



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_xlnet import XLNetTokenizer
else:
	__A       =						None


__A       =						logging.get_logger(__name__)

__A       =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A       =						{
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json''',
    },
}

__A       =						{
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

__A       =						'''▁'''

# Segments (not really needed)
__A       =						0
__A       =						1
__A       =						2
__A       =						3
__A       =						4






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: Optional[int]		       =							VOCAB_FILES_NAMES
	a__				: Any		       =							PRETRAINED_VOCAB_FILES_MAP
	a__				: List[str]		       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__				: Optional[Any]		       =							"left"
	a__				: List[Any]		       =							XLNetTokenizer




	def __init__(     self     :						Any						,  __UpperCAmelCase     :						Optional[int]=None						,  __UpperCAmelCase     :						List[Any]=None						,  __UpperCAmelCase     :						Dict=False						,  __UpperCAmelCase     :						Tuple=True						,  __UpperCAmelCase     :						List[Any]=False						,  __UpperCAmelCase     :						Optional[Any]="<s>"						,  __UpperCAmelCase     :						Any="</s>"						,  __UpperCAmelCase     :						Any="<unk>"						,  __UpperCAmelCase     :						str="<sep>"						,  __UpperCAmelCase     :						int="<pad>"						,  __UpperCAmelCase     :						Tuple="<cls>"						,  __UpperCAmelCase     :						Union[str, Any]="<mask>"						,  __UpperCAmelCase     :						List[Any]=["<eop>", "<eod>"]						,  **__UpperCAmelCase     :						str						,  )      ->			Dict:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_=       AddedToken(__UpperCAmelCase						,  lstrip=__UpperCAmelCase						,  rstrip=__UpperCAmelCase					) if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) else mask_token

							super().__init__(
							    vocab_file=__UpperCAmelCase						,  tokenizer_file=__UpperCAmelCase						,  do_lower_case=__UpperCAmelCase						,  remove_space=__UpperCAmelCase						,  keep_accents=__UpperCAmelCase						,  bos_token=__UpperCAmelCase						,  eos_token=__UpperCAmelCase						,  unk_token=__UpperCAmelCase						,  sep_token=__UpperCAmelCase						,  pad_token=__UpperCAmelCase						,  cls_token=__UpperCAmelCase						,  mask_token=__UpperCAmelCase						,  additional_special_tokens=__UpperCAmelCase						,  **__UpperCAmelCase						,  )

							UpperCAmelCase_=       3
							UpperCAmelCase_=       do_lower_case
							UpperCAmelCase_=       remove_space
							UpperCAmelCase_=       keep_accents
							UpperCAmelCase_=       vocab_file
							UpperCAmelCase_=       False if not self.vocab_file else True




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
							UpperCAmelCase_=       [self.sep_token_id]
							UpperCAmelCase_=       [self.cls_token_id]
							if token_ids_a is None:
													return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
							UpperCAmelCase_=       [self.sep_token_id]
							UpperCAmelCase_=       [2]

							if token_ids_a is None:
													return len(token_ids_a + sep					) * [0] + cls_segment_id
							return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1] + cls_segment_id




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Optional[str] = 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(__UpperCAmelCase					):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
													return
							UpperCAmelCase_=       os.path.join(
							    __UpperCAmelCase						,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

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

							return (out_vocab_file,)









import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__A       =						logging.get_logger(__name__)



def 	__a					(   lowerCAmelCase_       : Tuple=None      ,lowerCAmelCase_       : Optional[Any]=None    )				->			Tuple:






						'''simple docstring'''



						return field(default_factory=lambda: default      ,metadata=lowerCAmelCase_    )






@dataclass
class 		lowercase						:






	"""simple docstring"""


	a__				: List[str]		       =							list_field(
	    default=[]			,   metadata={
	        "help": (
	            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
	            " of all available models"
	        )
	    }			,   )

	a__				: List[int]		       =							list_field(
	    default=[8]			,   metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"})

	a__				: List[int]		       =							list_field(
	    default=[8, 32, 128, 512]			,   metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}			,   )

	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}			,   )
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}			,   )
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Use FP16 to accelerate inference."})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Benchmark training of model"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Verbose memory tracing"})
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}			,   )
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={
	        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
	    }			,   )
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Trace memory line by line"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Save result to a CSV file"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Save all print statements in a log file"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Whether to print environment information"})
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={
	        "help": (
	            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
	            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
	            " for debugging / testing and on TPU."
	        )
	    }			,   )
	a__				: str		       =							field(
	    default=F'inference_time_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving time results to csv."}			,   )
	a__				: str		       =							field(
	    default=F'inference_memory_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving memory results to csv."}			,   )
	a__				: str		       =							field(
	    default=F'train_time_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving time results to csv for training."}			,   )
	a__				: str		       =							field(
	    default=F'train_memory_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving memory results to csv for training."}			,   )
	a__				: str		       =							field(
	    default=F'env_info_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving environment information."}			,   )
	a__				: str		       =							field(
	    default=F'log_{round(time())}.csv'			,   metadata={"help": "Log filename used if print statements are saved in log."}			,   )
	a__				: int		       =							field(default=3			,   metadata={"help": "Times an experiment will be run."})
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={
	        "help": (
	            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
	            " model weights."
	        )
	    }			,   )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			str:
							warnings.warn(
							    F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
							    """ are deprecated in general and it is advised to use external Benchmarking libraries """
							    """ to benchmark Transformer models."""						,  __UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[str]:
							return json.dumps(dataclasses.asdict(self					)						,  indent=2					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[str]:
							if len(self.models					) <= 0:
													raise ValueError(
													    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
													    """ bert-base-cased` or `args.models = ['bert-base-cased']."""					)
							return self.models




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Tuple:
							if not self.multi_process:
													return False
							elif self.is_tpu:
													logger.info("""Multiprocessing is currently not possible on TPU."""					)
													return False
							else:
													return True




