



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			{'''vocab_file''': '''sentencepiece.bpe.model'''}

__UpperCAmelCase			  =			{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

__UpperCAmelCase			  =			{
    '''moussaKam/mbarthez''': 1_024,
    '''moussaKam/barthez''': 1_024,
    '''moussaKam/barthez-orangesum-title''': 1_024,
}

__UpperCAmelCase			  =			'''▁'''



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   Any  	=    VOCAB_FILES_NAMES
       UpperCAmelCase__							:   List[str]  	=    PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__							:   int  	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__							:   int  	=    ["input_ids", "attention_mask"]
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_="<s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="<s>",     SCREAMING_SNAKE_CASE_="<unk>",     SCREAMING_SNAKE_CASE_="<pad>",     SCREAMING_SNAKE_CASE_="<mask>",     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						None:
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCamelCase       :    str		      =      AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) else mask_token

          UpperCamelCase       :    str		      =      {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=SCREAMING_SNAKE_CASE_,     eos_token=SCREAMING_SNAKE_CASE_,     unk_token=SCREAMING_SNAKE_CASE_,     sep_token=SCREAMING_SNAKE_CASE_,     cls_token=SCREAMING_SNAKE_CASE_,     pad_token=SCREAMING_SNAKE_CASE_,     mask_token=SCREAMING_SNAKE_CASE_,     sp_model_kwargs=self.sp_model_kwargs,     **SCREAMING_SNAKE_CASE_,     )

          UpperCamelCase       :    Any		      =      vocab_file
          UpperCamelCase       :    Optional[Any]		      =      spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.Load(str(SCREAMING_SNAKE_CASE_			)			)

          UpperCamelCase       :    Union[str, Any]		      =      {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

          UpperCamelCase       :    int		      =      len(self.sp_model			) - 1
          UpperCamelCase       :    Any		      =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:

          if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCamelCase       :    Union[str, Any]		      =      [self.cls_token_id]
          UpperCamelCase       :    List[Any]		      =      [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = False			)	->						List[int]:
          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=SCREAMING_SNAKE_CASE_,     token_ids_a=SCREAMING_SNAKE_CASE_,     already_has_special_tokens=SCREAMING_SNAKE_CASE_			)

          if token_ids_a is None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
          return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:
          UpperCamelCase       :    Tuple		      =      [self.sep_token_id]
          UpperCamelCase       :    List[Any]		      =      [self.cls_token_id]

          if token_ids_a is None:
             return len(cls + token_ids_a + sep			) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]
       @property
       def    snake_case_			(     self			)	->						List[str]:
          return len(self.sp_model			)
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    List[str]		      =      {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_			): i for i in range(self.vocab_size			)}
          vocab.update(self.added_tokens_encoder			)
          return vocab
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          return self.sp_model.encode(SCREAMING_SNAKE_CASE_,     out_type=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
          UpperCamelCase       :    str		      =      self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_			)

          return spm_id if spm_id else self.unk_token_id
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						int:
          UpperCamelCase       :    str		      =      []
          UpperCamelCase       :    Optional[Any]		      =      ''
          UpperCamelCase       :    str		      =      False
          for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                if not prev_is_special:
                   out_string += " "
                out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_			) + token
                UpperCamelCase       :    Optional[int]		      =      True
                UpperCamelCase       :    Optional[int]		      =      []
             else:
                current_sub_tokens.append(SCREAMING_SNAKE_CASE_			)
                UpperCamelCase       :    Optional[Any]		      =      False
          out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_			)
          return out_string.strip()
       def __getstate__(     self			)	->						str:
          UpperCamelCase       :    Dict		      =      self.__dict__.copy()
          UpperCamelCase       :    Union[str, Any]		      =      None
          return state
       def __setstate__(     self,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          UpperCamelCase       :    Any		      =      d

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

          UpperCamelCase       :    Optional[int]		      =      spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.Load(self.vocab_file			)






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

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

          return (out_vocab_file,)







