


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

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A						,				__A = 1.0						,				__A = None						,				):






     """simple docstring"""





     super().__init__()
     lowerCamelCase :					Optional[Any]    =				initial_learning_rate
     lowerCamelCase :					List[Any]    =				warmup_steps
     lowerCamelCase :					Union[str, Any]    =				power
     lowerCamelCase :					Optional[int]    =				decay_schedule_fn
     lowerCamelCase :					Optional[Any]    =				name






   def __call__(		self						,				__A ):






     """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 :					str    =				tf.cast(lowercase_						,				tf.floataa )
       lowerCamelCase :					List[str]    =				tf.cast(self.warmup_steps						,				tf.floataa )
       lowerCamelCase :					Any    =				global_step_float / warmup_steps_float
       lowerCamelCase :					Optional[int]    =				self.initial_learning_rate * tf.math.pow(lowercase_						,				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=lowercase_						,				)






   def _snake_case    (		self ):






     """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_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = 0.0      ,						SCREAMING_SNAKE_CASE_ = 0.9      ,						SCREAMING_SNAKE_CASE_ = 0.999      ,						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      ,						):


  '''simple docstring'''

  lowerCamelCase :					Union[str, Any]    =				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:
    lowerCamelCase :					Union[str, Any]    =				WarmUp(
        initial_learning_rate=__UpperCAmelCase      ,						decay_schedule_fn=__UpperCAmelCase      ,						warmup_steps=__UpperCAmelCase      ,						)
  if weight_decay_rate > 0.0:
    lowerCamelCase :					Optional[int]    =				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:
    lowerCamelCase :					str    =				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 	UpperCAmelCase_					(					_lowerCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A = 0.001						,				__A = 0.9						,				__A = 0.999						,				__A = 1e-7						,				__A = False						,				__A = 0.0						,				__A = None						,				__A = None						,				__A = "AdamWeightDecay"						,				**__A						,				):






     """simple docstring"""





     super().__init__(lowercase_						,				lowercase_						,				lowercase_						,				lowercase_						,				lowercase_						,				lowercase_						,				**lowercase_ )
     lowerCamelCase :					Any    =				weight_decay_rate
     lowerCamelCase :					Optional[int]    =				include_in_weight_decay
     lowerCamelCase :					Union[str, Any]    =				exclude_from_weight_decay






   @classmethod
   def _snake_case    (		cls						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				{"WarmUp": WarmUp}
     return super(lowercase_						,				cls ).from_config(lowercase_						,				custom_objects=lowercase_ )






   def _snake_case    (		self						,				__A						,				__A						,				__A ):






     """simple docstring"""





     super(lowercase_						,				self )._prepare_local(lowercase_						,				lowercase_						,				lowercase_ )
     lowerCamelCase :					List[Any]    =				tf.constant(
         self.weight_decay_rate						,				name="adam_weight_decay_rate" )






   def _snake_case    (		self						,				__A						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				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 _snake_case    (		self						,				__A						,				__A=None						,				**__A ):






     """simple docstring"""





     lowerCamelCase    ,	lowerCamelCase :					int    =				list(zip(*lowercase_ ) )
     return super(lowercase_						,				self ).apply_gradients(zip(lowercase_						,				lowercase_ )						,				name=lowercase_						,				**lowercase_ )






   def _snake_case    (		self						,				__A						,				__A						,				__A ):






     """simple docstring"""





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

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

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






   def _snake_case    (		self						,				__A						,				__A						,				__A=None ):






     """simple docstring"""





     lowerCamelCase    ,	lowerCamelCase :					Optional[Any]    =				self._get_lr(var.device						,				var.dtype.base_dtype						,				lowercase_ )
     lowerCamelCase :					str    =				self._decay_weights_op(lowercase_						,				lowercase_						,				lowercase_ )
     with tf.control_dependencies([decay] ):
       return super(lowercase_						,				self )._resource_apply_dense(lowercase_						,				lowercase_						,				**lowercase_ )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A=None ):






     """simple docstring"""





     lowerCamelCase    ,	lowerCamelCase :					Any    =				self._get_lr(var.device						,				var.dtype.base_dtype						,				lowercase_ )
     lowerCamelCase :					Optional[Any]    =				self._decay_weights_op(lowercase_						,				lowercase_						,				lowercase_ )
     with tf.control_dependencies([decay] ):
       return super(lowercase_						,				self )._resource_apply_sparse(lowercase_						,				lowercase_						,				lowercase_						,				**lowercase_ )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				super().get_config()
     config.update({"weight_decay_rate": self.weight_decay_rate} )
     return config






   def _snake_case    (		self						,				__A ):






     """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(lowercase_						,				lowercase_ ) is not None:
           return True

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



class 	UpperCAmelCase_					(					_lowerCamelCase    ):

   '''simple docstring'''




   def __init__(		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				[]
     lowerCamelCase :					int    =				None






   @property
   def _snake_case    (		self ):






     """simple docstring"""





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

     return self._accum_steps.value()






   @property
   def _snake_case    (		self ):






     """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						,				__A ):






     """simple docstring"""





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

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

     self._accum_steps.assign_add(1 )






   def _snake_case    (		self ):






     """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(lowercase_ ) )









'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a  :				str									=  logging.get_logger(__name__)

a  :				str									=  {
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"big_bird"


				def __init__(		self    :					Union[str, Any]       ,				lowercase_    :					List[Any]=5_0358       ,				lowercase_    :					Tuple=768       ,				lowercase_    :					Dict=12       ,				lowercase_    :					str=12       ,				lowercase_    :					Tuple=3072       ,				lowercase_    :					Any="gelu_new"       ,				lowercase_    :					Optional[Any]=0.1       ,				lowercase_    :					List[Any]=0.1       ,				lowercase_    :					List[Any]=4096       ,				lowercase_    :					List[Any]=2       ,				lowercase_    :					List[str]=0.02       ,				lowercase_    :					Optional[int]=1e-12       ,				lowercase_    :					Tuple=True       ,				lowercase_    :					Tuple=0       ,				lowercase_    :					str=1       ,				lowercase_    :					Union[str, Any]=2       ,				lowercase_    :					Optional[Any]=66       ,				lowercase_    :					Optional[int]="block_sparse"       ,				lowercase_    :					Any=True       ,				lowercase_    :					List[str]=False       ,				lowercase_    :					Any=64       ,				lowercase_    :					Tuple=3       ,				lowercase_    :					Tuple=None       ,				**lowercase_    :					Tuple       ,				):
									super().__init__(
									    pad_token_id=lowercase_       ,				bos_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				sep_token_id=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_size
									snake_case_									=				max_position_embeddings
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				initializer_range
									snake_case_									=				type_vocab_size
									snake_case_									=				layer_norm_eps
									snake_case_									=				use_cache

									snake_case_									=				rescale_embeddings
									snake_case_									=				attention_type
									snake_case_									=				use_bias
									snake_case_									=				block_size
									snake_case_									=				num_random_blocks
									snake_case_									=				classifier_dropout







class 						a				(   _lowerCamelCase     ):


				@property
				def   A_ (		self    :					str					):
									if self.task == "multiple-choice":
														snake_case_									=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
														snake_case_									=				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ]					)

