





'''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 	_lowercase    (				__UpperCamelCase					):
   _UpperCamelCase									= """tf"""
   _UpperCamelCase									= field(
       default=__UpperCamelCase		,       metadata={"""help""": """Name of TPU"""}		,       )

   _UpperCamelCase									= field(
       default=__UpperCamelCase		,       metadata={"""help""": """Zone of TPU"""}		,       )

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

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

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


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

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

               A							:							Any				=    tf.distribute.TPUStrategy(__UpperCamelCase       )

           elif len(__UpperCamelCase       ) == 0:
               A							:							Optional[Any]				=    tf.distribute.OneDeviceStrategy(device='''/cpu:0'''       )
           elif len(__UpperCamelCase       ) == 1:
               A							:							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`
               A							:							Union[str, Any]				=    tf.distribute.MirroredStrategy()
           else:
               raise ValueError('''Cannot find the proper strategy, please check your environment properties.'''       )

       return strategy


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


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


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


   @property
   def        snake_case					(			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.'''       )
       A							:							Optional[Any]				=    self.per_gpu_train_batch_size or self.per_device_train_batch_size
       return per_device_batch_size * self.n_replicas


   @property
   def        snake_case					(			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.'''       )
       A							:							int				=    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
       return per_device_batch_size * self.n_replicas


   @property
   def        snake_case					(			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.'''     , __UpperCamelCase     , )
       return self._setup_strategy.num_replicas_in_sync


'''simple docstring'''







from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class  SCREAMING_SNAKE_CASE__       :
	def __init__(				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	vocab
					__a       :     int								=	normalizers.BertNormalizer(
					    clean_text=__UpperCamelCase  ,    handle_chinese_chars=__UpperCamelCase  ,    strip_accents=__UpperCamelCase  ,    lowercase=__UpperCamelCase  ,    )
					try:
									import rjieba
					except ImportError:
									raise ImportError(
									    """You need to install rjieba to use RoFormerTokenizer. """
									    """See https://pypi.org/project/rjieba/ for installation."""			)
					__a       :     Optional[Any]								=	rjieba
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	[]

					# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
					for token, start, end in self.jieba.tokenize(str(__UpperCamelCase			)  ,    hmm=__UpperCamelCase			):
									if token in self.vocab:
													splits.append(normalized_string[start:end]			)
									else:
													__a       :     str								=	self.normalizers.normalize_str(__UpperCamelCase			).split()
													for token in token_list:
																	if token:
																					__a       :     str								=	start + len(__UpperCamelCase			)
																					splits.append(normalized_string[start:end]			)
																					__a       :     Union[str, Any]								=	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

					return splits

	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					pretok.split(self.jieba_split			)