






"""simple docstring"""




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

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


if is_sentencepiece_available():
      from .tokenization_reformer import ReformerTokenizer
else:
      lowerCamelCase__			=   None


lowerCamelCase__			=   logging.get_logger(__name__)


lowerCamelCase__			=   "▁"

lowerCamelCase__			=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__			=   {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

lowerCamelCase__			=   {
    "google/reformer-crime-and-punishment": 524_288,
}







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  VOCAB_FILES_NAMES
    __lowerCamelCase         =  PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase         =  ["input_ids", "attention_mask"]
    __lowerCamelCase         =  ReformerTokenizer






    def __init__(self						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__="</s>"						,      lowerCamelCase__="<unk>"						,      lowerCamelCase__=[]						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      super().__init__(
          lowerCamelCase__						,      tokenizer_file=lowerCamelCase__						,      eos_token=lowerCamelCase__						,      unk_token=lowerCamelCase__						,      additional_special_tokens=lowerCamelCase__						,      **lowerCamelCase__						,      )

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






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      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
      A__											=							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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__			=   logging.get_logger(__name__)

lowerCamelCase__			=   {
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": (
        "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"
    ),
    "google/electra-base-discriminator": (
        "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"
    ),
    "google/electra-large-discriminator": (
        "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"
    ),
}







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  "electra"






    def __init__(self						,      lowerCamelCase__=3_0_5_2_2						,      lowerCamelCase__=1_2_8						,      lowerCamelCase__=2_5_6						,      lowerCamelCase__=1_2						,      lowerCamelCase__=4						,      lowerCamelCase__=1_0_2_4						,      lowerCamelCase__="gelu"						,      lowerCamelCase__=0.1						,      lowerCamelCase__=0.1						,      lowerCamelCase__=5_1_2						,      lowerCamelCase__=2						,      lowerCamelCase__=0.0_2						,      lowerCamelCase__=1E-12						,      lowerCamelCase__="first"						,      lowerCamelCase__=True						,      lowerCamelCase__="gelu"						,      lowerCamelCase__=0.1						,      lowerCamelCase__=0						,      lowerCamelCase__="absolute"						,      lowerCamelCase__=True						,      lowerCamelCase__=None						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      super().__init__(pad_token_id=lowerCamelCase__						,      **lowerCamelCase__   )

      A__											=							vocab_size
      A__											=							embedding_size
      A__											=							hidden_size
      A__											=							num_hidden_layers
      A__											=							num_attention_heads
      A__											=							intermediate_size
      A__											=							hidden_act
      A__											=							hidden_dropout_prob
      A__											=							attention_probs_dropout_prob
      A__											=							max_position_embeddings
      A__											=							type_vocab_size
      A__											=							initializer_range
      A__											=							layer_norm_eps

      A__											=							summary_type
      A__											=							summary_use_proj
      A__											=							summary_activation
      A__											=							summary_last_dropout
      A__											=							position_embedding_type
      A__											=							use_cache
      A__											=							classifier_dropout







class 					_UpperCamelCase		( __snake_case):






    @property
    def  A    (self   ):


      """simple docstring"""
      if self.task == "multiple-choice":
        A__											=							{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        A__											=							{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
              ("""token_type_ids""", dynamic_axis),
          ]   )




