"""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   _A						(    tf.keras.optimizers.schedules.LearningRateSchedule       ):




    """simple docstring"""




    def __init__(		self				:  Any						,   A_				:  Any						,   A_				:  Optional[Any]						,   A_				:  Dict						,   A_				:  str = 1.0						,   A_				:  Tuple = None						,   )     ->							Tuple:
         super().__init__()
         __snake_case				=				initial_learning_rate
         __snake_case				=				warmup_steps
         __snake_case				=				power
         __snake_case				=				decay_schedule_fn
         __snake_case				=				name
    def __call__(		self				:  Optional[Any]						,   A_				:  List[Any]     )     ->							Union[str, 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`.
              __snake_case				=				tf.cast(__SCREAMING_SNAKE_CASE						,   tf.floataa     )
              __snake_case				=				tf.cast(self.warmup_steps						,   tf.floataa     )
              __snake_case				=				global_step_float / warmup_steps_float
              __snake_case				=				self.initial_learning_rate * tf.math.pow(__SCREAMING_SNAKE_CASE						,   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=__SCREAMING_SNAKE_CASE						,   )







    def 			lowercase       (		self				:  List[str]     )     ->							Optional[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							(  snake_case,   snake_case,   snake_case,   snake_case = 0.0,   snake_case = 0.9,   snake_case = 0.999,   snake_case = 1E-8,   snake_case = None,   snake_case = None,   snake_case = 0.0,   snake_case = 1.0,   snake_case = None,   ):
     __snake_case				=				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:
          __snake_case				=				WarmUp(
              initial_learning_rate=UpperCamelCase__,   decay_schedule_fn=UpperCamelCase__,   warmup_steps=UpperCamelCase__,   )
     if weight_decay_rate > 0.0:
          __snake_case				=				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:
          __snake_case				=				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						(    UpperCamelCase_       ):




    """simple docstring"""




    def __init__(		self				:  Union[str, Any]						,   A_				:  Any = 0.0_01						,   A_				:  Tuple = 0.9						,   A_				:  int = 0.9_99						,   A_				:  str = 1E-7						,   A_				:  Optional[Any] = False						,   A_				:  Any = 0.0						,   A_				:  int = None						,   A_				:  Dict = None						,   A_				:  Optional[Any] = "AdamWeightDecay"						,   **A_				:  int						,   )     ->							Tuple:
         super().__init__(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE     )
         __snake_case				=				weight_decay_rate
         __snake_case				=				include_in_weight_decay
         __snake_case				=				exclude_from_weight_decay
    @classmethod
    def 			lowercase       (		cls				:  Union[str, Any]						,   A_				:  Union[str, Any]     )     ->							Dict:
         __snake_case				=				{'''WarmUp''': WarmUp}
         return super(__SCREAMING_SNAKE_CASE						,   cls     ).from_config(__SCREAMING_SNAKE_CASE						,   custom_objects=__SCREAMING_SNAKE_CASE     )
    def 			lowercase       (		self				:  Any						,   A_				:  Dict						,   A_				:  Optional[int]						,   A_				:  Dict     )     ->							Dict:
         super(__SCREAMING_SNAKE_CASE						,   self     )._prepare_local(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
         __snake_case				=				tf.constant(
             self.weight_decay_rate						,   name='''adam_weight_decay_rate'''     )
    def 			lowercase       (		self				:  Any						,   A_				:  Dict						,   A_				:  Tuple						,   A_				:  Any     )     ->							List[str]:
         __snake_case				=				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 			lowercase       (		self				:  Dict						,   A_				:  List[str]						,   A_				:  List[Any]=None						,   **A_				:  Dict     )     ->							Optional[Any]:
         __snake_case				=				list(zip(*__SCREAMING_SNAKE_CASE     )     )
         return super(__SCREAMING_SNAKE_CASE						,   self     ).apply_gradients(zip(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )						,   name=__SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE     )
    def 			lowercase       (		self				:  Dict						,   A_				:  List[Any]						,   A_				:  Any						,   A_				:  Tuple     )     ->							Tuple:







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

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

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







    def 			lowercase       (		self				:  List[str]						,   A_				:  Optional[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(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     ) is not None:
                        return True

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






class   _A						(    UpperCamelCase_       ):




    """simple docstring"""




    def __init__(		self				:  Optional[Any]     )     ->							List[Any]:
         __snake_case				=				[]
         __snake_case				=				None
    @property
    def 			lowercase       (		self				:  Any     )     ->							List[Any]:







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

         return self._accum_steps.value()
    @property
    def 			lowercase       (		self				:  List[str]     )     ->							List[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				:  Tuple						,   A_				:  List[Any]     )     ->							List[str]:







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

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

         self._accum_steps.assign_add(1     )







    def 			lowercase       (		self				:  List[Any]     )     ->							Tuple:







         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(__SCREAMING_SNAKE_CASE     )     )

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase  ={
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase  =[
        "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LukeForEntityClassification",
        "LukeForEntityPairClassification",
        "LukeForEntitySpanClassification",
        "LukeForMultipleChoice",
        "LukeForQuestionAnswering",
        "LukeForSequenceClassification",
        "LukeForTokenClassification",
        "LukeForMaskedLM",
        "LukeModel",
        "LukePreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    lowerCamelCase  =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