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






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							float     ,					_lowerCAmelCase							:							Callable     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							float = 1.0     ,					_lowerCAmelCase							:							str = None     ,					) ->			str:




     """simple docstring"""
     super().__init__()
     snake_case_            =       initial_learning_rate
     snake_case_            =       warmup_steps
     snake_case_            =       power
     snake_case_            =       decay_schedule_fn
     snake_case_            =       name



  def __call__(  self							:							Any     ,					_lowerCAmelCase							:							int	) ->			Tuple:




     """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`.
        snake_case_            =       tf.cast(_lowerCAmelCase     ,					tf.floataa	)
        snake_case_            =       tf.cast(self.warmup_steps     ,					tf.floataa	)
        snake_case_            =       global_step_float / warmup_steps_float
        snake_case_            =       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[Any]	) ->			Dict:




     """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_				:float		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:float = 0.0		,    lowerCAmelCase_				:float = 0.9		,    lowerCAmelCase_				:float = 0.9_9_9		,    lowerCAmelCase_				:float = 1e-8		,    lowerCAmelCase_				:Optional[float] = None		,    lowerCAmelCase_				:Optional[float] = None		,    lowerCAmelCase_				:float = 0.0		,    lowerCAmelCase_				:float = 1.0		,    lowerCAmelCase_				:Optional[List[str]] = None		,    )->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       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:
      snake_case_            =       WarmUp(
          initial_learning_rate=lowerCAmelCase_		,    decay_schedule_fn=lowerCAmelCase_		,    warmup_steps=lowerCAmelCase_		,    )
   if weight_decay_rate > 0.0:
      snake_case_            =       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:
      snake_case_            =       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      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							Dict     ,					_lowerCAmelCase							:							Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001     ,					_lowerCAmelCase							:							float = 0.9     ,					_lowerCAmelCase							:							float = 0.999     ,					_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]     ,					) ->			Optional[int]:




     """simple docstring"""
     super().__init__(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       weight_decay_rate
     snake_case_            =       include_in_weight_decay
     snake_case_            =       exclude_from_weight_decay



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Dict     ,					_lowerCAmelCase							:							Optional[Any]	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       {"WarmUp": WarmUp}
     return super(_lowerCAmelCase     ,					cls	).from_config(_lowerCAmelCase     ,					custom_objects=_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							Optional[int]	) ->			List[str]:




     """simple docstring"""
     super(_lowerCAmelCase     ,					self	)._prepare_local(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     snake_case_            =       tf.constant(
         self.weight_decay_rate     ,					name="adam_weight_decay_rate"	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       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							:							List[str]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[int]=None     ,					**_lowerCAmelCase							:							Dict	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       list(zip(*_lowerCAmelCase	)	)
     return super(_lowerCAmelCase     ,					self	).apply_gradients(zip(_lowerCAmelCase     ,					_lowerCAmelCase	)     ,					name=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[int]	) ->			int:




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

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

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



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							Tuple=None	) ->			List[str]:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       self._get_lr(var.device     ,					var.dtype.base_dtype     ,					_lowerCAmelCase	)
     snake_case_            =       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							:							List[Any]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Tuple=None	) ->			List[str]:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       self._get_lr(var.device     ,					var.dtype.base_dtype     ,					_lowerCAmelCase	)
     snake_case_            =       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							:							Union[str, Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       super().get_config()
     config.update({"weight_decay_rate": self.weight_decay_rate}	)
     return config



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]	) ->			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(_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      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							str	) ->			Dict:




     """simple docstring"""
     snake_case_            =       []
     snake_case_            =       None



  @property
  def        lowerCAmelCase__	(  self							:							Dict	) ->			List[Any]:




     """simple docstring"""
     if self._accum_steps is None:
        snake_case_            =       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							:							Tuple	) ->			List[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							:							List[Any]     ,					_lowerCAmelCase							:							Union[str, Any]	) ->			Optional[Any]:




     """simple docstring"""
     if not self._gradients:
        snake_case_            =       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							:							Union[str, Any]	) ->			Optional[Any]:




     """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	)	)




