



'''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  snake_case__						(    tf.keras.optimizers.schedules.LearningRateSchedule				):
 def __init__(   self						:							Optional[Any]      ,     __a						:							float      ,     __a						:							Callable      ,     __a						:							int      ,     __a						:							float = 1.0      ,     __a						:							str = None      ,     )      ->   str:







       '''simple docstring'''
       super().__init__()
       __snake_case :   Tuple													=    initial_learning_rate
       __snake_case :   Tuple													=    warmup_steps
       __snake_case :   str													=    power
       __snake_case :   str													=    decay_schedule_fn
       __snake_case :   Optional[int]													=    name
 def __call__(   self						:							Optional[int]      ,     __a						:							Any  )      ->   Any:







       '''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 :   Dict													=    tf.cast(__a      ,     tf.floataa  )
             __snake_case :   int													=    tf.cast(self.warmup_steps      ,     tf.floataa  )
             __snake_case :   List[Any]													=    global_step_float / warmup_steps_float
             __snake_case :   List[str]													=    self.initial_learning_rate * tf.math.pow(__a      ,     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=__a      ,     )





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







       '''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   a_   (		_UpperCAmelCase    :      float ,_UpperCAmelCase    :      int ,_UpperCAmelCase    :      int ,_UpperCAmelCase    :      float = 0.0 ,_UpperCAmelCase    :      float = 0.9 ,_UpperCAmelCase    :      float = 0.9_9_9 ,_UpperCAmelCase    :      float = 1E-8 ,_UpperCAmelCase    :      Optional[float] = None ,_UpperCAmelCase    :      Optional[float] = None ,_UpperCAmelCase    :      float = 0.0 ,_UpperCAmelCase    :      float = 1.0 ,_UpperCAmelCase    :      Optional[List[str]] = None ,)    ->  List[Any]:
      __snake_case :   str													=    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:
            __snake_case :   Tuple													=    WarmUp(
                initial_learning_rate=_UpperCAmelCase ,decay_schedule_fn=_UpperCAmelCase ,warmup_steps=_UpperCAmelCase ,)
      if weight_decay_rate > 0.0:
            __snake_case :   List[str]													=    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:
            __snake_case :   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  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def __init__(   self						:							List[Any]      ,     __a						:							Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1      ,     __a						:							float = 0.9      ,     __a						:							float = 0.9_9_9      ,     __a						:							float = 1e-7      ,     __a						:							bool = False      ,     __a						:							float = 0.0      ,     __a						:							Optional[List[str]] = None      ,     __a						:							Optional[List[str]] = None      ,     __a						:							str = "AdamWeightDecay"      ,     **__a						:							int      ,     )      ->   Tuple:







       '''simple docstring'''
       super().__init__(__a      ,     __a      ,     __a      ,     __a      ,     __a      ,     __a      ,     **__a  )
       __snake_case :   int													=    weight_decay_rate
       __snake_case :   List[Any]													=    include_in_weight_decay
       __snake_case :   List[str]													=    exclude_from_weight_decay
 @classmethod
 def A_   (   cls						:							Union[str, Any]      ,     __a						:							str  )      ->   Any:







       '''simple docstring'''
       __snake_case :   List[str]													=    {'WarmUp': WarmUp}
       return super(__a      ,     cls  ).from_config(__a      ,     custom_objects=__a  )
 def A_   (   self						:							Union[str, Any]      ,     __a						:							Union[str, Any]      ,     __a						:							Union[str, Any]      ,     __a						:							Optional[int]  )      ->   List[str]:







       '''simple docstring'''
       super(__a      ,     self  )._prepare_local(__a      ,     __a      ,     __a  )
       __snake_case :   Dict													=    tf.constant(
           self.weight_decay_rate      ,     name='adam_weight_decay_rate'  )
 def A_   (   self						:							Tuple      ,     __a						:							str      ,     __a						:							List[str]      ,     __a						:							Any  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    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						:							int      ,     __a						:							Any      ,     __a						:							Union[str, Any]=None      ,     **__a						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       __snake_case							,     __snake_case :   str													=    list(zip(*__a  )  )
       return super(__a      ,     self  ).apply_gradients(zip(__a      ,     __a  )      ,     name=__a      ,     **__a  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							Union[str, Any]      ,     __a						:							Any      ,     __a						:							Tuple  )      ->   List[Any]:







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

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

       return coefficients["lr_t"], {"apply_state": apply_state}
 def A_   (   self						:							Tuple      ,     __a						:							Optional[Any]      ,     __a						:							List[Any]      ,     __a						:							int=None  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Optional[int]													=    self._get_lr(var.device      ,     var.dtype.base_dtype      ,     __a  )
       __snake_case :   Dict													=    self._decay_weights_op(__a      ,     __a      ,     __a  )
       with tf.control_dependencies([decay]  ):
             return super(__a      ,     self  )._resource_apply_dense(__a      ,     __a      ,     **__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							List[str]      ,     __a						:							List[Any]      ,     __a						:							List[str]      ,     __a						:							Optional[int]=None  )      ->   Any:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Optional[int]													=    self._get_lr(var.device      ,     var.dtype.base_dtype      ,     __a  )
       __snake_case :   Optional[int]													=    self._decay_weights_op(__a      ,     __a      ,     __a  )
       with tf.control_dependencies([decay]  ):
             return super(__a      ,     self  )._resource_apply_sparse(__a      ,     __a      ,     __a      ,     **__a  )
 def A_   (   self						:							Optional[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Dict													=    super().get_config()
       config.update({'weight_decay_rate': self.weight_decay_rate}  )
       return config





 def A_   (   self						:							Union[str, Any]      ,     __a						:							Optional[int]  )      ->   Union[str, 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(__a      ,     __a  ) is not None:
                         return True

