



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



  def __init__( self  :    Any      ,							snake_case  :    float      ,							snake_case  :    Callable      ,							snake_case  :    int      ,							snake_case  :    float = 1.0      ,							snake_case  :    str = None      ,							)			->						int:






         '''simple docstring'''

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



  def __call__( self  :    Optional[Any]      ,							snake_case  :    List[str]				)			->						str:






         '''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`.
                A         =					tf.cast(snake_case      ,							tf.floataa				)
                A         =					tf.cast(self.warmup_steps      ,							tf.floataa				)
                A         =					global_step_float / warmup_steps_float
                A         =					self.initial_learning_rate * tf.math.pow(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=snake_case      ,							)



  def 					A_		( self  :    int				)			->						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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = 0.0				,      lowerCamelCase__ = 0.9				,      lowerCamelCase__ = 0.9_99				,      lowerCamelCase__ = 1E-8				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = 0.0				,      lowerCamelCase__ = 1.0				,      lowerCamelCase__ = None				,      ) ->							List[str]:
       A         =					tf.keras.optimizers.schedules.PolynomialDecay(
           initial_learning_rate=lowerCamelCase__				,      decay_steps=num_train_steps - num_warmup_steps				,      end_learning_rate=init_lr * min_lr_ratio				,      power=lowerCamelCase__				,      )
       if num_warmup_steps:
              A         =					WarmUp(
                  initial_learning_rate=lowerCamelCase__				,      decay_schedule_fn=lowerCamelCase__				,      warmup_steps=lowerCamelCase__				,      )
       if weight_decay_rate > 0.0:
              A         =					AdamWeightDecay(
                  learning_rate=lowerCamelCase__				,      weight_decay_rate=lowerCamelCase__				,      beta_a=lowerCamelCase__				,      beta_a=lowerCamelCase__				,      epsilon=lowerCamelCase__				,      clipnorm=lowerCamelCase__				,      global_clipnorm=lowerCamelCase__				,      exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']				,      include_in_weight_decay=lowerCamelCase__				,      )
       else:
              A         =					tf.keras.optimizers.Adam(
                  learning_rate=lowerCamelCase__				,      beta_a=lowerCamelCase__				,      beta_a=lowerCamelCase__				,      epsilon=lowerCamelCase__				,      clipnorm=lowerCamelCase__				,      global_clipnorm=lowerCamelCase__				,      )
       # 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__		(      UpperCamelCase     ):



  def __init__( self  :    Optional[int]      ,							snake_case  :    Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001      ,							snake_case  :    float = 0.9      ,							snake_case  :    float = 0.999      ,							snake_case  :    float = 1E-7      ,							snake_case  :    bool = False      ,							snake_case  :    float = 0.0      ,							snake_case  :    Optional[List[str]] = None      ,							snake_case  :    Optional[List[str]] = None      ,							snake_case  :    str = "AdamWeightDecay"      ,							**snake_case  :    Union[str, Any]      ,							)			->						Union[str, Any]:






         '''simple docstring'''

         super().__init__(snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							**snake_case				)
         A         =					weight_decay_rate
         A         =					include_in_weight_decay
         A         =					exclude_from_weight_decay



  @classmethod
  def 					A_		( cls  :    Any      ,							snake_case  :    str				)			->						Optional[int]:






         '''simple docstring'''

         A         =					{'WarmUp': WarmUp}
         return super(snake_case      ,							cls				).from_config(snake_case      ,							custom_objects=snake_case				)



  def 					A_		( self  :    Dict      ,							snake_case  :    Optional[int]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Union[str, Any]				)			->						Tuple:






         '''simple docstring'''

         super(snake_case      ,							self				)._prepare_local(snake_case      ,							snake_case      ,							snake_case				)
         A         =					tf.constant(
             self.weight_decay_rate      ,							name='adam_weight_decay_rate'				)



  def 					A_		( self  :    Dict      ,							snake_case  :    Optional[int]      ,							snake_case  :    int      ,							snake_case  :    Dict				)			->						Any:






         '''simple docstring'''

         A         =					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]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Dict=None      ,							**snake_case  :    str				)			->						Tuple:






