if num_warmup_steps:
				__lowercase=			WarmUp(
				    initial_learning_rate=A__   , decay_schedule_fn=A__   , warmup_steps=A__   , )
		if weight_decay_rate > 0.0:
				__lowercase=			AdamWeightDecay(
				    learning_rate=A__   , weight_decay_rate=A__   , beta_a=A__   , beta_a=A__   , epsilon=A__   , clipnorm=A__   , global_clipnorm=A__   , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']   , include_in_weight_decay=A__   , )
		else:
				__lowercase=			tf.keras.optimizers.Adam(
				    learning_rate=A__   , beta_a=A__   , beta_a=A__   , epsilon=A__   , clipnorm=A__   , global_clipnorm=A__   , )
		# We return the optimizer and the LR scheduler in order to better track the
		# evolution of the LR independently of the optimizer.
		return optimizer, lr_schedule





class       A    (   _a ):


							def __init__(self      ,				lowerCAmelCase = 0.0_01      ,				lowerCAmelCase = 0.9      ,				lowerCAmelCase = 0.9_99      ,				lowerCAmelCase = 1E-7      ,				lowerCAmelCase = False      ,				lowerCAmelCase = 0.0      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = "AdamWeightDecay"      ,				**lowerCAmelCase      ,				):
									super().__init__(UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				**UpperCamelCase__					)
									__lowercase=			weight_decay_rate
									__lowercase=			include_in_weight_decay
									__lowercase=			exclude_from_weight_decay


							@classmethod
							def 			_A       (cls      ,				lowerCAmelCase					):
									__lowercase=			{'WarmUp': WarmUp}
									return super(UpperCamelCase__      ,				cls					).from_config(UpperCamelCase__      ,				custom_objects=UpperCamelCase__					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									super(UpperCamelCase__      ,				self					)._prepare_local(UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__					)
									__lowercase=			tf.constant(
									    self.weight_decay_rate      ,				name='adam_weight_decay_rate'					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			self._do_use_weight_decay(var.name					)
									if do_decay:
											return var.assign_sub(
											    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate']      ,				use_locking=self._use_locking      ,				)
									return tf.no_op()


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=None      ,				**lowerCAmelCase					):
									__lowercase,							__lowercase=			list(zip(*UpperCamelCase__					)					)
									return super(UpperCamelCase__      ,				self					).apply_gradients(zip(UpperCamelCase__      ,				UpperCamelCase__					)      ,				name=UpperCamelCase__      ,				**UpperCamelCase__					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):







									if apply_state is None:
											return self._decayed_lr_t[var_dtype], {}

									__lowercase=			apply_state or {}
									__lowercase=			apply_state.get((var_device, var_dtype)					)
									if coefficients is None:
											__lowercase=			self._fallback_apply_state(UpperCamelCase__      ,				UpperCamelCase__					)
											__lowercase=			coefficients

									return coefficients["lr_t"], {"apply_state": apply_state}


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase=None					):
									__lowercase,							__lowercase=			self._get_lr(var.device      ,				var.dtype.base_dtype      ,				UpperCamelCase__					)
									__lowercase=			self._decay_weights_op(UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__					)
									with tf.control_dependencies([decay]					):
											return super(UpperCamelCase__      ,				self					)._resource_apply_dense(UpperCamelCase__      ,				UpperCamelCase__      ,				**UpperCamelCase__					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase=None					):
									__lowercase,							__lowercase=			self._get_lr(var.device      ,				var.dtype.base_dtype      ,				UpperCamelCase__					)
									__lowercase=			self._decay_weights_op(UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__					)
									with tf.control_dependencies([decay]					):
											return super(UpperCamelCase__      ,				self					)._resource_apply_sparse(UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				**UpperCamelCase__					)


							def 			_A       (self					):
									__lowercase=			super().get_config()
									config.update({'weight_decay_rate': self.weight_decay_rate}					)
									return config







							def 			_A       (self      ,				lowerCAmelCase					):







									if self.weight_decay_rate == 0:
											return False

									if self._include_in_weight_decay:
											for r in self._include_in_weight_decay:
													if re.search(UpperCamelCase__      ,				UpperCamelCase__					) is not None:
															return True

									if self._exclude_from_weight_decay:
											for r in self._exclude_from_weight_decay:
													if re.search(UpperCamelCase__      ,				UpperCamelCase__					) is not None:
															return False
									return True





class       A    (   _a ):


							def __init__(self					):
									__lowercase=			[]
									__lowercase=			None


							@property
							def 			_A       (self					):







									if self._accum_steps is None:
											__lowercase=			tf.Variable(
											    tf.constant(0      ,				dtype=tf.intaa					)      ,				trainable=UpperCamelCase__      ,				synchronization=tf.VariableSynchronization.ON_READ      ,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA      ,				)

									return self._accum_steps.value()


							@property
							def 			_A       (self					):







									if not self._gradients:
											raise ValueError('The accumulator should be called first to initialize the gradients'					)
									return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]


							def __call__(self      ,				lowerCAmelCase					):







									if not self._gradients:
											__lowercase=			self.step  # Create the step variable.
											self._gradients.extend(
											    [
											        tf.Variable(
											            tf.zeros_like(UpperCamelCase__					)      ,				trainable=UpperCamelCase__      ,				synchronization=tf.VariableSynchronization.ON_READ      ,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA      ,				)
											        if gradient is not None
											        else gradient
											        for gradient in gradients
											    ]					)
									if len(UpperCamelCase__					) != len(self._gradients					):
											raise ValueError(f'Expected {len(self._gradients					)} gradients, but got {len(UpperCamelCase__					)}'					)

									for accum_gradient, gradient in zip(self._gradients      ,				UpperCamelCase__					):
											if accum_gradient is not None and gradient is not None:
													accum_gradient.assign_add(UpperCamelCase__					)

									self._accum_steps.assign_add(1					)







							def 			_A       (self					):







									if not self._gradients:
											return
									self._accum_steps.assign(0					)
									for gradient in self._gradients:
											if gradient is not None:
													gradient.assign(tf.zeros_like(UpperCamelCase__					)					)











'''simple docstring'''


_lowerCamelCase	:       int      	=	"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich

