





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







  def __init__(		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase = 1.0			,     __lowercase = None			,     )				->   Optional[Any]:
    super().__init__()
    lowerCamelCase   : int								=initial_learning_rate
    lowerCamelCase   : Dict								=warmup_steps
    lowerCamelCase   : Optional[int]								=power
    lowerCamelCase   : List[str]								=decay_schedule_fn
    lowerCamelCase   : List[Any]								=name







  def __call__(		self			,     __lowercase   )				->   Optional[int]:
    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   : Optional[int]								=tf.cast(_A			,     tf.floataa   )
      lowerCamelCase   : Tuple								=tf.cast(self.warmup_steps			,     tf.floataa   )
      lowerCamelCase   : Any								=global_step_float / warmup_steps_float
      lowerCamelCase   : Union[str, Any]								=self.initial_learning_rate * tf.math.pow(_A			,     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=_A			,     )







  def 			__lowercase   (		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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = 0.0							,       SCREAMING_SNAKE_CASE_ = 0.9							,       SCREAMING_SNAKE_CASE_ = 0.9_9_9							,       SCREAMING_SNAKE_CASE_ = 1E-8							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 0.0							,       SCREAMING_SNAKE_CASE_ = 1.0							,       SCREAMING_SNAKE_CASE_ = None							,       )   ->     Optional[int]:
  lowerCamelCase   : Dict								=tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=__snake_case							,       decay_steps=num_train_steps - num_warmup_steps							,       end_learning_rate=init_lr * min_lr_ratio							,       power=__snake_case							,       )
  if num_warmup_steps:
    lowerCamelCase   : List[Any]								=WarmUp(
        initial_learning_rate=__snake_case							,       decay_schedule_fn=__snake_case							,       warmup_steps=__snake_case							,       )
  if weight_decay_rate > 0.0:
    lowerCamelCase   : List[Any]								=AdamWeightDecay(
        learning_rate=__snake_case							,       weight_decay_rate=__snake_case							,       beta_a=__snake_case							,       beta_a=__snake_case							,       epsilon=__snake_case							,       clipnorm=__snake_case							,       global_clipnorm=__snake_case							,       exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']							,       include_in_weight_decay=__snake_case							,       )
  else:
    lowerCamelCase   : Tuple								=tf.keras.optimizers.Adam(
        learning_rate=__snake_case							,       beta_a=__snake_case							,       beta_a=__snake_case							,       epsilon=__snake_case							,       clipnorm=__snake_case							,       global_clipnorm=__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        snake_case_		(			__lowercase):







  def __init__(		self			,     __lowercase = 0.0_0_1			,     __lowercase = 0.9			,     __lowercase = 0.9_9_9			,     __lowercase = 1e-7			,     __lowercase = False			,     __lowercase = 0.0			,     __lowercase = None			,     __lowercase = None			,     __lowercase = "AdamWeightDecay"			,     **__lowercase			,     )				->   Optional[Any]:
    super().__init__(_A			,     _A			,     _A			,     _A			,     _A			,     _A			,     **_A   )
    lowerCamelCase   : Tuple								=weight_decay_rate
    lowerCamelCase   : Union[str, Any]								=include_in_weight_decay
    lowerCamelCase   : List[Any]								=exclude_from_weight_decay







  @classmethod
  def 			__lowercase   (		cls			,     __lowercase   )				->   int:
    lowerCamelCase   : Optional[Any]								={'''WarmUp''': WarmUp}
    return super(_A			,     cls   ).from_config(_A			,     custom_objects=_A   )







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Union[str, Any]:
    super(_A			,     self   )._prepare_local(_A			,     _A			,     _A   )
    lowerCamelCase   : Union[str, Any]								=tf.constant(
        self.weight_decay_rate			,     name='''adam_weight_decay_rate'''   )







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Tuple:
    lowerCamelCase   : Tuple								=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			,     __lowercase			,     __lowercase=None			,     **__lowercase   )				->   str:
    lowerCamelCase		,   lowerCamelCase   : Any								=list(zip(*_A   )   )
    return super(_A			,     self   ).apply_gradients(zip(_A			,     _A   )			,     name=_A			,     **_A   )







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Any:
    if apply_state is None:
      return self._decayed_lr_t[var_dtype], {}

    lowerCamelCase   : Any								=apply_state or {}
    lowerCamelCase   : Dict								=apply_state.get((var_device, var_dtype)   )
    if coefficients is None:
      lowerCamelCase   : str								=self._fallback_apply_state(_A			,     _A   )
      lowerCamelCase   : Union[str, Any]								=coefficients

