



'''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


lowerCAmelCase: str							=						logging.get_logger(__name__)

lowerCAmelCase: Union[str, Any]							=						'▁'

lowerCAmelCase: str							=						{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase: Tuple							=						{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowerCAmelCase: str							=						{
    'xlm-roberta-base': 5_1_2,
    'xlm-roberta-large': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-english': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-german': 5_1_2,
}

class 							a__( lowerCamelCase__     ):
    lowercase__			=   VOCAB_FILES_NAMES
    lowercase__			=   PRETRAINED_VOCAB_FILES_MAP
    lowercase__			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__			=   ["""input_ids""", """attention_mask"""]







    def __init__(				self			:  Tuple						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[int]="<s>"						,      __snake_case			:  Any="</s>"						,      __snake_case			:  int="</s>"						,      __snake_case			:  int="<s>"						,      __snake_case			:  Optional[Any]="<unk>"						,      __snake_case			:  List[str]="<pad>"						,      __snake_case			:  Tuple="<mask>"						,      __snake_case			:  Optional[Dict[str, Any]] = None						,      **__snake_case			:  Any						,      ):
      # Mask token behave like a normal word, i.e. include the space before it
      a						: Tuple					       =					AddedToken(__snake_case						,      lstrip=__snake_case						,      rstrip=__snake_case     ) if isinstance(__snake_case						,      __snake_case     ) else mask_token

      a						: List[str]					       =					{} if sp_model_kwargs is None else sp_model_kwargs

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

      a						: Any					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
      self.sp_model.Load(str(__snake_case     )     )
      a						: Any					       =					vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # Mimic fairseq token-to-id alignment for the first 4 token
      a						: Any					       =					{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      a						: List[str]					       =					1

      a						: Tuple					       =					len(self.sp_model     ) + self.fairseq_offset
      a						: Any					       =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}







    def __getstate__(				self			:  List[str]     ):
      a						: int					       =					self.__dict__.copy()
      a						: Optional[Any]					       =					None
      a						: Union[str, Any]					       =					self.sp_model.serialized_model_proto()
      return state







    def __setstate__(				self			:  Optional[Any]						,      __snake_case			:  List[str]     ):
      a						: List[Any]					       =					d

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

      a						: Optional[Any]					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None     ):

      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      a						: Optional[int]					       =					[self.cls_token_id]
      a						: List[str]					       =					[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None						,      __snake_case			:  bool = False     ):

      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__snake_case						,      token_ids_a=__snake_case						,      already_has_special_tokens=__snake_case     )

      if token_ids_a is None:
        return [1] + ([0] * len(__snake_case     )) + [1]
      return [1] + ([0] * len(__snake_case     )) + [1, 1] + ([0] * len(__snake_case     )) + [1]







    def       lowercase_	(				self			:  Any						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None     ):
      a						: Optional[Any]					       =					[self.sep_token_id]
      a						: Optional[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       lowercase_	(				self			:  Optional[Any]     ):
      return len(self.sp_model     ) + self.fairseq_offset + 1  # Add the <mask> token







    def       lowercase_	(				self			:  int     ):
      a						: Optional[int]					       =					{self.convert_ids_to_tokens(__snake_case     ): i for i in range(self.vocab_size     )}
      vocab.update(self.added_tokens_encoder     )
      return vocab







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  str     ):
      return self.sp_model.encode(__snake_case						,      out_type=__snake_case     )







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  str     ):
      if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
      a						: List[str]					       =					self.sp_model.PieceToId(__snake_case     )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Tuple     ):
      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       lowercase_	(				self			:  Tuple						,      __snake_case			:  Dict     ):
      a						: Optional[Any]					       =					''.join(__snake_case     ).replace(__snake_case						,      ' '     ).strip()
      return out_string







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

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

      return (out_vocab_file,)




