
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :			str					=							logging.get_logger(__name__)

_a      :			Tuple					=							{
    """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      _UpperCAmelCase				(					_snake_case):
    __lowercase							: int  =					"""gpt_neox"""
    def __init__(    self						,    snake_case_=5_04_32						,    snake_case_=61_44						,    snake_case_=44						,    snake_case_=64						,    snake_case_=2_45_76						,    snake_case_="gelu"						,    snake_case_=0.25						,    snake_case_=1_00_00						,    snake_case_=0.0						,    snake_case_=0.0						,    snake_case_=0.1						,    snake_case_=20_48						,    snake_case_=0.02						,    snake_case_=1E-5						,    snake_case_=True						,    snake_case_=0						,    snake_case_=2						,    snake_case_=False						,    snake_case_=True						,    snake_case_=None						,    **snake_case_						,    ):
      super().__init__(bos_token_id=snake_case_						,    eos_token_id=snake_case_						,    **snake_case_     )
      _snake_case     :						str						=   vocab_size
      _snake_case     :						Tuple						=   max_position_embeddings
      _snake_case     :						str						=   hidden_size
      _snake_case     :						Optional[int]						=   num_hidden_layers
      _snake_case     :						List[str]						=   num_attention_heads
      _snake_case     :						Tuple						=   intermediate_size
      _snake_case     :						Tuple						=   hidden_act
      _snake_case     :						Dict						=   rotary_pct
      _snake_case     :						List[Any]						=   rotary_emb_base
      _snake_case     :						Union[str, Any]						=   attention_dropout
      _snake_case     :						int						=   hidden_dropout
      _snake_case     :						List[Any]						=   classifier_dropout
      _snake_case     :						Any						=   initializer_range
      _snake_case     :						int						=   layer_norm_eps
      _snake_case     :						List[Any]						=   use_cache
      _snake_case     :						int						=   tie_word_embeddings
      _snake_case     :						Any						=   use_parallel_residual
      _snake_case     :						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 					lowerCamelCase__	(    self     ):
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling						,    snake_case_     ) 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}'     )
      _snake_case     :						List[Any]						=   self.rope_scaling.get("type"						,    snake_case_     )
      _snake_case     :						List[str]						=   self.rope_scaling.get("factor"						,    snake_case_     )
      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(snake_case_						,    snake_case_     ) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'     )



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_a      :			List[Any]					=							{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			List[Any]					=							["""BartphoTokenizer"""]

if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_bartpho import BartphoTokenizer

else:
    import sys

    _a      :			Optional[int]					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

