



'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_camembert import CamembertTokenizer
else:
						lowercase_      :				int   					=    None


lowercase_      :				Union[str, Any]   					=    logging.get_logger(__name__)

lowercase_      :				Optional[Any]   					=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase_      :				List[Any]   					=    {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowercase_      :				Dict   					=    {
    '''camembert-base''': 512,
}

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




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					VOCAB_FILES_NAMES
	__A			  =					PRETRAINED_VOCAB_FILES_MAP
	__A			  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A			  =					['''input_ids''', '''attention_mask''']
	__A			  =					CamembertTokenizer







	def __init__(    self ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase="<s>" ,				_lowerCAmelCase="</s>" ,				_lowerCAmelCase="</s>" ,				_lowerCAmelCase="<s>" ,				_lowerCAmelCase="<unk>" ,				_lowerCAmelCase="<pad>" ,				_lowerCAmelCase="<mask>" ,				_lowerCAmelCase=["<s>NOTUSED", "</s>NOTUSED"] ,				**_lowerCAmelCase ,				)    ->    Any:
							lowercase        =							AddedToken(_lowerCAmelCase ,				lstrip=_lowerCAmelCase ,				rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase ,				_lowerCAmelCase						) else mask_token

							super().__init__(
							    _lowerCAmelCase ,				tokenizer_file=_lowerCAmelCase ,				bos_token=_lowerCAmelCase ,				eos_token=_lowerCAmelCase ,				sep_token=_lowerCAmelCase ,				cls_token=_lowerCAmelCase ,				unk_token=_lowerCAmelCase ,				pad_token=_lowerCAmelCase ,				mask_token=_lowerCAmelCase ,				additional_special_tokens=_lowerCAmelCase ,				**_lowerCAmelCase ,				)

							lowercase        =							vocab_file
							lowercase        =							False if not self.vocab_file else True







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    List[int]:






							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase        =							[self.cls_token_id]
							lowercase        =							[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    List[int]:
							lowercase        =							[self.sep_token_id]
							lowercase        =							[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]







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    Tuple[str]:






							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
													    """tokenizer."""						)

							if not os.path.isdir(_lowerCAmelCase						):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
													return
							lowercase        =							os.path.join(
							    _lowerCAmelCase ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(_lowerCAmelCase						):
													copyfile(self.vocab_file ,				_lowerCAmelCase						)

							return (out_vocab_file,)







'''simple docstring'''


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
						import tensorflow as tf


lowercase_      :				Union[str, Any]   					=    logging.get_logger(__name__)




@dataclass
class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					[
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]







	def __init__(    self ,				**_lowerCAmelCase						)    ->    Optional[int]:
							'''simple docstring'''






							for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																			lowercase        =							deprecated_arg[3:]
																			lowercase        =							not kwargs.pop(_lowerCAmelCase						)
																			logger.warning(
																			    F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																			    F""" {positive_arg}={kwargs[positive_arg]}"""						)
							lowercase        =							kwargs.pop("""tpu_name""" ,				self.tpu_name						)
							lowercase        =							kwargs.pop("""device_idx""" ,				self.device_idx						)
							lowercase        =							kwargs.pop("""eager_mode""" ,				self.eager_mode						)
							lowercase        =							kwargs.pop("""use_xla""" ,				self.use_xla						)
							super().__init__(**_lowerCAmelCase						)

	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''Name of TPU'''}			,   )
	__A			  =					field(
	    default=0			,   metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}			,   )
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Benchmark models in eager model.'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={
	        '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
	    }			,   )







	@cached_property
	def   _a   (    self						)    ->    Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							lowercase        =							None
							if self.tpu:
													try:
																			if self.tpu_name:
																									lowercase        =							tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name						)
																			else:
																									lowercase        =							tf.distribute.cluster_resolver.TPUClusterResolver()
													except ValueError:
																			lowercase        =							None
							return tpu







	@cached_property
	def   _a   (    self						)    ->    Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							if self.is_tpu:
													tf.config.experimental_connect_to_cluster(self._setup_tpu						)
													tf.tpu.experimental.initialize_tpu_system(self._setup_tpu						)

													lowercase        =							tf.distribute.TPUStrategy(self._setup_tpu						)
							else:
													# currently no multi gpu is allowed
													if self.is_gpu:
																			# TODO: Currently only single GPU is supported
																			tf.config.set_visible_devices(self.gpu_list[self.device_idx] ,				"""GPU"""						)
																			lowercase        =							tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""						)
													else:
																			tf.config.set_visible_devices([] ,				"""GPU"""						)  # disable GPU
																			lowercase        =							tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""						)

							return strategy







	@property
	def   _a   (    self						)    ->    bool:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							return self._setup_tpu is not None







	@property
	def   _a   (    self						)    ->    "tf.distribute.Strategy":
							'''simple docstring'''






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







	@property
	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							return tf.config.list_physical_devices("""GPU"""						)







	@property
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							if self.cuda:
													return len(self.gpu_list						)
							return 0







	@property
	def   _a   (    self						)    ->    bool:
							'''simple docstring'''






							return self.n_gpu > 0


