



"""simple docstring"""







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

from ..utils import logging


lowerCAmelCase_   						=   logging.get_logger(__name__)



def      __UpperCAmelCase      (    __lowerCamelCase=None						,     __lowerCamelCase=None	)							->      Union[str, Any]:
		return field(default_factory=lambda: default						,     metadata=__lowerCamelCase	)





@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					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"
		        )
		    }     ,)

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

		lowerCAmelCase			:					List[int]   	=				list_field(
		    default=[8, 3_2, 1_2_8, 5_1_2]     ,metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}     ,)

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







		def 					UpperCAmelCase    (	self						:  Dict						)					->		int:






				"""simple docstring"""



				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.'''			,_snake_case			,)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		str:






				"""simple docstring"""



				return json.dumps(dataclasses.asdict(self						)			,indent=2						)







		@property
		def 					UpperCAmelCase    (	self						:  int						)					->		List[str]:






				"""simple docstring"""



				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 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				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



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A_       : List[str]         =			logging.get_logger(__name__)

A_       : Tuple         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_       : List[Any]         =			{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

A_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						super().__init__(
						    A__    ,       tokenizer_file=A__    ,       do_lower_case=A__    ,       unk_token=A__    ,       sep_token=A__    ,       pad_token=A__    ,       cls_token=A__    ,       mask_token=A__    ,       tokenize_chinese_chars=A__    ,       strip_accents=A__    ,       **A__    ,       )

						A__							:    Tuple        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
						if (
						    pre_tok_state.get("""lowercase"""    ,       A__			) != do_lower_case
						    or pre_tok_state.get("""strip_accents"""    ,       A__			) != strip_accents
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[self.sep_token_id]
						A__							:    Tuple        =		[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			) * [0] + len(token_ids_a + sep			) * [1]






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



