



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      ):

     '''simple docstring'''





     def __init__(     self       :      List[str] ,   __snake_case       :      str ,   __snake_case       :      Optional[Any] ,   __snake_case       :      List[Any] ,   __snake_case       :      List[str] = 1.0 ,   __snake_case       :      Any = None ,   ):
         super().__init__()
         UpperCAmelCase_										=					initial_learning_rate
         UpperCAmelCase_										=					warmup_steps
         UpperCAmelCase_										=					power
         UpperCAmelCase_										=					decay_schedule_fn
         UpperCAmelCase_										=					name
     def __call__(     self       :      List[str] ,   __snake_case       :      List[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`.
             UpperCAmelCase_										=					tf.cast(UpperCamelCase__ ,   tf.floataa    )
             UpperCAmelCase_										=					tf.cast(self.warmup_steps ,   tf.floataa    )
             UpperCAmelCase_										=					global_step_float / warmup_steps_float
             UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Union[str, 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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						List[Any] = 0.0	,					__UpperCamelCase      :						str = 0.9	,					__UpperCamelCase      :						Optional[Any] = 0.999	,					__UpperCamelCase      :						Any = 1e-8	,					__UpperCamelCase      :						Tuple = None	,					__UpperCamelCase      :						str = None	,					__UpperCamelCase      :						Any = 0.0	,					__UpperCamelCase      :						List[Any] = 1.0	,					__UpperCamelCase      :						Union[str, Any] = None	,					)  ->							Any:
    UpperCAmelCase_										=					tf.keras.optimizers.schedules.PolynomialDecay(
        initial_learning_rate=UpperCAmelCase__	,					decay_steps=num_train_steps - num_warmup_steps	,					end_learning_rate=init_lr * min_lr_ratio	,					power=UpperCAmelCase__	,					)
    if num_warmup_steps:
        UpperCAmelCase_										=					WarmUp(
            initial_learning_rate=UpperCAmelCase__	,					decay_schedule_fn=UpperCAmelCase__	,					warmup_steps=UpperCAmelCase__	,					)
    if weight_decay_rate > 0.0:
        UpperCAmelCase_										=					AdamWeightDecay(
            learning_rate=UpperCAmelCase__	,					weight_decay_rate=UpperCAmelCase__	,					beta_a=UpperCAmelCase__	,					beta_a=UpperCAmelCase__	,					epsilon=UpperCAmelCase__	,					clipnorm=UpperCAmelCase__	,					global_clipnorm=UpperCAmelCase__	,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']	,					include_in_weight_decay=UpperCAmelCase__	,					)
    else:
        UpperCAmelCase_										=					tf.keras.optimizers.Adam(
            learning_rate=UpperCAmelCase__	,					beta_a=UpperCAmelCase__	,					beta_a=UpperCAmelCase__	,					epsilon=UpperCAmelCase__	,					clipnorm=UpperCAmelCase__	,					global_clipnorm=UpperCAmelCase__	,					)
    # 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					(       _snake_case      ):

     '''simple docstring'''





     def __init__(     self       :      List[str] ,   __snake_case       :      List[Any] = 0.001 ,   __snake_case       :      int = 0.9 ,   __snake_case       :      Any = 0.999 ,   __snake_case       :      Any = 1E-7 ,   __snake_case       :      Any = False ,   __snake_case       :      str = 0.0 ,   __snake_case       :      Optional[Any] = None ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      List[str] = "AdamWeightDecay" ,   **__snake_case       :      Optional[Any] ,   ):
         super().__init__(UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   **UpperCamelCase__    )
         UpperCAmelCase_										=					weight_decay_rate
         UpperCAmelCase_										=					include_in_weight_decay
         UpperCAmelCase_										=					exclude_from_weight_decay
     @classmethod
     def   lowerCamelCase_       (     cls       :      List[str] ,   __snake_case       :      Optional[Any]    ):
         UpperCAmelCase_										=					{'''WarmUp''': WarmUp}
         return super(UpperCamelCase__ ,   cls    ).from_config(UpperCamelCase__ ,   custom_objects=UpperCamelCase__    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      List[Any] ,   __snake_case       :      Any ,   __snake_case       :      Any    ):
         super(UpperCamelCase__ ,   self    )._prepare_local(UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__    )
         UpperCAmelCase_										=					tf.constant(
             self.weight_decay_rate ,   name='''adam_weight_decay_rate'''    )
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      List[str] ,   __snake_case       :      List[Any] ,   __snake_case       :      str    ):
         UpperCAmelCase_										=					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       :      Any ,   __snake_case       :      List[str] ,   __snake_case       :      Optional[Any]=None ,   **__snake_case       :      Tuple    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					list(zip(*UpperCamelCase__    )    )
         return super(UpperCamelCase__ ,   self    ).apply_gradients(zip(UpperCamelCase__ ,   UpperCamelCase__    ) ,   name=UpperCamelCase__ ,   **UpperCamelCase__    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Dict    ):
         if apply_state is None:
             return self._decayed_lr_t[var_dtype], {}

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

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




     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      List[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(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					(       _snake_case      ):

     '''simple docstring'''





     def __init__(     self       :      Dict    ):
         UpperCAmelCase_										=					[]
         UpperCAmelCase_										=					None
     @property
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         if self._accum_steps is None:
             UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      str    ):
         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       :      Union[str, Any] ,   __snake_case       :      Union[str, Any]    ):
         if not self._gradients:
             UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         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__    )    )











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase      			=						{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['RobertaTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RobertaForCausalLM',
     'RobertaForMaskedLM',
     'RobertaForMultipleChoice',
     'RobertaForQuestionAnswering',
     'RobertaForSequenceClassification',
     'RobertaForTokenClassification',
     'RobertaModel',
     'RobertaPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFRobertaForCausalLM',
     'TFRobertaForMaskedLM',
     'TFRobertaForMultipleChoice',
     'TFRobertaForQuestionAnswering',
     'TFRobertaForSequenceClassification',
     'TFRobertaForTokenClassification',
     'TFRobertaMainLayer',
     'TFRobertaModel',
     'TFRobertaPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'FlaxRobertaForCausalLM',
     'FlaxRobertaForMaskedLM',
     'FlaxRobertaForMultipleChoice',
     'FlaxRobertaForQuestionAnswering',
     'FlaxRobertaForSequenceClassification',
     'FlaxRobertaForTokenClassification',
     'FlaxRobertaModel',
     'FlaxRobertaPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
 from .tokenization_roberta import RobertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roberta_fast import RobertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta import (
      ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaForCausalLM,
      RobertaForMaskedLM,
      RobertaForMultipleChoice,
      RobertaForQuestionAnswering,
      RobertaForSequenceClassification,
      RobertaForTokenClassification,
      RobertaModel,
      RobertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta import (
      TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaForCausalLM,
      TFRobertaForMaskedLM,
      TFRobertaForMultipleChoice,
      TFRobertaForQuestionAnswering,
      TFRobertaForSequenceClassification,
      TFRobertaForTokenClassification,
      TFRobertaMainLayer,
      TFRobertaModel,
      TFRobertaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
      FlaxRobertaPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase      			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





