



"""simple docstring"""

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


lowercase__       =	logging.get_logger(__name__)

lowercase__       =	{
    "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 SCREAMING_SNAKE_CASE__       (		lowercase_   ):
						_lowerCAmelCase    						=       "gpt_neox"



						def __init__(self    ,  _lowercase=50432    ,  _lowercase=6144    ,  _lowercase=44    ,  _lowercase=64    ,  _lowercase=24576    ,  _lowercase="gelu"    ,  _lowercase=0.25    ,  _lowercase=10000    ,  _lowercase=0.0    ,  _lowercase=0.0    ,  _lowercase=0.1    ,  _lowercase=2048    ,  _lowercase=0.02    ,  _lowercase=1e-5    ,  _lowercase=True    ,  _lowercase=0    ,  _lowercase=2    ,  _lowercase=False    ,  _lowercase=True    ,  _lowercase=None    ,  **_lowercase    ,  ):





													'''simple docstring'''

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





													'''simple docstring'''

													if self.rope_scaling is None:
																				return

													if not isinstance(self.rope_scaling    ,  a__				) 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}'''				)
													__a		:		Tuple						=							self.rope_scaling.get("""type"""    ,  a__				)
													__a		:		int						=							self.rope_scaling.get("""factor"""    ,  a__				)
													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(a__    ,  a__				) or rope_scaling_factor <= 1.0:
																				raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''				)









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate




@dataclass(frozen=lowercase_)
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			field(default="audio-classification"						,     metadata={"include_in_asdict_even_if_is_default": True})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"audio": Audio()})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"labels": ClassLabel})
       lowerCamelCase				:					str								=			"audio"
       lowerCamelCase				:					str								=			"labels"



       def        __lowercase    (				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            if self.label_column not in features:
                 raise ValueError(F'''Column {self.label_column} is not present in features.'''     )
            if not isinstance(features[self.label_column]      ,    a__     ):
                 raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''     )
            __snake_case      :Any 							=    copy.deepcopy(self     )
            __snake_case      :List[Any] 							=    self.label_schema.copy()
            __snake_case      :List[Any] 							=    features[self.label_column]
            __snake_case      :Optional[Any] 							=    label_schema
            return task_template



       @property
       def        __lowercase    (				self     )			->   Dict[str, str]:






            '''simple docstring'''





            return {
                self.audio_column: "audio",
                self.label_column: "labels",
            }


