




import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length







def 		lowercase__   (		__A:	Any				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							int(__A				)
					__magic_name__							,    __magic_name__							,    __magic_name__							:	int    							=							t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
					return F'''{h}:{m:02d}:{s:02d}''' if h != 0 else F'''{m:02d}:{s:02d}'''







def 		lowercase__   (		__A:	str							,__A:	List[Any]							,__A:	Optional[int]							,__A:	str							,__A:	Dict=3_0_0				):





					'''simple docstring'''






					return F'''
    <div>
      {prefix}
      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
      {label}
    </div>
    '''







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							'''<table border="1" class="dataframe">\n'''
					html_code += """  <thead>\n <tr style="text-align: left;">\n"""
					for i in items[0]:
										html_code += F'''      <th>{i}</th>\n'''
					html_code += "    </tr>\n  </thead>\n  <tbody>\n"
					for line in items[1:]:
										html_code += "    <tr>\n"
										for elt in line:
															__magic_name__							:	List[str]    							=							F'''{elt:.6f}''' if isinstance(__A							,__A				) else str(__A				)
															html_code += F'''      <td>{elt}</td>\n'''
										html_code += "    </tr>\n"
					html_code += "  </tbody>\n</table><p>"
					return html_code


class    lowerCamelCase    :






			'''simple docstring'''





			UpperCamelCase__								=5
			UpperCamelCase__								=0.2

			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Optional[str] = None       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Optional["NotebookTrainingTracker"] = None       ,						lowerCamelCase_			:   int = 300       ,						)						->   Optional[Any]:
								__magic_name__							:	Any    							=							total
								__magic_name__							:	int    							=							'''''' if prefix is None else prefix
								__magic_name__							:	Any    							=							leave
								__magic_name__							:	str    							=							parent
								__magic_name__							:	Optional[int]    							=							width
								__magic_name__							:	Union[str, Any]    							=							None
								__magic_name__							:	Any    							=							None
								__magic_name__							:	int    							=							None

			def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   bool = False       ,						lowerCamelCase_			:   str = None  )						->   Optional[Any]:
								__magic_name__							:	Tuple    							=							value
								if comment is not None:
													__magic_name__							:	Tuple    							=							comment
								if self.last_value is None:
													__magic_name__							:	Dict    							=							time.time()
													__magic_name__							:	List[Any]    							=							value
													__magic_name__							:	Tuple    							=							None
													__magic_name__							:	List[str]    							=							self.warmup
													__magic_name__							:	List[Any]    							=							1
													self.update_bar(lowerCamelCase_  )
								elif value <= self.last_value and not force_update:
													return
								elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for       ,						self.total  ):
													if self.first_calls > 0:
																		self.first_calls -= 1
													__magic_name__							:	List[Any]    							=							time.time()
													__magic_name__							:	Optional[Any]    							=							current_time - self.start_time
													# We could have value = self.start_value if the update is called twixe with the same start value.
													if value > self.start_value:
																		__magic_name__							:	List[str]    							=							self.elapsed_time / (value - self.start_value)
													else:
																		__magic_name__							:	Optional[Any]    							=							None
													if value >= self.total:
																		__magic_name__							:	Optional[int]    							=							self.total
																		__magic_name__							:	Optional[Any]    							=							None
																		if not self.leave:
																							self.close()
													elif self.average_time_per_item is not None:
																		__magic_name__							:	str    							=							self.average_time_per_item * (self.total - value)
													self.update_bar(lowerCamelCase_  )
													__magic_name__							:	Optional[int]    							=							value
													__magic_name__							:	List[str]    							=							current_time
													if self.average_time_per_item is None:
																		__magic_name__							:	Tuple    							=							1
													else:
																		__magic_name__							:	str    							=							max(int(self.update_every / self.average_time_per_item  )       ,						1  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   List[Any]=None  )						->   str:
								__magic_name__							:	str    							=							''' ''' * (len(str(self.total  )  ) - len(str(lowerCamelCase_  )  )) + str(lowerCamelCase_  )
								if self.elapsed_time is None:
													__magic_name__							:	Optional[int]    							=							F'''[{spaced_value}/{self.total} : < :'''
								elif self.predicted_remaining is None:
													__magic_name__							:	Optional[Any]    							=							F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time  )}'''
								else:
													__magic_name__							:	Dict    							=							(
													    F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time  )} <'''
													    F''' {format_time(self.predicted_remaining  )}'''
													)
													self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
								self.label += "]" if self.comment is None or len(self.comment  ) == 0 else F''', {self.comment}]'''
								self.display()

			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   str:
								__magic_name__							:	List[Any]    							=							html_progress_bar(self.value       ,						self.total       ,						self.prefix       ,						self.label       ,						self.width  )
								if self.parent is not None:
													# If this is a child bar, the parent will take care of the display.
													self.parent.display()
													return
								if self.output is None:
													__magic_name__							:	List[Any]    							=							disp.display(disp.HTML(self.html_code  )       ,						display_id=lowerCamelCase_  )
								else:
													self.output.update(disp.HTML(self.html_code  )  )


			def 							UpperCAmelCase__       ( self			:   int  )						->   Union[str, Any]:
								if self.parent is None and self.output is not None:
													self.output.update(disp.HTML(''''''  )  )






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   List[str]=None  )						->   str:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	int    							=							None if column_names is None else [column_names]
								__magic_name__							:	str    							=							None

			def 							UpperCAmelCase__       ( self			:   int  )						->   Any:
								__magic_name__							:	Any    							=							html_progress_bar(self.value       ,						self.total       ,						self.prefix       ,						self.label       ,						self.width  )
								if self.inner_table is not None:
													self.html_code += text_to_html_table(self.inner_table  )
								if self.child_bar is not None:
													self.html_code += self.child_bar.html_code
								if self.output is None:
													__magic_name__							:	List[Any]    							=							disp.display(disp.HTML(self.html_code  )       ,						display_id=lowerCamelCase_  )
								else:
													self.output.update(disp.HTML(self.html_code  )  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Dict  )						->   Optional[int]:
								if self.inner_table is None:
													__magic_name__							:	int    							=							[list(values.keys()  ), list(values.values()  )]
								else:
													__magic_name__							:	Any    							=							self.inner_table[0]
													if len(self.inner_table  ) == 1:
																		# We give a chance to update the column names at the first iteration
																		for key in values.keys():
																							if key not in columns:
																												columns.append(lowerCamelCase_  )
																		__magic_name__							:	Union[str, Any]    							=							columns
													self.inner_table.append([values[c] for c in columns]  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   Any=300  )						->   Any:
								__magic_name__							:	Union[str, Any]    							=							NotebookProgressBar(lowerCamelCase_       ,						prefix=lowerCamelCase_       ,						parent=self       ,						width=lowerCamelCase_  )
								return self.child_bar


			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Union[str, Any]:
								__magic_name__							:	str    							=							None
								self.display()






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Tuple  )						->   Union[str, Any]:
								__magic_name__							:	int    							=							None
								__magic_name__							:	Dict    							=							None
								__magic_name__							:	List[str]    							=							False

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   List[Any]       ,						**lowerCamelCase_			:   Dict  )						->   int:
								__magic_name__							:	Any    							=							'''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
								__magic_name__							:	Tuple    							=							0
								__magic_name__							:	Dict    							=							0
								__magic_name__							:	List[Any]    							=							[self.first_column] + ['''Training Loss''']
								if args.evaluation_strategy != IntervalStrategy.NO:
													column_names.append('''Validation Loss'''  )
								__magic_name__							:	Union[str, Any]    							=							NotebookTrainingTracker(state.max_steps       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Any       ,						**lowerCamelCase_			:   List[Any]  )						->   Any:
								__magic_name__							:	str    							=							int(state.epoch  ) if int(state.epoch  ) == state.epoch else F'''{state.epoch:.2f}'''
								self.training_tracker.update(
								    state.global_step + 1       ,						comment=F'''Epoch {epoch}/{state.num_train_epochs}'''       ,						force_update=self._force_next_update       ,						)
								__magic_name__							:	int    							=							False

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Tuple=None       ,						**lowerCamelCase_			:   Dict  )						->   Any:
								if not has_length(lowerCamelCase_  ):
													return
								if self.prediction_bar is None:
													if self.training_tracker is not None:
																		__magic_name__							:	Tuple    							=							self.training_tracker.add_child(len(lowerCamelCase_  )  )
													else:
																		__magic_name__							:	Tuple    							=							NotebookProgressBar(len(lowerCamelCase_  )  )
													self.prediction_bar.update(1  )
								else:
													self.prediction_bar.update(self.prediction_bar.value + 1  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   str       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   List[str]:
								if self.prediction_bar is not None:
													self.prediction_bar.close()
								__magic_name__							:	Any    							=							None

			def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Tuple       ,						lowerCamelCase_			:   Optional[Any]=None       ,						**lowerCamelCase_			:   List[str]  )						->   str:
								# Only for when there is no evaluation
								if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
													__magic_name__							:	Union[str, Any]    							=							{'''Training Loss''': logs['''loss''']}
													# First column is necessarily Step sine we're not in epoch eval strategy
													__magic_name__							:	str    							=							state.global_step
													self.training_tracker.write_line(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   Optional[int]=None       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   Dict:
								if self.training_tracker is not None:
													__magic_name__							:	List[str]    							=							{'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
													for log in reversed(state.log_history  ):
																		if "loss" in log:
																							__magic_name__							:	str    							=							log['''loss''']
																							break

													if self.first_column == "Epoch":
																		__magic_name__							:	Optional[int]    							=							int(state.epoch  )
													else:
																		__magic_name__							:	Optional[Any]    							=							state.global_step
													__magic_name__							:	int    							=							'''eval'''
													for k in metrics:
																		if k.endswith('''_loss'''  ):
																							__magic_name__							:	Tuple    							=							re.sub(R'''\_loss$'''       ,						''''''       ,						lowerCamelCase_  )
													__magic_name__							:	int    							=							metrics.pop('''total_flos'''       ,						lowerCamelCase_  )
													__magic_name__							:	Tuple    							=							metrics.pop('''epoch'''       ,						lowerCamelCase_  )
													__magic_name__							:	Optional[Any]    							=							metrics.pop(F'''{metric_key_prefix}_runtime'''       ,						lowerCamelCase_  )
													__magic_name__							:	List[Any]    							=							metrics.pop(F'''{metric_key_prefix}_samples_per_second'''       ,						lowerCamelCase_  )
													__magic_name__							:	Optional[int]    							=							metrics.pop(F'''{metric_key_prefix}_steps_per_second'''       ,						lowerCamelCase_  )
													__magic_name__							:	str    							=							metrics.pop(F'''{metric_key_prefix}_jit_compilation_time'''       ,						lowerCamelCase_  )
													for k, v in metrics.items():
																		if k == F'''{metric_key_prefix}_loss''':
																							__magic_name__							:	List[Any]    							=							v
																		else:
																							__magic_name__							:	Optional[Any]    							=							k.split('''_'''  )
																							__magic_name__							:	List[str]    							=							''' '''.join([part.capitalize() for part in splits[1:]]  )
																							__magic_name__							:	Optional[int]    							=							v
													self.training_tracker.write_line(lowerCamelCase_  )
													self.training_tracker.remove_child()
													__magic_name__							:	List[str]    							=							None
													# Evaluation takes a long time so we should force the next update.
													__magic_name__							:	str    							=							True


			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								self.training_tracker.update(
								    state.global_step       ,						comment=F'''Epoch {int(state.epoch  )}/{state.num_train_epochs}'''       ,						force_update=lowerCamelCase_  )
								__magic_name__							:	List[Any]    							=							None









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    lowerCamelCase    (		tf.keras.optimizers.schedules.LearningRateSchedule						):






			'''simple docstring'''





			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   float       ,						lowerCamelCase_			:   Callable       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   float = 1.0       ,						lowerCamelCase_			:   str = None       ,						)						->   Optional[int]:
								super().__init__()
								__magic_name__							:	Union[str, Any]    							=							initial_learning_rate
								__magic_name__							:	Optional[int]    							=							warmup_steps
								__magic_name__							:	Union[str, Any]    							=							power
								__magic_name__							:	List[Any]    							=							decay_schedule_fn
								__magic_name__							:	Dict    							=							name

			def __call__( self			:   Any       ,						lowerCamelCase_			:   Dict  )						->   Optional[Any]:
								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`.
													__magic_name__							:	Union[str, Any]    							=							tf.cast(lowerCamelCase_       ,						tf.floataa  )
													__magic_name__							:	Dict    							=							tf.cast(self.warmup_steps       ,						tf.floataa  )
													__magic_name__							:	Dict    							=							global_step_float / warmup_steps_float
													__magic_name__							:	List[str]    							=							self.initial_learning_rate * tf.math.pow(lowerCamelCase_       ,						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=lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   str:
								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 		lowercase__   (		__A:	float							,__A:	int							,__A:	int							,__A:	float = 0.0							,__A:	float = 0.9							,__A:	float = 0.999							,__A:	float = 1e-8							,__A:	Optional[float] = None							,__A:	Optional[float] = None							,__A:	float = 0.0							,__A:	float = 1.0							,__A:	Optional[List[str]] = None							,):





					'''simple docstring'''






