"""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


_a      :		List[Any]           =   logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]=None			,_lowerCamelCase						:       Union[str, Any]=None	)       ->      Dict:
  return field(default_factory=lambda: default			,metadata=_lowerCamelCase	)






@dataclass
class 						__A							:
     _UpperCamelCase							:			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"
             )
         }    ,	)

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

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

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






     def     __A  (		self  ):
       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."""				,     a__				,     )






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






     @property
     def     __A  (		self  ):
       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     __A  (		self  ):
       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





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_a      :		List[Any]           =   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_a      :		Union[str, Any]           =   {
    'vocab_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'
        ),
        'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt',
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-generator': (
            'https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'
        ),
    },
}

_a      :		Optional[Any]           =   {
    'google/electra-small-generator': 512,
    'google/electra-base-generator': 512,
    'google/electra-large-generator': 512,
    'google/electra-small-discriminator': 512,
    'google/electra-base-discriminator': 512,
    'google/electra-large-discriminator': 512,
}

_a      :		Any           =   {
    'google/electra-small-generator': {'do_lower_case': True},
    'google/electra-base-generator': {'do_lower_case': True},
    'google/electra-large-generator': {'do_lower_case': True},
    'google/electra-small-discriminator': {'do_lower_case': True},
    'google/electra-base-discriminator': {'do_lower_case': True},
    'google/electra-large-discriminator': {'do_lower_case': True},
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Tuple							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_INIT_CONFIGURATION
     _UpperCamelCase							:			Union[str, Any]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			Optional[Any]							=		ElectraTokenizer






     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__				,     )

       _lowerCAmelCase						:       int  			=     json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
       if (
           normalizer_state.get("""lowercase"""				,     a__  ) != do_lower_case
           or normalizer_state.get("""strip_accents"""				,     a__  ) != strip_accents
           or normalizer_state.get("""handle_chinese_chars"""				,     a__  ) != tokenize_chinese_chars
       ):
         _lowerCAmelCase						:       Dict  			=     getattr(a__				,     normalizer_state.pop("""type"""  )  )
         _lowerCAmelCase						:       int  			=     do_lower_case
         _lowerCAmelCase						:       str  			=     strip_accents
         _lowerCAmelCase						:       Dict  			=     tokenize_chinese_chars
         _lowerCAmelCase						:       str  			=     normalizer_class(**a__  )

       _lowerCAmelCase						:       List[str]  			=     do_lower_case






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       int  			=     [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  ):
       _lowerCAmelCase						:       List[str]  			=     [self.sep_token_id]
       _lowerCAmelCase						:       Any  			=     [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  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self._tokenizer.model.save(a__				,     name=a__  )
       return tuple(a__  )