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



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def __init__(   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    []
       __snake_case :   List[Any]													=    None
 @property
 def A_   (   self						:							Optional[int]  )      ->   Optional[Any]:







       '''simple docstring'''
       if self._accum_steps is None:
             __snake_case :   Optional[Any]													=    tf.Variable(
                 tf.constant(0      ,     dtype=tf.intaa  )      ,     trainable=__a      ,     synchronization=tf.VariableSynchronization.ON_READ      ,     aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA      ,     )

       return self._accum_steps.value()
 @property
 def A_   (   self						:							Optional[int]  )      ->   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						:							List[str]      ,     __a						:							Any  )      ->   Any:







       '''simple docstring'''
       if not self._gradients:
             __snake_case :   str													=    self.step  # Create the step variable.
             self._gradients.extend(
                 [
                     tf.Variable(
                         tf.zeros_like(__a  )      ,     trainable=__a      ,     synchronization=tf.VariableSynchronization.ON_READ      ,     aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA      ,     )
                     if gradient is not None
                     else gradient
                     for gradient in gradients
                 ]  )
       if len(__a  ) != len(self._gradients  ):
             raise ValueError(f'''Expected {len(self._gradients  )} gradients, but got {len(__a  )}'''  )

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

       self._accum_steps.assign_add(1  )





 def A_   (   self						:							int  )      ->   Tuple:







       '''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(__a  )  )








'''simple docstring'''





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


A__    :   Tuple           =					logging.get_logger(__name__)

A__    :   int           =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

A__    :   int           =					{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

A__    :   List[str]           =					{
    '''camembert-base''': 5_1_2,
}

A__    :   str           =					'''▁'''



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= ['''input_ids''', '''attention_mask''']
 def __init__(   self						:							List[Any]      ,     __a						:							int      ,     __a						:							str="<s>"      ,     __a						:							Dict="</s>"      ,     __a						:							Union[str, Any]="</s>"      ,     __a						:							List[str]="<s>"      ,     __a						:							Optional[int]="<unk>"      ,     __a						:							Optional[Any]="<pad>"      ,     __a						:							str="<mask>"      ,     __a						:							Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]      ,     __a						:							Optional[Dict[str, Any]] = None      ,     **__a						:							Any      ,     )      ->   None:







       '''simple docstring'''
       # Mask token behave like a normal word, i.e. include the space before it
       __snake_case :   Optional[int]													=    AddedToken(__a      ,     lstrip=__a      ,     rstrip=__a  ) if isinstance(__a      ,     __a  ) else mask_token

       __snake_case :   Optional[Any]													=    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__a      ,     eos_token=__a      ,     unk_token=__a      ,     sep_token=__a      ,     cls_token=__a      ,     pad_token=__a      ,     mask_token=__a      ,     additional_special_tokens=__a      ,     sp_model_kwargs=self.sp_model_kwargs      ,     **__a      ,     )
       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(__a  )  )
       __snake_case :   str													=    vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       __snake_case :   Any													=    {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       __snake_case :   Optional[int]													=    len(self.fairseq_tokens_to_ids  )
       __snake_case :   str													=    len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
       __snake_case :   Dict													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def A_   (   self						:							List[str]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''

       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __snake_case :   Dict													=    [self.cls_token_id]
       __snake_case :   Optional[int]													=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None      ,     __a						:							bool = False  )      ->   List[int]:







       '''simple docstring'''
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__a      ,     token_ids_a=__a      ,     already_has_special_tokens=__a  )

       if token_ids_a is None:
             return [1] + ([0] * len(__a  )) + [1]
       return [1] + ([0] * len(__a  )) + [1, 1] + ([0] * len(__a  )) + [1]
 def A_   (   self						:							int      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    [self.sep_token_id]
       __snake_case :   Optional[int]													=    [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 A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )
 def A_   (   self						:							List[str]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Any													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def A_   (   self						:							Dict      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[str]  )      ->   List[Any]:







       '''simple docstring'''
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(__a  ) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset  )
 def A_   (   self						:							Dict      ,     __a						:							Union[str, Any]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    []
       __snake_case :   List[Any]													=    ''
       __snake_case :   Optional[int]													=    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(__a  ) + token
                   __snake_case :   Optional[int]													=    True
                   __snake_case :   Any													=    []
             else:
                   current_sub_tokens.append(__a  )
                   __snake_case :   Dict													=    False
       out_string += self.sp_model.decode(__a  )
       return out_string.strip()
 def __getstate__(   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    self.__dict__.copy()
       __snake_case :   int													=    None
       return state
 def __setstate__(   self						:							List[str]      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[str]													=    d

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

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





 def A_   (   self						:							Tuple      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







       '''simple docstring'''
       if not os.path.isdir(__a  ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
       __snake_case :   Dict													=    os.path.join(
           __a      ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(__a  ) and os.path.isfile(self.vocab_file  ):
             copyfile(self.vocab_file      ,     __a  )
       elif not os.path.isfile(self.vocab_file  ):
             with open(__a      ,     'wb'  ) as fi:
                   __snake_case :   Union[str, Any]													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (out_vocab_file,)



