code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional, Tuple
  
  from .training_args import TrainingArguments
  from .utils import cached_property, is_tf_available, logging, requires_backends
  
  
  _lowerCamelCase				:	Tuple				    =       logging.get_logger(__name__)
  
  if is_tf_available():
      import tensorflow as tf
  
  
  
  @dataclass
  class 							__UpperCAmelCase  (       a_						):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    __lowerCAmelCase 				=    'tf'
    __lowerCAmelCase 				=    field(
        default=a_		,					metadata={'''help''': '''Name of TPU'''}		,					)
  
    __lowerCAmelCase 				=    field(
        default=a_		,					metadata={'''help''': '''Zone of TPU'''}		,					)
  
    __lowerCAmelCase 				=    field(
        default=a_		,					metadata={'''help''': '''Name of Cloud TPU-enabled project'''}		,					)
  
    __lowerCAmelCase 				=    field(
        default=1.0		,					metadata={'''help''': '''Power for the Polynomial decay LR scheduler.'''}		,					)
  
    __lowerCAmelCase 				=    field(default=a_		,					metadata={'''help''': '''Whether to activate the XLA compilation or not'''}						)
  
  
  
  
  
  
  
    @cached_property
    def        A     (self						:      int      ):
     requires_backends(self      ,      ["""tf"""]      )
     logger.info("""Tensorflow: setting up strategy"""      )
  
     A							  =      tf.config.list_physical_devices("""GPU"""      )
  
     # Set to float16 at first
     if self.fpaa:
      tf.keras.mixed_precision.set_global_policy("""mixed_float16"""      )
  
     if self.no_cuda:
      A							  =      tf.distribute.OneDeviceStrategy(device="""/cpu:0"""      )
     else:
      try:
       if self.tpu_name:
        A							  =      tf.distribute.cluster_resolver.TPUClusterResolver(
            self.tpu_name      ,      zone=self.tpu_zone      ,      project=self.gcp_project      )
       else:
        A							  =      tf.distribute.cluster_resolver.TPUClusterResolver()
      except ValueError:
       if self.tpu_name:
        raise RuntimeError(F"""Couldn\'t connect to TPU {self.tpu_name}!"""      )
       else:
        A							  =      None
  
      if tpu:
       # Set to bfloat16 in case of TPU
       if self.fpaa:
        tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""      )
  
       tf.config.experimental_connect_to_cluster(_snake_case      )
       tf.tpu.experimental.initialize_tpu_system(_snake_case      )
  
       A							  =      tf.distribute.TPUStrategy(_snake_case      )
  
      elif len(_snake_case      ) == 0:
       A							  =      tf.distribute.OneDeviceStrategy(device="""/cpu:0"""      )
      elif len(_snake_case      ) == 1:
       A							  =      tf.distribute.OneDeviceStrategy(device="""/gpu:0"""      )
      elif len(_snake_case      ) > 1:
       # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
       A							  =      tf.distribute.MirroredStrategy()
      else:
       raise ValueError("""Cannot find the proper strategy, please check your environment properties."""      )
  
     return strategy
  
  
  
  
  
  
  
    @property
    def        A     (self						:      Tuple      ):
     requires_backends(self      ,      ["""tf"""]      )
     return self._setup_strategy
  
  
  
  
  
  
  
    @property
    def        A     (self						:      Tuple      ):
     requires_backends(self      ,      ["""tf"""]      )
     return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  
    @property
    def        A     (self						:      int      ):
     return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
  
  
    @property
    def        A     (self						:      List[str]      ):
     if self.per_gpu_train_batch_size:
      logger.warning(
          """Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future """
          """version. Using `--per_device_train_batch_size` is preferred."""      )
     A							  =      self.per_gpu_train_batch_size or self.per_device_train_batch_size
     return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
    @property
    def        A     (self						:      List[Any]      ):
     if self.per_gpu_eval_batch_size:
      logger.warning(
          """Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future """
          """version. Using `--per_device_eval_batch_size` is preferred."""      )
     A							  =      self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
     return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
    @property
    def        A     (self						:      int      ):
     requires_backends(self      ,      ["""tf"""]      )
     warnings.warn(
         """The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."""      ,      _snake_case      ,      )
     return self._setup_strategy.num_replicas_in_sync
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCamelCase				:	int				    =       logging.get_logger(__name__)
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    ['''input_ids''', '''attention_mask''']
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      str="[PAD]"      ,      _lowerCAmelCase						:      Dict="[BOS]"      ,      _lowerCAmelCase						:      Any="[EOS]"      ,      _lowerCAmelCase						:      List[Any]="[MASK]"      ,      _lowerCAmelCase						:      int="[CLS]"      ,      _lowerCAmelCase						:      Optional[Any]="[SEP]"      ,      _lowerCAmelCase						:      List[str]=2048      ,      **_lowerCAmelCase						:      Dict      ,      ):
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else pad_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else bos_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else eos_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else mask_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else cls_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else sep_token
  
