


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					,      ):


             '''simple docstring'''



             super().__init__()
             lowerCamelCase__		:				Optional[Any]    =  initial_learning_rate
             lowerCamelCase__		:				List[str]    =  warmup_steps
             lowerCamelCase__		:				Optional[Any]    =  power
             lowerCamelCase__		:				str    =  decay_schedule_fn
             lowerCamelCase__		:				str    =  name





       def __call__(   self							:			int					,      __lowerCamelCase							:			int       ):


             '''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__		:				Any    =  tf.cast(__lowerCamelCase					,      tf.floataa       )
                   lowerCamelCase__		:				Union[str, Any]    =  tf.cast(self.warmup_steps					,      tf.floataa       )
                   lowerCamelCase__		:				Optional[Any]    =  global_step_float / warmup_steps_float
                   lowerCamelCase__		:				Optional[int]    =  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 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''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       lowercase_		(		_A			:    float    ,					_A			:    int    ,					_A			:    int    ,					_A			:    float = 0.0    ,					_A			:    float = 0.9    ,					_A			:    float = 0.999    ,					_A			:    float = 1E-8    ,					_A			:    Optional[float] = None    ,					_A			:    Optional[float] = None    ,					_A			:    float = 0.0    ,					_A			:    float = 1.0    ,					_A			:    Optional[List[str]] = None    ,					):

      """simple docstring"""




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


       """simple docstring"""




       def __init__(   self							:			Optional[Any]					,      __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							:			Union[str, Any]					,      ):


             '''simple docstring'''



             super().__init__(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      **__lowerCamelCase       )
             lowerCamelCase__		:				Any    =  weight_decay_rate
             lowerCamelCase__		:				Dict    =  include_in_weight_decay
             lowerCamelCase__		:				List[str]    =  exclude_from_weight_decay





       @classmethod
       def 						lowerCAmelCase     (   cls							:			str					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  {"WarmUp": WarmUp}
             return super(__lowerCamelCase					,      cls       ).from_config(__lowerCamelCase					,      custom_objects=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Tuple       ):


             '''simple docstring'''



             super(__lowerCamelCase					,      self       )._prepare_local(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[str]    =  tf.constant(
                 self.weight_decay_rate					,      name="adam_weight_decay_rate"       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  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							:			int					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Optional[Any]=None					,      **__lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  list(zip(*__lowerCamelCase       )       )
             return super(__lowerCamelCase					,      self       ).apply_gradients(zip(__lowerCamelCase					,      __lowerCamelCase       )					,      name=__lowerCamelCase					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



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

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

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





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Dict=None       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Any=None       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			List[Any]       ):


             '''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 (							lowercase__):


       """simple docstring"""




       def __init__(   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  []
             lowerCamelCase__		:				Any    =  None





       @property
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             if self._accum_steps is None:
                   lowerCamelCase__		:				int    =  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 						lowerCAmelCase     (   self							:			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							:			Any					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             if not self._gradients:
                   lowerCamelCase__		:				int    =  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 						lowerCAmelCase     (   self							:			str       ):


             '''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       )       )






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


A      :    str		=					logging.get_logger(__name__)

A      :    int		=					{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"mgp-str"





       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			List[Any]=[32, 128]					,      __lowerCamelCase							:			Optional[int]=4					,      __lowerCamelCase							:			str=3					,      __lowerCamelCase							:			Optional[Any]=27					,      __lowerCamelCase							:			List[str]=38					,      __lowerCamelCase							:			Dict=50257					,      __lowerCamelCase							:			List[Any]=30522					,      __lowerCamelCase							:			Optional[Any]=768					,      __lowerCamelCase							:			str=12					,      __lowerCamelCase							:			Optional[int]=12					,      __lowerCamelCase							:			List[str]=4.0					,      __lowerCamelCase							:			Union[str, Any]=True					,      __lowerCamelCase							:			Any=False					,      __lowerCamelCase							:			Dict=1E-5					,      __lowerCamelCase							:			int=0.0					,      __lowerCamelCase							:			Optional[int]=0.0					,      __lowerCamelCase							:			List[Any]=0.0					,      __lowerCamelCase							:			Union[str, Any]=False					,      __lowerCamelCase							:			str=0.0_2					,      **__lowerCamelCase							:			Dict					,      ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase       )

             lowerCamelCase__		:				int    =  image_size
             lowerCamelCase__		:				Union[str, Any]    =  patch_size
             lowerCamelCase__		:				Dict    =  num_channels
             lowerCamelCase__		:				Union[str, Any]    =  max_token_length
             lowerCamelCase__		:				Optional[int]    =  num_character_labels
             lowerCamelCase__		:				Union[str, Any]    =  num_bpe_labels
             lowerCamelCase__		:				Optional[int]    =  num_wordpiece_labels
             lowerCamelCase__		:				List[str]    =  hidden_size
             lowerCamelCase__		:				Dict    =  num_hidden_layers
             lowerCamelCase__		:				Any    =  num_attention_heads
             lowerCamelCase__		:				Any    =  mlp_ratio
             lowerCamelCase__		:				List[Any]    =  distilled
             lowerCamelCase__		:				Optional[Any]    =  layer_norm_eps
             lowerCamelCase__		:				Union[str, Any]    =  drop_rate
             lowerCamelCase__		:				List[Any]    =  qkv_bias
             lowerCamelCase__		:				int    =  attn_drop_rate
             lowerCamelCase__		:				List[Any]    =  drop_path_rate
             lowerCamelCase__		:				List[str]    =  output_aa_attentions
             lowerCamelCase__		:				Dict    =  initializer_range


