






'''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 				lowercase       (							tf.keras.optimizers.schedules.LearningRateSchedule      ):


    '''simple docstring'''

    def __init__(					self     :     Tuple				,     __lowerCamelCase     :     float				,     __lowerCamelCase     :     Callable				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     float = 1.0				,     __lowerCamelCase     :     str = None				,     )							->							List[Any]:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__  =     initial_learning_rate
     lowerCamelCase__  =     warmup_steps
     lowerCamelCase__  =     power
     lowerCamelCase__  =     decay_schedule_fn
     lowerCamelCase__  =     name

    def __call__(					self     :     Tuple				,     __lowerCamelCase     :     List[Any]     )							->							Dict:

     '''simple docstring'''




     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`.
      lowerCamelCase__  =     tf.cast(__lowerCamelCase				,     tf.floataa     )
      lowerCamelCase__  =     tf.cast(self.warmup_steps				,     tf.floataa     )
      lowerCamelCase__  =     global_step_float / warmup_steps_float
      lowerCamelCase__  =     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    a__				(					self     :     Optional[int]     )							->							List[Any]:

     '''simple docstring'''




     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				,     ):
 lowerCamelCase__  =     tf.keras.optimizers.schedules.PolynomialDecay(
     initial_learning_rate=lowercase__				,     decay_steps=num_train_steps - num_warmup_steps				,     end_learning_rate=init_lr * min_lr_ratio				,     power=lowercase__				,     )
 if num_warmup_steps:
  lowerCamelCase__  =     WarmUp(
      initial_learning_rate=lowercase__				,     decay_schedule_fn=lowercase__				,     warmup_steps=lowercase__				,     )
 if weight_decay_rate > 0.0:
  lowerCamelCase__  =     AdamWeightDecay(
      learning_rate=lowercase__				,     weight_decay_rate=lowercase__				,     beta_a=lowercase__				,     beta_a=lowercase__				,     epsilon=lowercase__				,     clipnorm=lowercase__				,     global_clipnorm=lowercase__				,     exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]				,     include_in_weight_decay=lowercase__				,     )
 else:
  lowerCamelCase__  =     tf.keras.optimizers.Adam(
      learning_rate=lowercase__				,     beta_a=lowercase__				,     beta_a=lowercase__				,     epsilon=lowercase__				,     clipnorm=lowercase__				,     global_clipnorm=lowercase__				,     )
 # 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 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def __init__(					self     :     Tuple				,     __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     :     Any				,     )							->							Union[str, Any]:

     '''simple docstring'''




     super().__init__(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     **__lowerCamelCase     )
     lowerCamelCase__  =     weight_decay_rate
     lowerCamelCase__  =     include_in_weight_decay
     lowerCamelCase__  =     exclude_from_weight_decay

    @classmethod
    def    a__				(					cls     :     Any				,     __lowerCamelCase     :     List[Any]     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     {"WarmUp": WarmUp}
     return super(__lowerCamelCase				,     cls     ).from_config(__lowerCamelCase				,     custom_objects=__lowerCamelCase     )

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     Union[str, Any]     )							->							List[Any]:

     '''simple docstring'''




     super(__lowerCamelCase				,     self     )._prepare_local(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )
     lowerCamelCase__  =     tf.constant(
         self.weight_decay_rate				,     name="adam_weight_decay_rate"     )

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     Optional[Any]     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     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     :     List[str]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Optional[int]=None				,     **__lowerCamelCase     :     int     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     list(zip(*__lowerCamelCase     )     )
     return super(__lowerCamelCase				,     self     ).apply_gradients(zip(__lowerCamelCase				,     __lowerCamelCase     )				,     name=__lowerCamelCase				,     **__lowerCamelCase     )

    def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     str     )							->							int:

     '''simple docstring'''




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

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

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

    def    a__				(					self     :     int				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     List[str]=None     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     self._get_lr(var.device				,     var.dtype.base_dtype				,     __lowerCamelCase     )
     lowerCamelCase__  =     self._decay_weights_op(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )
     with tf.control_dependencies([decay]     ):
      return super(__lowerCamelCase				,     self     )._resource_apply_dense(__lowerCamelCase				,     __lowerCamelCase				,     **__lowerCamelCase     )

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Tuple=None     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     self._get_lr(var.device				,     var.dtype.base_dtype				,     __lowerCamelCase     )
     lowerCamelCase__  =     self._decay_weights_op(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )
     with tf.control_dependencies([decay]     ):
      return super(__lowerCamelCase				,     self     )._resource_apply_sparse(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     **__lowerCamelCase     )

    def    a__				(					self     :     List[str]     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     super().get_config()
     config.update({"weight_decay_rate": self.weight_decay_rate}     )
     return config




    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     List[str]     )							->							int:

     '''simple docstring'''




     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 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def __init__(					self     :     Any     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     []
     lowerCamelCase__  =     None

    @property
    def    a__				(					self     :     Dict     )							->							Any:

     '''simple docstring'''




     if self._accum_steps is None:
      lowerCamelCase__  =     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    a__				(					self     :     List[Any]     )							->							str:

     '''simple docstring'''




     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     :     int				,     __lowerCamelCase     :     Union[str, Any]     )							->							Optional[int]:

     '''simple docstring'''




     if not self._gradients:
      lowerCamelCase__  =     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    a__				(					self     :     Union[str, Any]     )							->							int:

     '''simple docstring'''




     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     )     )









'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A							:							Union[str, Any]  							=		{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Optional[Any]  							=		["""LayoutLMv2TokenizerFast"""]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							List[Any]  							=		["""LayoutLMv2FeatureExtractor"""]
 __A							:							List[Any]  							=		["""LayoutLMv2ImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Any  							=		[
     """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """LayoutLMv2ForQuestionAnswering""",
     """LayoutLMv2ForSequenceClassification""",
     """LayoutLMv2ForTokenClassification""",
     """LayoutLMv2Layer""",
     """LayoutLMv2Model""",
     """LayoutLMv2PreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaLayer,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )
else:
 import sys

 __A							:							Tuple  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