         '''simple docstring'''

         A      ,  A         =					list(zip(*snake_case				)				)
         return super(snake_case      ,							self				).apply_gradients(zip(snake_case      ,							snake_case				)      ,							name=snake_case      ,							**snake_case				)



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






         '''simple docstring'''

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

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

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



  def 					A_		( self  :    Dict      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    str      ,							snake_case  :    str=None				)			->						int:






         '''simple docstring'''

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



  def 					A_		( self  :    Optional[int]      ,							snake_case  :    str      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Optional[int]      ,							snake_case  :    Any=None				)			->						str:






         '''simple docstring'''

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



  def 					A_		( self  :    Optional[Any]				)			->						str:






         '''simple docstring'''

         A         =					super().get_config()
         config.update({'weight_decay_rate': self.weight_decay_rate}				)
         return config



  def 					A_		( self  :    Dict      ,							snake_case  :    Union[str, Any]				)			->						Tuple:






         '''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(snake_case      ,							snake_case				) is not None:
                              return True

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






class      UpperCAmelCase__		(      UpperCamelCase     ):



  def __init__( self  :    Any				)			->						int:






         '''simple docstring'''

         A         =					[]
         A         =					None



  @property
  def 					A_		( self  :    List[Any]				)			->						Optional[int]:






         '''simple docstring'''

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

         return self._accum_steps.value()



  @property
  def 					A_		( self  :    Any				)			->						Optional[int]:






         '''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  :    Tuple      ,							snake_case  :    List[str]				)			->						Dict:






         '''simple docstring'''

         if not self._gradients:
                A         =					self.step  # Create the step variable.
                self._gradients.extend(
                    [
                        tf.Variable(
                            tf.zeros_like(snake_case				)      ,							trainable=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(snake_case				) != len(self._gradients				):
                raise ValueError(f"""Expected {len(self._gradients				)} gradients, but got {len(snake_case				)}"""				)

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

         self._accum_steps.assign_add(1				)



  def 					A_		( self  :    Dict				)			->						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(snake_case				)				)








"""simple docstring"""







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


A    =			logging.get_logger(__name__)

A    =			{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     """decision_transformer"""
		lowerCAmelCase_       :	Tuple 							=     ["""past_key_values"""]
		lowerCAmelCase_       :	Dict 							=     {
		    """max_position_embeddings""": """n_positions""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}



		def __init__( self  :    Optional[Any]      ,							snake_case  :    int=17      ,							snake_case  :    int=4      ,							snake_case  :    Union[str, Any]=128      ,							snake_case  :    Optional[Any]=4_096      ,							snake_case  :    List[Any]=True      ,							snake_case  :    str=1      ,							snake_case  :    str=1_024      ,							snake_case  :    List[str]=3      ,							snake_case  :    str=1      ,							snake_case  :    Any=None      ,							snake_case  :    Optional[int]="relu"      ,							snake_case  :    List[Any]=0.1      ,							snake_case  :    str=0.1      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    Union[str, Any]=1E-5      ,							snake_case  :    Optional[int]=0.02      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Tuple=True      ,							snake_case  :    Optional[Any]=50_256      ,							snake_case  :    List[Any]=50_256      ,							snake_case  :    List[str]=False      ,							snake_case  :    Any=False      ,							**snake_case  :    List[str]      ,							)			->						List[Any]:






									'''simple docstring'''

									A         =					state_dim
									A         =					act_dim
									A         =					hidden_size
									A         =					max_ep_len
									A         =					action_tanh
									A         =					vocab_size
									A         =					n_positions
									A         =					n_layer
									A         =					n_head
									A         =					n_inner
									A         =					activation_function
									A         =					resid_pdrop
									A         =					embd_pdrop
									A         =					attn_pdrop
									A         =					layer_norm_epsilon
									A         =					initializer_range
									A         =					scale_attn_weights
									A         =					use_cache
									A         =					scale_attn_by_inverse_layer_idx
									A         =					reorder_and_upcast_attn

									A         =					bos_token_id
									A         =					eos_token_id

									super().__init__(bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)