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







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase=None   )				->   List[Any]:
    lowerCamelCase		,   lowerCamelCase   : Union[str, Any]								=self._get_lr(var.device			,     var.dtype.base_dtype			,     _A   )
    lowerCamelCase   : int								=self._decay_weights_op(_A			,     _A			,     _A   )
    with tf.control_dependencies([decay]   ):
      return super(_A			,     self   )._resource_apply_dense(_A			,     _A			,     **_A   )







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase=None   )				->   Optional[int]:
    lowerCamelCase		,   lowerCamelCase   : List[Any]								=self._get_lr(var.device			,     var.dtype.base_dtype			,     _A   )
    lowerCamelCase   : List[Any]								=self._decay_weights_op(_A			,     _A			,     _A   )
    with tf.control_dependencies([decay]   ):
      return super(_A			,     self   )._resource_apply_sparse(_A			,     _A			,     _A			,     **_A   )







  def 			__lowercase   (		self   )				->   List[str]:
    lowerCamelCase   : List[Any]								=super().get_config()
    config.update({'''weight_decay_rate''': self.weight_decay_rate}   )
    return config







  def 			__lowercase   (		self			,     __lowercase   )				->   int:
    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(_A			,     _A   ) is not None:
          return True

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

class        snake_case_		(			__lowercase):







  def __init__(		self   )				->   Optional[Any]:
    lowerCamelCase   : str								=[]
    lowerCamelCase   : Optional[Any]								=None







  @property
  def 			__lowercase   (		self   )				->   Any:
    if self._accum_steps is None:
      lowerCamelCase   : Dict								=tf.Variable(
          tf.constant(0			,     dtype=tf.intaa   )			,     trainable=_A			,     synchronization=tf.VariableSynchronization.ON_READ			,     aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,     )

    return self._accum_steps.value()







  @property
  def 			__lowercase   (		self   )				->   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			,     __lowercase   )				->   Any:
    if not self._gradients:
      lowerCamelCase   : Tuple								=self.step  # Create the step variable.
      self._gradients.extend(
          [
              tf.Variable(
                  tf.zeros_like(_A   )			,     trainable=_A			,     synchronization=tf.VariableSynchronization.ON_READ			,     aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,     )
              if gradient is not None
              else gradient
              for gradient in gradients
          ]   )
    if len(_A   ) != len(self._gradients   ):
      raise ValueError(F"Expected {len(self._gradients   )} gradients, but got {len(_A   )}"   )

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

    self._accum_steps.assign_add(1   )







  def 			__lowercase   (		self   )				->   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(_A   )   )








from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_							=			logging.get_logger(__name__)

snake_case_							=			{
    '''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}
class        snake_case_		(			_A):
		lowerCamelCase    :Union[str, Any]						=     "timesformer"







		def __init__(		self			,     __lowercase=2_2_4			,     __lowercase=1_6			,     __lowercase=3			,     __lowercase=8			,     __lowercase=7_6_8			,     __lowercase=1_2			,     __lowercase=1_2			,     __lowercase=3_0_7_2			,     __lowercase="gelu"			,     __lowercase=0.0			,     __lowercase=0.0			,     __lowercase=0.0_2			,     __lowercase=1e-6			,     __lowercase=True			,     __lowercase="divided_space_time"			,     __lowercase=0			,     **__lowercase			,     )				->   List[Any]:
				super().__init__(**__lowercase   )

				lowerCamelCase   : int								=image_size
				lowerCamelCase   : List[str]								=patch_size
				lowerCamelCase   : Union[str, Any]								=num_channels
				lowerCamelCase   : str								=num_frames

				lowerCamelCase   : Dict								=hidden_size
				lowerCamelCase   : int								=num_hidden_layers
				lowerCamelCase   : Dict								=num_attention_heads
				lowerCamelCase   : Dict								=intermediate_size
				lowerCamelCase   : Union[str, Any]								=hidden_act
				lowerCamelCase   : str								=hidden_dropout_prob
				lowerCamelCase   : Union[str, Any]								=attention_probs_dropout_prob
				lowerCamelCase   : List[Any]								=initializer_range
				lowerCamelCase   : List[Any]								=layer_norm_eps
				lowerCamelCase   : List[str]								=qkv_bias

				lowerCamelCase   : Tuple								=attention_type
				lowerCamelCase   : List[Any]								=drop_path_rate

