





"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase__				:Tuple				   =       logging.get_logger(__name__)

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

lowercase__				:Union[str, Any]				   =       {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__				:Optional[int]				   =       {
    'squeezebert/squeezebert-uncased': 5_1_2,
    'squeezebert/squeezebert-mnli': 5_1_2,
    'squeezebert/squeezebert-mnli-headless': 5_1_2,
}


lowercase__				:List[str]				   =       {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Dict         =       VOCAB_FILES_NAMES
      _A     :       Dict         =       PRETRAINED_VOCAB_FILES_MAP
      _A     :       str         =       PRETRAINED_INIT_CONFIGURATION
      _A     :       int         =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A     :       Dict         =       SqueezeBertTokenizer

      def __init__(							self       :      Optional[int]						,				__lowercase       :      Tuple=None						,				__lowercase       :      Union[str, Any]=None						,				__lowercase       :      str=True						,				__lowercase       :      str="[UNK]"						,				__lowercase       :      Any="[SEP]"						,				__lowercase       :      Union[str, Any]="[PAD]"						,				__lowercase       :      List[Any]="[CLS]"						,				__lowercase       :      Tuple="[MASK]"						,				__lowercase       :      int=True						,				__lowercase       :      Optional[Any]=None						,				**__lowercase       :      Dict						,				):


        '''simple docstring'''




        super().__init__(
            __lowercase						,				tokenizer_file=__lowercase						,				do_lower_case=__lowercase						,				unk_token=__lowercase						,				sep_token=__lowercase						,				pad_token=__lowercase						,				cls_token=__lowercase						,				mask_token=__lowercase						,				tokenize_chinese_chars=__lowercase						,				strip_accents=__lowercase						,				**__lowercase						,				)

        __UpperCAmelCase    :						Dict       =						json.loads(self.backend_tokenizer.normalizer.__getstate__() )
        if (
            normalizer_state.get('''lowercase'''						,				__lowercase ) != do_lower_case
            or normalizer_state.get('''strip_accents'''						,				__lowercase ) != strip_accents
            or normalizer_state.get('''handle_chinese_chars'''						,				__lowercase ) != tokenize_chinese_chars
        ):
          __UpperCAmelCase    :						Optional[int]       =						getattr(__lowercase						,				normalizer_state.pop('''type''' ) )
          __UpperCAmelCase    :						Any       =						do_lower_case
          __UpperCAmelCase    :						Optional[Any]       =						strip_accents
          __UpperCAmelCase    :						int       =						tokenize_chinese_chars
          __UpperCAmelCase    :						Dict       =						normalizer_class(**__lowercase )

        __UpperCAmelCase    :						Any       =						do_lower_case

      def       A_					(							self       :      Dict						,				__lowercase       :      Optional[Any]						,				__lowercase       :      Union[str, Any]=None ):


        '''simple docstring'''




        __UpperCAmelCase    :						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       :      Union[str, Any]						,				__lowercase       :      List[int]						,				__lowercase       :      Optional[List[int]] = None ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						[self.sep_token_id]
        __UpperCAmelCase    :						int       =						[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       :      Optional[int]						,				__lowercase       :      str						,				__lowercase       :      Optional[str] = None ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						self._tokenizer.model.save(__lowercase						,				name=__lowercase )
        return tuple(__lowercase )






"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__				:int				   =       logging.get_logger(__name__)

lowercase__				:List[str]				   =       {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[Any]         =       'gpt_neox'

      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      Any=50_432						,				__lowercase       :      int=6_144						,				__lowercase       :      List[str]=44						,				__lowercase       :      List[Any]=64						,				__lowercase       :      int=24_576						,				__lowercase       :      List[str]="gelu"						,				__lowercase       :      Optional[int]=0.2_5						,				__lowercase       :      Any=10_000						,				__lowercase       :      Union[str, Any]=0.0						,				__lowercase       :      List[str]=0.0						,				__lowercase       :      Union[str, Any]=0.1						,				__lowercase       :      Optional[int]=2_048						,				__lowercase       :      Union[str, Any]=0.0_2						,				__lowercase       :      Optional[int]=1e-5						,				__lowercase       :      str=True						,				__lowercase       :      str=0						,				__lowercase       :      Tuple=2						,				__lowercase       :      Tuple=False						,				__lowercase       :      int=True						,				__lowercase       :      Any=None						,				**__lowercase       :      List[str]						,				):


        '''simple docstring'''




        super().__init__(bos_token_id=__lowercase						,				eos_token_id=__lowercase						,				**__lowercase )
        __UpperCAmelCase    :						int       =						vocab_size
        __UpperCAmelCase    :						Union[str, Any]       =						max_position_embeddings
        __UpperCAmelCase    :						List[str]       =						hidden_size
        __UpperCAmelCase    :						Tuple       =						num_hidden_layers
        __UpperCAmelCase    :						Any       =						num_attention_heads
        __UpperCAmelCase    :						Union[str, Any]       =						intermediate_size
        __UpperCAmelCase    :						str       =						hidden_act
        __UpperCAmelCase    :						str       =						rotary_pct
        __UpperCAmelCase    :						str       =						rotary_emb_base
        __UpperCAmelCase    :						Dict       =						attention_dropout
        __UpperCAmelCase    :						str       =						hidden_dropout
        __UpperCAmelCase    :						Any       =						classifier_dropout
        __UpperCAmelCase    :						Tuple       =						initializer_range
        __UpperCAmelCase    :						Any       =						layer_norm_eps
        __UpperCAmelCase    :						Optional[int]       =						use_cache
        __UpperCAmelCase    :						List[Any]       =						tie_word_embeddings
        __UpperCAmelCase    :						Any       =						use_parallel_residual
        __UpperCAmelCase    :						List[str]       =						rope_scaling
        self._rope_scaling_validation()

        if self.hidden_size % self.num_attention_heads != 0:
          raise ValueError(
              '''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )







      def       A_					(							self       :      int ):


        '''simple docstring'''




        if self.rope_scaling is None:
          return

        if not isinstance(self.rope_scaling						,				__lowercase ) or len(self.rope_scaling ) != 2:
          raise ValueError(
              '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
              f'''got {self.rope_scaling}''' )
        __UpperCAmelCase    :						Union[str, Any]       =						self.rope_scaling.get('''type'''						,				__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self.rope_scaling.get('''factor'''						,				__lowercase )
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
          raise ValueError(
              f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
        if rope_scaling_factor is None or not isinstance(__lowercase						,				__lowercase ) or rope_scaling_factor <= 1.0:
          raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )