




"""simple docstring"""




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    UpperCAmelCase_  (       tf.keras.optimizers.schedules.LearningRateSchedule						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_ = None							,		)		->    Optional[int]:
					super().__init__()
					__lowercase       :   int        =    initial_learning_rate
					__lowercase       :   Any        =    warmup_steps
					__lowercase       :   Union[str, Any]        =    power
					__lowercase       :   str        =    decay_schedule_fn
					__lowercase       :   List[str]        =    name
		def __call__(		self							,		UpperCamelCase_    )		->    str:




					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       :   Any        =    tf.cast(lowercase_							,		tf.floataa    )
								__lowercase       :   Dict        =    tf.cast(self.warmup_steps							,		tf.floataa    )
								__lowercase       :   List[str]        =    global_step_float / warmup_steps_float
								__lowercase       :   int        =    self.initial_learning_rate * tf.math.pow(lowercase_							,		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=lowercase_							,		)
		def      _lowerCamelCase				(		self    )		->    Any:
					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 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = 0.0     ,							__UpperCamelCase = 0.9     ,							__UpperCamelCase = 0.999     ,							__UpperCamelCase = 1e-8     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 0.0     ,							__UpperCamelCase = 1.0     ,							__UpperCamelCase = None     ,							):
			__lowercase       :   Optional[int]        =    tf.keras.optimizers.schedules.PolynomialDecay(
			    initial_learning_rate=SCREAMING_SNAKE_CASE__     ,							decay_steps=num_train_steps - num_warmup_steps     ,							end_learning_rate=init_lr * min_lr_ratio     ,							power=SCREAMING_SNAKE_CASE__     ,							)
			if num_warmup_steps:
						__lowercase       :   Tuple        =    WarmUp(
						    initial_learning_rate=SCREAMING_SNAKE_CASE__     ,							decay_schedule_fn=SCREAMING_SNAKE_CASE__     ,							warmup_steps=SCREAMING_SNAKE_CASE__     ,							)
			if weight_decay_rate > 0.0:
						__lowercase       :   Optional[int]        =    AdamWeightDecay(
						    learning_rate=SCREAMING_SNAKE_CASE__     ,							weight_decay_rate=SCREAMING_SNAKE_CASE__     ,							beta_a=SCREAMING_SNAKE_CASE__     ,							beta_a=SCREAMING_SNAKE_CASE__     ,							epsilon=SCREAMING_SNAKE_CASE__     ,							clipnorm=SCREAMING_SNAKE_CASE__     ,							global_clipnorm=SCREAMING_SNAKE_CASE__     ,							exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']     ,							include_in_weight_decay=SCREAMING_SNAKE_CASE__     ,							)
			else:
						__lowercase       :   int        =    tf.keras.optimizers.Adam(
						    learning_rate=SCREAMING_SNAKE_CASE__     ,							beta_a=SCREAMING_SNAKE_CASE__     ,							beta_a=SCREAMING_SNAKE_CASE__     ,							epsilon=SCREAMING_SNAKE_CASE__     ,							clipnorm=SCREAMING_SNAKE_CASE__     ,							global_clipnorm=SCREAMING_SNAKE_CASE__     ,							)
			# 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    UpperCAmelCase_  (       UpperCamelCase_						):
		def __init__(		self							,		UpperCamelCase_ = 0.0_0_1							,		UpperCamelCase_ = 0.9							,		UpperCamelCase_ = 0.9_9_9							,		UpperCamelCase_ = 1E-7							,		UpperCamelCase_ = False							,		UpperCamelCase_ = 0.0							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		UpperCamelCase_ = "AdamWeightDecay"							,		**UpperCamelCase_							,		)		->    List[str]:
					super().__init__(lowercase_							,		lowercase_							,		lowercase_							,		lowercase_							,		lowercase_							,		lowercase_							,		**lowercase_    )
					__lowercase       :   Tuple        =    weight_decay_rate
					__lowercase       :   List[Any]        =    include_in_weight_decay
					__lowercase       :   str        =    exclude_from_weight_decay
		@classmethod
		def      _lowerCamelCase				(		cls							,		UpperCamelCase_    )		->    Dict:
					__lowercase       :   int        =    {'WarmUp': WarmUp}
					return super(lowercase_							,		cls    ).from_config(lowercase_							,		custom_objects=lowercase_    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_    )		->    Optional[int]:
					super(lowercase_							,		self    )._prepare_local(lowercase_							,		lowercase_							,		lowercase_    )
					__lowercase       :   Union[str, Any]        =    tf.constant(
					    self.weight_decay_rate							,		name='''adam_weight_decay_rate'''    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_    )		->    Optional[int]:
					__lowercase       :   List[str]        =    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      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_=None							,		**UpperCamelCase_    )		->    Any:
					__lowercase       :   List[Any]        =    list(zip(*lowercase_    )    )
					return super(lowercase_							,		self    ).apply_gradients(zip(lowercase_							,		lowercase_    )							,		name=lowercase_							,		**lowercase_    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_    )		->    List[Any]:




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

					__lowercase       :   int        =    apply_state or {}
					__lowercase       :   List[str]        =    apply_state.get((var_device, var_dtype)    )
					if coefficients is None:
								__lowercase       :   List[str]        =    self._fallback_apply_state(lowercase_							,		lowercase_    )
								__lowercase       :   Union[str, Any]        =    coefficients

					return coefficients["lr_t"], {"apply_state": apply_state}
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_=None    )		->    Union[str, Any]:
					__lowercase       :   Optional[int]        =    self._get_lr(var.device							,		var.dtype.base_dtype							,		lowercase_    )
					__lowercase       :   Tuple        =    self._decay_weights_op(lowercase_							,		lowercase_							,		lowercase_    )
					with tf.control_dependencies([decay]    ):
								return super(lowercase_							,		self    )._resource_apply_dense(lowercase_							,		lowercase_							,		**lowercase_    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_=None    )		->    Tuple:
					__lowercase       :   int        =    self._get_lr(var.device							,		var.dtype.base_dtype							,		lowercase_    )
					__lowercase       :   Dict        =    self._decay_weights_op(lowercase_							,		lowercase_							,		lowercase_    )
					with tf.control_dependencies([decay]    ):
								return super(lowercase_							,		self    )._resource_apply_sparse(lowercase_							,		lowercase_							,		lowercase_							,		**lowercase_    )
		def      _lowerCamelCase				(		self    )		->    Optional[int]:
					__lowercase       :   Tuple        =    super().get_config()
					config.update({'''weight_decay_rate''': self.weight_decay_rate}    )
					return config
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    List[str]:




					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(lowercase_							,		lowercase_    ) is not None:
														return True

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


class    UpperCAmelCase_  (       UpperCamelCase_						):
		def __init__(		self    )		->    List[str]:
					__lowercase       :   List[str]        =    []
					__lowercase       :   Tuple        =    None
		@property
		def      _lowerCamelCase				(		self    )		->    str:




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

					return self._accum_steps.value()
		@property
		def      _lowerCamelCase				(		self    )		->    Dict:




					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							,		UpperCamelCase_    )		->    Tuple:




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

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

					self._accum_steps.assign_add(1    )
		def      _lowerCamelCase				(		self    )		->    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(lowercase_    )    )







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

lowerCamelCase											=       'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append('importlib_metadata')

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      str=None     ):
       '''simple docstring'''





       require_version(deps[pkg]		,			SCREAMING_SNAKE_CASE__     )


