




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

from ..utils import logging


lowerCamelCase__					:  int      							=				logging.get_logger(__name__)

def    UpperCAmelCase_				( __UpperCAmelCase		:      int=None     ,     __UpperCAmelCase		:      Tuple=None				)   ->					str:
      return field(default_factory=lambda: default     ,     metadata=__UpperCAmelCase				)





@dataclass
class 			lowerCamelCase_				:


 '''simple docstring'''







 lowercase_							       =       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"
         )
     }			,  )

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

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

 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}			,  )
 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}			,  )
 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Use FP16 to accelerate inference."}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Benchmark training of model"}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Verbose memory tracing"}   )
 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}			,  )
 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={
         "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
     }			,  )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Trace memory line by line"}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Save result to a CSV file"}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Save all print statements in a log file"}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "Whether to print environment information"}   )
 lowercase_							       =       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."
         )
     }			,  )
 lowercase_							       =       field(
     default=F'''inference_time_{round(time()   )}.csv'''			,  metadata={"help": "CSV filename used if saving time results to csv."}			,  )
 lowercase_							       =       field(
     default=F'''inference_memory_{round(time()   )}.csv'''			,  metadata={"help": "CSV filename used if saving memory results to csv."}			,  )
 lowercase_							       =       field(
     default=F'''train_time_{round(time()   )}.csv'''			,  metadata={"help": "CSV filename used if saving time results to csv for training."}			,  )
 lowercase_							       =       field(
     default=F'''train_memory_{round(time()   )}.csv'''			,  metadata={"help": "CSV filename used if saving memory results to csv for training."}			,  )
 lowercase_							       =       field(
     default=F'''env_info_{round(time()   )}.csv'''			,  metadata={"help": "CSV filename used if saving environment information."}			,  )
 lowercase_							       =       field(
     default=F'''log_{round(time()   )}.csv'''			,  metadata={"help": "Log filename used if print statements are saved in log."}			,  )
 lowercase_							       =       field(default=3			,  metadata={"help": "Times an experiment will be run."}   )
 lowercase_							       =       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  lowerCAmelCase_    (				self    :						List[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.'							,							_lowerCAmelCase							,							)



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



 @property
 def  lowerCAmelCase_    (				self    :						Dict		):
       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  lowerCAmelCase_    (				self    :						Any		):
       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 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_fnet import FNetTokenizer
else:
   lowerCamelCase__					:  Optional[Any]      							=				None

lowerCamelCase__					:  List[str]      							=				logging.get_logger(__name__)
lowerCamelCase__					:  List[str]      							=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					:  List[str]      							=				{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__					:  Optional[Any]      							=				{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					:  List[Any]      							=				'▁'





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       VOCAB_FILES_NAMES
 lowercase_							       =       PRETRAINED_VOCAB_FILES_MAP
 lowercase_							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_							       =       ["input_ids", "token_type_ids"]
 lowercase_							       =       FNetTokenizer



 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						List[str]=False							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Tuple=True							,							_lowerCAmelCase    :						List[Any]="<unk>"							,							_lowerCAmelCase    :						Optional[Any]="[SEP]"							,							_lowerCAmelCase    :						Optional[Any]="<pad>"							,							_lowerCAmelCase    :						Optional[int]="[CLS]"							,							_lowerCAmelCase    :						Optional[Any]="[MASK]"							,							**_lowerCAmelCase    :						Any							,							):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       SCREAMING_SNAKE_CASE_      		=     (
           AddedToken(_lowerCAmelCase							,							lstrip=_lowerCAmelCase							,							rstrip=_lowerCAmelCase							,							normalized=_lowerCAmelCase		)
           if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		)
           else mask_token
       )

       super().__init__(
           _lowerCAmelCase							,							tokenizer_file=_lowerCAmelCase							,							do_lower_case=_lowerCAmelCase							,							remove_space=_lowerCAmelCase							,							keep_accents=_lowerCAmelCase							,							unk_token=_lowerCAmelCase							,							sep_token=_lowerCAmelCase							,							pad_token=_lowerCAmelCase							,							cls_token=_lowerCAmelCase							,							mask_token=_lowerCAmelCase							,							**_lowerCAmelCase							,							)

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



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_SNAKE_CASE_      		=     [self.cls_token_id]
       if token_ids_a is None:
             return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_SNAKE_CASE_      		=     [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  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str] = None		):
       if not os.path.isdir(_lowerCAmelCase		):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
             return
       SCREAMING_SNAKE_CASE_      		=     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,)