					__magic_name__							:	Any    							=							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:
										__magic_name__							:	Tuple    							=							WarmUp(
										    initial_learning_rate=__A							,decay_schedule_fn=__A							,warmup_steps=__A							,)
					if weight_decay_rate > 0.0:
										__magic_name__							:	Union[str, Any]    							=							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:
										__magic_name__							:	str    							=							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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Optional[int]       ,						lowerCamelCase_			:   Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1       ,						lowerCamelCase_			:   float = 0.9       ,						lowerCamelCase_			:   float = 0.9_9_9       ,						lowerCamelCase_			:   float = 1E-7       ,						lowerCamelCase_			:   bool = False       ,						lowerCamelCase_			:   float = 0.0       ,						lowerCamelCase_			:   Optional[List[str]] = None       ,						lowerCamelCase_			:   Optional[List[str]] = None       ,						lowerCamelCase_			:   str = "AdamWeightDecay"       ,						**lowerCamelCase_			:   int       ,						)						->   List[str]:
								super().__init__(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	int    							=							weight_decay_rate
								__magic_name__							:	Tuple    							=							include_in_weight_decay
								__magic_name__							:	Union[str, Any]    							=							exclude_from_weight_decay

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   int       ,						lowerCamelCase_			:   int  )						->   Optional[Any]:
								__magic_name__							:	Tuple    							=							{'''WarmUp''': WarmUp}
								return super(lowerCamelCase_       ,						cls  ).from_config(lowerCamelCase_       ,						custom_objects=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   int:
								super(lowerCamelCase_       ,						self  )._prepare_local(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							:	Any    							=							tf.constant(
								    self.weight_decay_rate       ,						name='''adam_weight_decay_rate'''  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Any  )						->   Optional[Any]:
								__magic_name__							:	Optional[int]    							=							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 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int=None       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							,    __magic_name__							:	Dict    							=							list(zip(*lowerCamelCase_  )  )
								return super(lowerCamelCase_       ,						self  ).apply_gradients(zip(lowerCamelCase_       ,						lowerCamelCase_  )       ,						name=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
								if apply_state is None:
													return self._decayed_lr_t[var_dtype], {}

								__magic_name__							:	str    							=							apply_state or {}
								__magic_name__							:	Optional[Any]    							=							apply_state.get((var_device, var_dtype)  )
								if coefficients is None:
													__magic_name__							:	List[str]    							=							self._fallback_apply_state(lowerCamelCase_       ,						lowerCamelCase_  )
													__magic_name__							:	List[Any]    							=							coefficients

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

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Dict=None  )						->   Optional[Any]:
								__magic_name__							,    __magic_name__							:	Union[str, Any]    							=							self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							self._decay_weights_op(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								with tf.control_dependencies([decay]  ):
													return super(lowerCamelCase_       ,						self  )._resource_apply_dense(lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Union[str, Any]=None  )						->   Any:
								__magic_name__							,    __magic_name__							:	Tuple    							=							self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCamelCase_  )
								__magic_name__							:	Optional[int]    							=							self._decay_weights_op(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								with tf.control_dependencies([decay]  ):
													return super(lowerCamelCase_       ,						self  )._resource_apply_sparse(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   Dict:
								__magic_name__							:	List[Any]    							=							super().get_config()
								config.update({'''weight_decay_rate''': self.weight_decay_rate}  )
								return config


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
								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(lowerCamelCase_       ,						lowerCamelCase_  ) is not None:
																							return True

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






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Optional[Any]  )						->   Any:
								__magic_name__							:	Optional[int]    							=							[]
								__magic_name__							:	Dict    							=							None

			@property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
								if self._accum_steps is None:
													__magic_name__							:	Optional[Any]    							=							tf.Variable(
													    tf.constant(0       ,						dtype=tf.intaa  )       ,						trainable=lowerCamelCase_       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)

								return self._accum_steps.value()

			@property
			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[Any]:
								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			:   Optional[int]       ,						lowerCamelCase_			:   Any  )						->   List[str]:
								if not self._gradients:
													__magic_name__							:	int    							=							self.step  # Create the step variable.
													self._gradients.extend(
													    [
													        tf.Variable(
													            tf.zeros_like(lowerCamelCase_  )       ,						trainable=lowerCamelCase_       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)
													        if gradient is not None
													        else gradient
													        for gradient in gradients
													    ]  )
								if len(lowerCamelCase_  ) != len(self._gradients  ):
													raise ValueError(F'''Expected {len(self._gradients  )} gradients, but got {len(lowerCamelCase_  )}'''  )

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

								self._accum_steps.assign_add(1  )


			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[int]:
								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(lowerCamelCase_  )  )



