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

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


lowercase__   			=      logging.get_logger(__name__)




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						["""input_ids""", """attention_mask"""]



      def __init__(		self       ,     lowercase="[PAD]"       ,     lowercase="[BOS]"       ,     lowercase="[EOS]"       ,     lowercase="[MASK]"       ,     lowercase="[CLS]"       ,     lowercase="[SEP]"       ,     lowercase=20_48       ,     **lowercase       ,     )      ->     None:





        '''simple docstring'''





        a__:	Any     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else pad_token
        a__:	Tuple     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else bos_token
        a__:	Union[str, Any]     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else eos_token
        a__:	Tuple     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else mask_token
        a__:	int     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else cls_token
        a__:	Any     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else sep_token

        super().__init__(
            pad_token=lowercase       ,     bos_token=lowercase       ,     eos_token=lowercase       ,     mask_token=lowercase       ,     cls_token=lowercase       ,     sep_token=lowercase       ,     model_max_length=lowercase       ,     **lowercase       ,     )

        a__:	Optional[Any]     				=      2**8  # utf is 8 bits

        # define special tokens dict
        a__:	Dict[str, int]     				=      {
            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__:	Union[str, Any]     				=      len(self.special_tokens_encoder)
        a__:	Dict[int, str]     				=      {v: k for k, v in self.special_tokens_encoder.items()}



      def     lowerCamelCase_  (		self)      ->     Dict[str, int]:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.special_tokens_encoder.copy()
        vocab.update(self.added_tokens_encoder)
        for i in range(self._utf_vocab_size):
          a__:	Optional[int]     				=      chr(lowercase)
          a__:	str     				=      i + len(self.special_tokens_encoder)
        return vocab



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





        '''simple docstring'''





        return self._utf_vocab_size + self._num_special_tokens



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = False)      ->     List[int]:





        '''simple docstring'''





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

        # normal case: some special tokens
        if token_ids_a is None:
          return [1] + [0] * len(lowercase) + [1]
        return [1] + ([0] * len(lowercase)) + [1] + ([0] * len(lowercase)) + [1]



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None)      ->     List[int]:





        '''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       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Any     				=      [chr(lowercase) for i in text.encode('utf-8')]
        return tokens



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        if token in self.special_tokens_encoder:
          a__:	Optional[int]     				=      self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
          a__:	Optional[int]     				=      self.added_tokens_encoder[token]
        elif len(lowercase) != 1:
          a__:	Dict     				=      self.unk_token_id
        else:
          a__:	Optional[Any]     				=      ord(lowercase) + self._num_special_tokens
        return token_id



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        if index in self.special_tokens_decoder:
          a__:	List[Any]     				=      self.special_tokens_decoder[index]
        elif index in self.added_tokens_decoder:
          a__:	Tuple     				=      self.added_tokens_decoder[index]
        else:
          a__:	List[str]     				=      chr(index - self._num_special_tokens)
        return token



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[int]:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None)      ->     Tuple[str]:





        '''simple docstring'''





        return ()




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_				:							Optional[Any]       =    logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf







@dataclass
class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""tf"""
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Name of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Zone of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Name of Cloud TPU-enabled project"""}							,					)

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

     lowercase__				  =			field(default=lowerCamelCase							,					metadata={"""help""": """Whether to activate the XLA compilation or not"""}					)

     @cached_property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            logger.info('Tensorflow: setting up strategy'    )

            __snake_case						       =   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:
                   __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
            else:
                   try:
                          if self.tpu_name:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver(
                                     self.tpu_name							,       zone=self.tpu_zone							,       project=self.gcp_project    )
                          else:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver()
                   except ValueError:
                          if self.tpu_name:
                                 raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''    )
                          else:
                                 __snake_case						       =   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(SCREAMING_SNAKE_CASE_    )
                          tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE_    )

                          __snake_case						       =   tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE_    )

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

            return strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy.num_replicas_in_sync

     @property
     def 						__lowerCamelCase				(  self    ):
            return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 						__lowerCamelCase				(  self    ):
            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.'    )
            __snake_case						       =   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    ):
            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.'    )
            __snake_case						       =   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    ):
            requires_backends(self							,       ['tf']    )
            warnings.warn(
                'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'							,       SCREAMING_SNAKE_CASE_							,       )
            return self._setup_strategy.num_replicas_in_sync
