





import math

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


__UpperCAmelCase							=   logging.get_logger(__name__)

__UpperCAmelCase							=   {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class 		__a				(						__SCREAMING_SNAKE_CASE   ):
       __snake_case    : Optional[int]   =		"""data2vec-audio"""



       def __init__(  self						: str       ,      UpperCAmelCase						: Dict=32       ,      UpperCAmelCase						: int=7_68       ,      UpperCAmelCase						: int=12       ,      UpperCAmelCase						: str=12       ,      UpperCAmelCase						: str=30_72       ,      UpperCAmelCase						: List[Any]="gelu"       ,      UpperCAmelCase						: Any=0.1       ,      UpperCAmelCase						: Tuple=0.1       ,      UpperCAmelCase						: List[Any]=0.1       ,      UpperCAmelCase						: Optional[int]=0.0       ,      UpperCAmelCase						: Dict=0.1       ,      UpperCAmelCase						: Optional[int]=0.1       ,      UpperCAmelCase						: Tuple=0.02       ,      UpperCAmelCase						: Any=1e-5       ,      UpperCAmelCase						: Any="gelu"       ,      UpperCAmelCase						: Optional[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)       ,      UpperCAmelCase						: Dict=(5, 2, 2, 2, 2, 2, 2)       ,      UpperCAmelCase						: List[str]=(10, 3, 3, 3, 3, 2, 2)       ,      UpperCAmelCase						: Dict=False       ,      UpperCAmelCase						: Any=16       ,      UpperCAmelCase						: Union[str, Any]=19       ,      UpperCAmelCase						: str=5       ,      UpperCAmelCase						: Optional[int]=0.05       ,      UpperCAmelCase						: Dict=10       ,      UpperCAmelCase						: Tuple=2       ,      UpperCAmelCase						: Tuple=0.0       ,      UpperCAmelCase						: List[str]=10       ,      UpperCAmelCase						: Optional[Any]=0       ,      UpperCAmelCase						: Union[str, Any]="sum"       ,      UpperCAmelCase						: Optional[Any]=False       ,      UpperCAmelCase						: Union[str, Any]=False       ,      UpperCAmelCase						: Optional[Any]=2_56       ,      UpperCAmelCase						: Optional[int]=(5_12, 5_12, 5_12, 5_12, 15_00)       ,      UpperCAmelCase						: int=(5, 3, 3, 1, 1)       ,      UpperCAmelCase						: str=(1, 2, 3, 1, 1)       ,      UpperCAmelCase						: List[str]=5_12       ,      UpperCAmelCase						: Optional[Any]=0       ,      UpperCAmelCase						: Dict=1       ,      UpperCAmelCase						: Optional[Any]=2       ,      UpperCAmelCase						: int=False       ,      UpperCAmelCase						: Optional[int]=3       ,      UpperCAmelCase						: Optional[int]=2       ,      UpperCAmelCase						: List[Any]=3       ,      UpperCAmelCase						: Dict=None       ,      **UpperCAmelCase						: Dict       ,      ):
           super().__init__(**A_       ,      pad_token_id=A_       ,      bos_token_id=A_       ,      eos_token_id=A_      )
           lowerCAmelCase_       :						Tuple							= hidden_size
           lowerCAmelCase_       :						List[str]							= feat_extract_activation
           lowerCAmelCase_       :						Optional[Any]							= list(A_      )
           lowerCAmelCase_       :						Optional[int]							= list(A_      )
           lowerCAmelCase_       :						List[Any]							= list(A_      )
           lowerCAmelCase_       :						List[Any]							= conv_bias
           lowerCAmelCase_       :						int							= num_conv_pos_embeddings
           lowerCAmelCase_       :						str							= num_conv_pos_embedding_groups
           lowerCAmelCase_       :						List[Any]							= conv_pos_kernel_size
           lowerCAmelCase_       :						Optional[Any]							= len(self.conv_dim      )
           lowerCAmelCase_       :						Dict							= num_hidden_layers
           lowerCAmelCase_       :						Any							= intermediate_size
           lowerCAmelCase_       :						Any							= hidden_act
           lowerCAmelCase_       :						Optional[int]							= num_attention_heads
           lowerCAmelCase_       :						Union[str, Any]							= hidden_dropout
           lowerCAmelCase_       :						Union[str, Any]							= attention_dropout
           lowerCAmelCase_       :						Union[str, Any]							= activation_dropout
           lowerCAmelCase_       :						int							= feat_proj_dropout
           lowerCAmelCase_       :						Tuple							= final_dropout
           lowerCAmelCase_       :						Optional[Any]							= layerdrop
           lowerCAmelCase_       :						Union[str, Any]							= layer_norm_eps
           lowerCAmelCase_       :						int							= initializer_range
           lowerCAmelCase_       :						Dict							= vocab_size
           lowerCAmelCase_       :						Dict							= use_weighted_layer_sum

           if (
               (len(self.conv_stride      ) != self.num_feat_extract_layers)
               or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
               or (len(self.conv_dim      ) != self.num_feat_extract_layers)
           ):
               raise ValueError(
                   """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                   """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                   F' {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,'
                   F' `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'      )

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           lowerCAmelCase_       :						str							= mask_time_prob
           lowerCAmelCase_       :						List[str]							= mask_time_length
           lowerCAmelCase_       :						Union[str, Any]							= mask_time_min_masks
           lowerCAmelCase_       :						Tuple							= mask_feature_prob
           lowerCAmelCase_       :						Optional[int]							= mask_feature_length
           lowerCAmelCase_       :						Any							= mask_feature_min_masks

           # ctc loss
           lowerCAmelCase_       :						Tuple							= ctc_loss_reduction
           lowerCAmelCase_       :						Any							= ctc_zero_infinity

           # adapter
           lowerCAmelCase_       :						List[Any]							= add_adapter
           lowerCAmelCase_       :						Any							= adapter_kernel_size
           lowerCAmelCase_       :						List[Any]							= adapter_stride
           lowerCAmelCase_       :						Optional[int]							= num_adapter_layers
           lowerCAmelCase_       :						Optional[Any]							= output_hidden_size or hidden_size

           # SequenceClassification-specific parameter. Feel free to ignore for other classes.
           lowerCAmelCase_       :						Union[str, Any]							= classifier_proj_size

           # XVector-specific parameters. Feel free to ignore for other classes.
           lowerCAmelCase_       :						str							= list(A_      )
           lowerCAmelCase_       :						Any							= list(A_      )
           lowerCAmelCase_       :						Optional[int]							= list(A_      )
           lowerCAmelCase_       :						Union[str, Any]							= xvector_output_dim



       @property
       def 			A				(  self						: List[Any]      ):
           return math.prod(self.conv_stride      )









class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			name
        SCREAMING_SNAKE_CASE__											=			value
        SCREAMING_SNAKE_CASE__											=			weight



    def __repr__(       self     ):





        '''simple docstring'''

        return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.name



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.weight




    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value / self.weight





def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[]
    for i in range(len(lowerCAmelCase_	)	):
        menu.append(Things(name[i] ,							value[i] ,							weight[i]	)	)
    return menu


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			sorted(lowerCAmelCase_ ,							key=lowerCAmelCase_ ,							reverse=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			0.0, 0.0
    for i in range(len(lowerCAmelCase_	)	):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]	)
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)


def 							__snake_case							(    )				->						str:
    pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