'''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			:  Optional[Any]						,      __snake_case			:  float						,      __snake_case			:  Callable						,      __snake_case			:  int						,      __snake_case			:  float = 1.0						,      __snake_case			:  str = None						,      ):
      super().__init__()
      a						: Dict					       =					initial_learning_rate
      a						: Optional[int]					       =					warmup_steps
      a						: Tuple					       =					power
      a						: str					       =					decay_schedule_fn
      a						: Optional[int]					       =					name







    def __call__(				self			:  List[Any]						,      __snake_case			:  int     ):
      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						: Optional[int]					       =					tf.cast(__snake_case						,      tf.floataa     )
        a						: Tuple					       =					tf.cast(self.warmup_steps						,      tf.floataa     )
        a						: str					       =					global_step_float / warmup_steps_float
        a						: Union[str, Any]					       =					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       lowercase_	(				self			:  Dict     ):
      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__	(						_A			,      _A			,      _A			,      _A = 0.0			,      _A = 0.9			,      _A = 0.999			,      _A = 1E-8			,      _A = None			,      _A = None			,      _A = 0.0			,      _A = 1.0			,      _A = None			,      ):
  a						: Optional[int]					       =					tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=_A			,      decay_steps=num_train_steps - num_warmup_steps			,      end_learning_rate=init_lr * min_lr_ratio			,      power=_A			,      )
  if num_warmup_steps:
    a						: Tuple					       =					WarmUp(
        initial_learning_rate=_A			,      decay_schedule_fn=_A			,      warmup_steps=_A			,      )
  if weight_decay_rate > 0.0:
    a						: List[Any]					       =					AdamWeightDecay(
        learning_rate=_A			,      weight_decay_rate=_A			,      beta_a=_A			,      beta_a=_A			,      epsilon=_A			,      clipnorm=_A			,      global_clipnorm=_A			,      exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']			,      include_in_weight_decay=_A			,      )
  else:
    a						: Tuple					       =					tf.keras.optimizers.Adam(
        learning_rate=_A			,      beta_a=_A			,      beta_a=_A			,      epsilon=_A			,      clipnorm=_A			,      global_clipnorm=_A			,      )
  # 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__( lowerCamelCase__     ):







    def __init__(				self			:  Union[str, Any]						,      __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			:  Optional[int]						,      ):
      super().__init__(__snake_case						,      __snake_case						,      __snake_case						,      __snake_case						,      __snake_case						,      __snake_case						,      **__snake_case     )
      a						: Tuple					       =					weight_decay_rate
      a						: Optional[Any]					       =					include_in_weight_decay
      a						: Any					       =					exclude_from_weight_decay







    @classmethod
    def       lowercase_	(				cls			:  Any						,      __snake_case			:  Optional[Any]     ):
      a						: Any					       =					{'WarmUp': WarmUp}
      return super(__snake_case						,      cls     ).from_config(__snake_case						,      custom_objects=__snake_case     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Optional[int]						,      __snake_case			:  Dict						,      __snake_case			:  Optional[int]     ):
      super(__snake_case						,      self     )._prepare_local(__snake_case						,      __snake_case						,      __snake_case     )
      a						: List[str]					       =					tf.constant(
          self.weight_decay_rate						,      name='adam_weight_decay_rate'     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  Dict						,      __snake_case			:  List[Any]						,      __snake_case			:  str     ):
      a						: Optional[Any]					       =					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]						,      __snake_case			:  List[str]						,      __snake_case			:  Any=None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Dict					       =					list(zip(*__snake_case     )     )
      return super(__snake_case						,      self     ).apply_gradients(zip(__snake_case						,      __snake_case     )						,      name=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[int]     ):
      if apply_state is None:
        return self._decayed_lr_t[var_dtype], {}

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

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







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Dict						,      __snake_case			:  Tuple=None     ):
      a       ,     a						: List[Any]					       =					self._get_lr(var.device						,      var.dtype.base_dtype						,      __snake_case     )
      a						: List[Any]					       =					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       lowercase_	(				self			:  Any						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  List[str]						,      __snake_case			:  int=None     ):
      a       ,     a						: Tuple					       =					self._get_lr(var.device						,      var.dtype.base_dtype						,      __snake_case     )
      a						: int					       =					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       lowercase_	(				self			:  Optional[Any]     ):
      a						: Union[str, Any]					       =					super().get_config()
      config.update({'weight_decay_rate': self.weight_decay_rate}     )
      return config







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Dict     ):
      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 							a__( lowerCamelCase__     ):







    def __init__(				self			:  List[str]     ):
      a						: List[Any]					       =					[]
      a						: Optional[int]					       =					None







    @property
    def       lowercase_	(				self			:  Any     ):
      if self._accum_steps is None:
        a						: List[Any]					       =					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       lowercase_	(				self			:  Optional[int]     ):
      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]						,      __snake_case			:  Any     ):
      if not self._gradients:
        a						: Union[str, Any]					       =					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       lowercase_	(				self			:  Tuple     ):
      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     )     )