


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


  def __init__(			self:						List[str]							,__lowerCAmelCase:						float							,__lowerCAmelCase:						Callable							,__lowerCAmelCase:						int							,__lowerCAmelCase:						float = 1.0							,__lowerCAmelCase:						str = None							,):







     '''simple docstring'''







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


  def __call__(			self:						List[str]							,__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   :				List[Any]							=     tf.cast(__lowerCAmelCase							,tf.floataa    )
        _lowerCamelCase   :				Optional[int]							=     tf.cast(self.warmup_steps							,tf.floataa    )
        _lowerCamelCase   :				Union[str, Any]							=     global_step_float / warmup_steps_float
        _lowerCamelCase   :				Dict							=     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 							_lowercase					(			self:						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_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = 0.0			,		_lowerCamelCase = 0.9			,		_lowerCamelCase = 0.9_9_9			,		_lowerCamelCase = 1e-8			,		_lowerCamelCase = None			,		_lowerCamelCase = None			,		_lowerCamelCase = 0.0			,		_lowerCamelCase = 1.0			,		_lowerCamelCase = None			,		)    ->     Any:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     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:
      _lowerCamelCase   :				List[str]							=     WarmUp(
          initial_learning_rate=_lowerCamelCase			,		decay_schedule_fn=_lowerCamelCase			,		warmup_steps=_lowerCamelCase			,		)
   if weight_decay_rate > 0.0:
      _lowerCamelCase   :				List[str]							=     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:
      _lowerCamelCase   :				Optional[int]							=     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 							A_      (       _a ):


  def __init__(			self:						Optional[int]							,__lowerCAmelCase:						Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_01							,__lowerCAmelCase:						float = 0.9							,__lowerCAmelCase:						float = 0.9_99							,__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:						Tuple							,):







     '''simple docstring'''







     super().__init__(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,**__lowerCAmelCase    )
     _lowerCamelCase   :				Optional[Any]							=     weight_decay_rate
     _lowerCamelCase   :				Union[str, Any]							=     include_in_weight_decay
     _lowerCamelCase   :				Dict							=     exclude_from_weight_decay


  @classmethod
  def 							_lowercase					(			cls:						Union[str, Any]							,__lowerCAmelCase:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[Any]							=     {"WarmUp": WarmUp}
     return super(__lowerCAmelCase							,cls    ).from_config(__lowerCAmelCase							,custom_objects=__lowerCAmelCase    )


  def 							_lowercase					(			self:						List[str]							,__lowerCAmelCase:						Dict							,__lowerCAmelCase:						Tuple							,__lowerCAmelCase:						Tuple    ):







     '''simple docstring'''







     super(__lowerCAmelCase							,self    )._prepare_local(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase    )
     _lowerCamelCase   :				Optional[int]							=     tf.constant(
         self.weight_decay_rate							,name="adam_weight_decay_rate"    )


  def 							_lowercase					(			self:						Any							,__lowerCAmelCase:						Any							,__lowerCAmelCase:						Optional[Any]							,__lowerCAmelCase:						Optional[Any]    ):







     '''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 							_lowercase					(			self:						List[str]							,__lowerCAmelCase:						Tuple							,__lowerCAmelCase:						Union[str, Any]=None							,**__lowerCAmelCase:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase,					_lowerCamelCase   :				int							=     list(zip(*__lowerCAmelCase    )    )
     return super(__lowerCAmelCase							,self    ).apply_gradients(zip(__lowerCAmelCase							,__lowerCAmelCase    )							,name=__lowerCAmelCase							,**__lowerCAmelCase    )


  def 							_lowercase					(			self:						Union[str, Any]							,__lowerCAmelCase:						Optional[Any]							,__lowerCAmelCase:						Dict							,__lowerCAmelCase:						Optional[int]    ):







     '''simple docstring'''







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

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

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


  def 							_lowercase					(			self:						Optional[int]							,__lowerCAmelCase:						int							,__lowerCAmelCase:						List[Any]							,__lowerCAmelCase:						Union[str, Any]=None    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						List[str]							,__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						Optional[Any]							,__lowerCAmelCase:						Any							,__lowerCAmelCase:						Tuple=None    ):







     '''simple docstring'''







     _lowerCamelCase,					_lowerCamelCase   :				Tuple							=     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_sparse(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,**__lowerCAmelCase    )


  def 							_lowercase					(			self:						int    ):







     '''simple docstring'''







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






  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						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 							A_      (       _a ):


  def __init__(			self:						Tuple    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     []
     _lowerCamelCase   :				List[str]							=     None