  			super().__init__(
  			    pad_token=_lowerCAmelCase      ,      bos_token=_lowerCAmelCase      ,      eos_token=_lowerCAmelCase      ,      mask_token=_lowerCAmelCase      ,      cls_token=_lowerCAmelCase      ,      sep_token=_lowerCAmelCase      ,      model_max_length=_lowerCAmelCase      ,      **_lowerCAmelCase      ,      )
  
  			A							  =      2**8  # utf is 8 bits
  
  			# define special tokens dict
  			A							  =      {
  			    self.pad_token: 0,
  			    self.bos_token: 1,
  			    self.eos_token: 2,
  			    self.mask_token: 3,
  			    self.cls_token: 4,
  			    self.sep_token: 5,
  			}
  			A							  =      len(self.special_tokens_encoder      )
  			A							  =      {v: k for k, v in self.special_tokens_encoder.items()}
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  			A							  =      self.special_tokens_encoder.copy()
  			vocab.update(self.added_tokens_encoder      )
  			for i in range(self._utf_vocab_size      ):
  				A							  =      chr(_lowerCAmelCase      )
  				A							  =      i + len(self.special_tokens_encoder      )
  			return vocab
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Tuple      ):
  			return self._utf_vocab_size + self._num_special_tokens
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ,      _lowerCAmelCase						:      bool = False      ):
  			if already_has_special_tokens:
  				return super().get_special_tokens_mask(
  				    token_ids_a=_lowerCAmelCase      ,      token_ids_a=_lowerCAmelCase      ,      already_has_special_tokens=_lowerCAmelCase      )
  
  			# normal case: some special tokens
  			if token_ids_a is None:
  				return [1] + [0] * len(_lowerCAmelCase      ) + [1]
  			return [1] + ([0] * len(_lowerCAmelCase      )) + [1] + ([0] * len(_lowerCAmelCase      )) + [1]
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ):
  			if token_ids_a is None:
  				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  			else:
  				return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      str      ):
  			A							  =      [chr(_lowerCAmelCase      ) for i in text.encode("""utf-8"""      )]
  			return tokens
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      List[str]      ):
  			if token in self.special_tokens_encoder:
  				A							  =      self.special_tokens_encoder[token]
  			elif token in self.added_tokens_encoder:
  				A							  =      self.added_tokens_encoder[token]
  			elif len(_lowerCAmelCase      ) != 1:
  				A							  =      self.unk_token_id
  			else:
  				A							  =      ord(_lowerCAmelCase      ) + self._num_special_tokens
  			return token_id
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Any      ):
  			if index in self.special_tokens_decoder:
  				A							  =      self.special_tokens_decoder[index]
  			elif index in self.added_tokens_decoder:
  				A							  =      self.added_tokens_decoder[index]
  			else:
  				A							  =      chr(index - self._num_special_tokens      )
  			return token
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      List[str]      ):
  			A							  =      b""""""
  			for token in tokens:
  				if token in self.special_tokens_decoder:
  					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
  				elif token in self.added_tokens_decoder:
  					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
  				elif token in self.special_tokens_encoder:
  					A							  =      token.encode("""utf-8"""      )
  				elif token in self.added_tokens_encoder:
  					A							  =      token.encode("""utf-8"""      )
  				else:
  					A							  =      bytes([ord(_lowerCAmelCase      )]      )
  				bstring += tok_string
  			A							  =      bstring.decode("""utf-8"""      ,      errors="""replace"""      )
  			return string
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Optional[str] = None      ):
  			return ()
  
  