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


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""open-llama"""



      def __init__(						self    ,			lowercase=10_00_00    ,			lowercase=40_96    ,			lowercase=1_10_08    ,			lowercase=32    ,			lowercase=32    ,			lowercase="silu"    ,			lowercase=20_48    ,			lowercase=0.02    ,			lowercase=1e-6    ,			lowercase=True    ,			lowercase=0    ,			lowercase=1    ,			lowercase=2    ,			lowercase=False    ,			lowercase=True    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=True    ,			lowercase=True    ,			lowercase=None    ,			**lowercase    ,			)	->			List[str]:





        '''simple docstring'''





        a__:     List[Any]										=	vocab_size
        a__:     Any										=	max_position_embeddings
        a__:     int										=	hidden_size
        a__:     str										=	intermediate_size
        a__:     Union[str, Any]										=	num_hidden_layers
        a__:     List[Any]										=	num_attention_heads
        a__:     Dict										=	hidden_act
        a__:     List[Any]										=	initializer_range
        a__:     Optional[int]										=	rms_norm_eps
        a__:     Optional[Any]										=	use_cache
        a__:     Any										=	kwargs.pop(
            'use_memorry_efficient_attention'    ,			lowercase)
        a__:     List[Any]										=	hidden_dropout_prob
        a__:     str										=	attention_dropout_prob
        a__:     int										=	use_stable_embedding
        a__:     Union[str, Any]										=	shared_input_output_embedding
        a__:     int										=	rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=lowercase    ,			bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			tie_word_embeddings=lowercase    ,			**lowercase    ,			)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''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}')
        a__:     Optional[int]										=	self.rope_scaling.get('type'    ,			lowercase)
        a__:     Optional[int]										=	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}')



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


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'MIT/ast-finetuned-audioset-10-10-0.4593': (
        'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
    ),
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""audio-spectrogram-transformer"""



      def __init__(						self    ,			lowercase=7_68    ,			lowercase=12    ,			lowercase=12    ,			lowercase=30_72    ,			lowercase="gelu"    ,			lowercase=0.0    ,			lowercase=0.0    ,			lowercase=0.02    ,			lowercase=1e-12    ,			lowercase=16    ,			lowercase=True    ,			lowercase=10    ,			lowercase=10    ,			lowercase=10_24    ,			lowercase=1_28    ,			**lowercase    ,			)	->			str:





        '''simple docstring'''





        super().__init__(**lowercase)

        a__:     Any										=	hidden_size
        a__:     int										=	num_hidden_layers
        a__:     Union[str, Any]										=	num_attention_heads
        a__:     Any										=	intermediate_size
        a__:     Union[str, Any]										=	hidden_act
        a__:     int										=	hidden_dropout_prob
        a__:     str										=	attention_probs_dropout_prob
        a__:     str										=	initializer_range
        a__:     Tuple										=	layer_norm_eps
        a__:     Any										=	patch_size
        a__:     int										=	qkv_bias
        a__:     Optional[Any]										=	frequency_stride
        a__:     int										=	time_stride
        a__:     List[str]										=	max_length
        a__:     Tuple										=	num_mel_bins