  @property
  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     if self._accum_steps is None:
        _lowerCamelCase   :				Any							=     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 							_lowercase					(			self:						Dict    ):







     '''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:						str							,__lowerCAmelCase:						Tuple    ):







     '''simple docstring'''







     if not self._gradients:
        _lowerCamelCase   :				Dict							=     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 							_lowercase					(			self:						Optional[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    )    )






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase							:			List[str]        =							logging.get_logger(__name__)

_UpperCAmelCase							:			List[str]        =							{"""vocab_file""": """spiece.model"""}

_UpperCAmelCase							:			Tuple        =							{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

_UpperCAmelCase							:			List[str]        =							{"""bert_for_seq_generation""": 5_12}

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						VOCAB_FILES_NAMES
 UpperCAmelCase__						=						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__						=						[]
 UpperCAmelCase__						=						["""input_ids""", """attention_mask"""]
 def __init__( self    : Union[str, Any]     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : int="<s>"     ,	UpperCAmelCase    : Any="</s>"     ,	UpperCAmelCase    : List[Any]="<unk>"     ,	UpperCAmelCase    : Any="<pad>"     ,	UpperCAmelCase    : Dict="<::::>"     ,	UpperCAmelCase    : Optional[Dict[str, Any]] = None     ,	**UpperCAmelCase    : Any     ,	)     ->      None:
     lowerCamelCase__       : str   					=						{} if sp_model_kwargs is None else sp_model_kwargs

     # Add extra_ids to the special token list
     super().__init__(
         bos_token=UpperCAmelCase     ,	eos_token=UpperCAmelCase     ,	unk_token=UpperCAmelCase     ,	pad_token=UpperCAmelCase     ,	sep_token=UpperCAmelCase     ,	sp_model_kwargs=self.sp_model_kwargs     ,	**UpperCAmelCase     ,	)

     lowerCamelCase__       : List[str]   					=						vocab_file

     lowerCamelCase__       : List[str]   					=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(UpperCAmelCase      )
 @property
 def 	A_      ( self    : List[str]      )     ->      Optional[int]:
     return self.sp_model.get_piece_size()
 def 	A_      ( self    : Tuple      )     ->      Any:
     lowerCamelCase__       : Tuple   					=						{self.convert_ids_to_tokens(UpperCAmelCase      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab
 def __getstate__( self    : Union[str, Any]      )     ->      Optional[Any]:
     lowerCamelCase__       : List[str]   					=						self.__dict__.copy()
     lowerCamelCase__       : List[Any]   					=						None
     return state
 def __setstate__( self    : Dict     ,	UpperCAmelCase    : Optional[int]      )     ->      List[Any]:
     lowerCamelCase__       : Optional[int]   					=						d

     # for backward compatibility
     if not hasattr(self     ,	'sp_model_kwargs'      ):
         lowerCamelCase__       : Dict   					=						{}

     lowerCamelCase__       : Optional[int]   					=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(self.vocab_file      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : str      )     ->      List[str]:
     return self.sp_model.encode(UpperCAmelCase     ,	out_type=UpperCAmelCase      )
 def 	A_      ( self    : str     ,	UpperCAmelCase    : Tuple      )     ->      Union[str, Any]:
     return self.sp_model.piece_to_id(UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : Tuple      )     ->      Dict:
     lowerCamelCase__       : str   					=						self.sp_model.IdToPiece(UpperCAmelCase      )
     return token
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : int      )     ->      Union[str, Any]:
     lowerCamelCase__       : str   					=						[]
     lowerCamelCase__       : List[str]   					=						''
     for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
             out_string += self.sp_model.decode(UpperCAmelCase      ) + token
             lowerCamelCase__       : Tuple   					=						[]
         else:
             current_sub_tokens.append(UpperCAmelCase      )
     out_string += self.sp_model.decode(UpperCAmelCase      )
     return out_string.strip()







 def 	A_      ( self    : List[Any]     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : Optional[str] = None      )     ->      Tuple[str]:
     if not os.path.isdir(UpperCAmelCase      ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
         return
     lowerCamelCase__       : Any   					=						os.path.join(
         UpperCAmelCase     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

     if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCAmelCase      ) and os.path.isfile(self.vocab_file      ):
         copyfile(self.vocab_file     ,	UpperCAmelCase      )
     elif not os.path.isfile(self.vocab_file      ):
         with open(UpperCAmelCase     ,	'wb'      ) as fi:
             lowerCamelCase__       : int   					=						self.sp_model.serialized_model_proto()
             fi.write(UpperCAmelCase      )

     return (out_vocab_file,)





