
'''simple docstring'''
from typing import Dict, List, Optional, Tuple

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


_snake_case     						=						logging.get_logger(__name__)


class      a__						(							lowerCAmelCase_						):
   _SCREAMING_SNAKE_CASE					:  Optional[int]			 =							['input_ids', 'attention_mask']


   def __init__(				self    ,       _UpperCamelCase="[PAD]"    ,       _UpperCamelCase="[BOS]"    ,       _UpperCamelCase="[EOS]"    ,       _UpperCamelCase="[MASK]"    ,       _UpperCamelCase="[CLS]"    ,       _UpperCamelCase="[SEP]"    ,       _UpperCamelCase=2048    ,       **_UpperCamelCase    ,       ):





      """simple docstring"""





      _lowercase    :   str      =     AddedToken(lowerCamelCase_    ,       lstrip=lowerCamelCase_    ,       rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_    ,       lowerCamelCase_						) else pad_token
      _lowercase    :   Union[str, Any]      =     AddedToken(lowerCamelCase_    ,       lstrip=lowerCamelCase_    ,       rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_    ,       lowerCamelCase_						) else bos_token
      _lowercase    :   List[Any]      =     AddedToken(lowerCamelCase_    ,       lstrip=lowerCamelCase_    ,       rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_    ,       lowerCamelCase_						) else eos_token
      _lowercase    :   Union[str, Any]      =     AddedToken(lowerCamelCase_    ,       lstrip=lowerCamelCase_    ,       rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_    ,       lowerCamelCase_						) else mask_token
      _lowercase    :   int      =     AddedToken(lowerCamelCase_    ,       lstrip=lowerCamelCase_    ,       rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_    ,       lowerCamelCase_						) else cls_token
      _lowercase    :   int      =     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_    ,       )

      _lowercase    :   Dict      =     2**8  # utf is 8 bits

      # define special tokens dict
      _lowercase    :   Optional[Any]      =     {
          self.pad_token: 0,
          self.bos_token: 1,
          self.eos_token: 2,
          self.mask_token: 3,
          self.cls_token: 4,
          self.sep_token: 5,
      }
      _lowercase    :   Optional[Any]      =     len(self.special_tokens_encoder						)
      _lowercase    :   int      =     {v: k for k, v in self.special_tokens_encoder.items()}


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   str      =     self.special_tokens_encoder.copy()
      vocab.update(self.added_tokens_encoder						)
      for i in range(self._utf_vocab_size						):
         _lowercase    :   List[str]      =     chr(lowerCamelCase_						)
         _lowercase    :   Any      =     i + len(self.special_tokens_encoder						)
      return vocab


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self._utf_vocab_size + self._num_special_tokens


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase = None    ,       _UpperCamelCase = False						):





      """simple docstring"""





      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 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase = None						):





      """simple docstring"""





      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 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Dict      =     [chr(lowerCamelCase_						) for i in text.encode("utf-8"						)]
      return tokens


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      if token in self.special_tokens_encoder:
         _lowercase    :   str      =     self.special_tokens_encoder[token]
      elif token in self.added_tokens_encoder:
         _lowercase    :   List[str]      =     self.added_tokens_encoder[token]
      elif len(lowerCamelCase_						) != 1:
         _lowercase    :   Dict      =     self.unk_token_id
      else:
         _lowercase    :   Any      =     ord(lowerCamelCase_						) + self._num_special_tokens
      return token_id


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      if index in self.special_tokens_decoder:
         _lowercase    :   Optional[int]      =     self.special_tokens_decoder[index]
      elif index in self.added_tokens_decoder:
         _lowercase    :   str      =     self.added_tokens_decoder[index]
      else:
         _lowercase    :   Optional[Any]      =     chr(index - self._num_special_tokens						)
      return token


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Tuple      =     B""
      for token in tokens:
         if token in self.special_tokens_decoder:
            _lowercase    :   List[str]      =     self.special_tokens_decoder[token].encode("utf-8"						)
         elif token in self.added_tokens_decoder:
            _lowercase    :   str      =     self.special_tokens_decoder[token].encode("utf-8"						)
         elif token in self.special_tokens_encoder:
            _lowercase    :   Union[str, Any]      =     token.encode("utf-8"						)
         elif token in self.added_tokens_encoder:
            _lowercase    :   int      =     token.encode("utf-8"						)
         else:
            _lowercase    :   List[str]      =     bytes([ord(lowerCamelCase_						)]						)
         bstring += tok_string
      _lowercase    :   Tuple      =     bstring.decode("utf-8"    ,       errors="replace"						)
      return string






   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase = None						):





      """simple docstring"""





      return ()









'''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


_snake_case     						=						logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf


@dataclass
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  int			 =							'tf'
   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Zone of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of Cloud TPU-enabled project'}						,   )

   _SCREAMING_SNAKE_CASE					:  float			 =							field(
       default=1.0						,   metadata={'help': 'Power for the Polynomial decay LR scheduler.'}						,   )

   _SCREAMING_SNAKE_CASE					:  bool			 =							field(default=lowerCamelCase_						,   metadata={'help': 'Whether to activate the XLA compilation or not'}						)


   @cached_property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      logger.info("Tensorflow: setting up strategy"						)

      _lowercase    :   Optional[int]      =     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:
         _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
      else:
         try:
            if self.tpu_name:
               _lowercase    :   int      =     tf.distribute.cluster_resolver.TPUClusterResolver(
                   self.tpu_name    ,       zone=self.tpu_zone    ,       project=self.gcp_project						)
            else:
               _lowercase    :   Any      =     tf.distribute.cluster_resolver.TPUClusterResolver()
         except ValueError:
            if self.tpu_name:
               raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''						)
            else:
               _lowercase    :   Tuple      =     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(_UpperCamelCase						)
            tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase						)

            _lowercase    :   List[str]      =     tf.distribute.TPUStrategy(_UpperCamelCase						)

         elif len(_UpperCamelCase						) == 0:
            _lowercase    :   Optional[Any]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
         elif len(_UpperCamelCase						) == 1:
            _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/gpu:0"						)
         elif len(_UpperCamelCase						) > 1:
            # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
            _lowercase    :   Any      =     tf.distribute.MirroredStrategy()
         else:
            raise ValueError("Cannot find the proper strategy, please check your environment properties."						)

      return strategy


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      return self._setup_strategy


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      return self._setup_strategy.num_replicas_in_sync


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return False  # TF Logging is handled by Keras not the Trainer


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      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."						)
      _lowercase    :   str      =     self.per_gpu_train_batch_size or self.per_device_train_batch_size
      return per_device_batch_size * self.n_replicas


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      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."						)
      _lowercase    :   List[Any]      =     self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
      return per_device_batch_size * self.n_replicas






   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      warnings.warn(
          "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."    ,       _UpperCamelCase    ,       )
      return self._setup_strategy.num_replicas_in_sync






