




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     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Dict    		=   'data2vec-audio'
      def __init__(				self: Any    ,       UpperCamelCase_: List[str]=32    ,       UpperCamelCase_: Optional[Any]=7_68    ,       UpperCamelCase_: str=12    ,       UpperCamelCase_: int=12    ,       UpperCamelCase_: Optional[Any]=30_72    ,       UpperCamelCase_: str="gelu"    ,       UpperCamelCase_: Any=0.1    ,       UpperCamelCase_: Dict=0.1    ,       UpperCamelCase_: List[Any]=0.1    ,       UpperCamelCase_: List[str]=0.0    ,       UpperCamelCase_: str=0.1    ,       UpperCamelCase_: Tuple=0.1    ,       UpperCamelCase_: List[str]=0.02    ,       UpperCamelCase_: int=1E-5    ,       UpperCamelCase_: Union[str, Any]="gelu"    ,       UpperCamelCase_: Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)    ,       UpperCamelCase_: int=(5, 2, 2, 2, 2, 2, 2)    ,       UpperCamelCase_: int=(10, 3, 3, 3, 3, 2, 2)    ,       UpperCamelCase_: Any=False    ,       UpperCamelCase_: Tuple=16    ,       UpperCamelCase_: Union[str, Any]=19    ,       UpperCamelCase_: List[str]=5    ,       UpperCamelCase_: Optional[Any]=0.05    ,       UpperCamelCase_: int=10    ,       UpperCamelCase_: Union[str, Any]=2    ,       UpperCamelCase_: Dict=0.0    ,       UpperCamelCase_: Dict=10    ,       UpperCamelCase_: List[Any]=0    ,       UpperCamelCase_: Tuple="sum"    ,       UpperCamelCase_: Tuple=False    ,       UpperCamelCase_: Optional[int]=False    ,       UpperCamelCase_: int=2_56    ,       UpperCamelCase_: Dict=(5_12, 5_12, 5_12, 5_12, 15_00)    ,       UpperCamelCase_: List[str]=(5, 3, 3, 1, 1)    ,       UpperCamelCase_: Any=(1, 2, 3, 1, 1)    ,       UpperCamelCase_: Dict=5_12    ,       UpperCamelCase_: Optional[int]=0    ,       UpperCamelCase_: str=1    ,       UpperCamelCase_: Tuple=2    ,       UpperCamelCase_: str=False    ,       UpperCamelCase_: int=3    ,       UpperCamelCase_: Any=2    ,       UpperCamelCase_: Optional[int]=3    ,       UpperCamelCase_: Tuple=None    ,       **UpperCamelCase_: int    ,       ):
             super().__init__(**UpperCamelCase_    ,       pad_token_id=UpperCamelCase_    ,       bos_token_id=UpperCamelCase_    ,       eos_token_id=UpperCamelCase_   )
             __lowerCamelCase   =						hidden_size
             __lowerCamelCase   =						feat_extract_activation
             __lowerCamelCase   =						list(UpperCamelCase_   )
             __lowerCamelCase   =						list(UpperCamelCase_   )
             __lowerCamelCase   =						list(UpperCamelCase_   )
             __lowerCamelCase   =						conv_bias
             __lowerCamelCase   =						num_conv_pos_embeddings
             __lowerCamelCase   =						num_conv_pos_embedding_groups
             __lowerCamelCase   =						conv_pos_kernel_size
             __lowerCamelCase   =						len(self.conv_dim   )
             __lowerCamelCase   =						num_hidden_layers
             __lowerCamelCase   =						intermediate_size
             __lowerCamelCase   =						hidden_act
             __lowerCamelCase   =						num_attention_heads
             __lowerCamelCase   =						hidden_dropout
             __lowerCamelCase   =						attention_dropout
             __lowerCamelCase   =						activation_dropout
             __lowerCamelCase   =						feat_proj_dropout
             __lowerCamelCase   =						final_dropout
             __lowerCamelCase   =						layerdrop
             __lowerCamelCase   =						layer_norm_eps
             __lowerCamelCase   =						initializer_range
             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						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   =						mask_time_prob
             __lowerCamelCase   =						mask_time_length
             __lowerCamelCase   =						mask_time_min_masks
             __lowerCamelCase   =						mask_feature_prob
             __lowerCamelCase   =						mask_feature_length
             __lowerCamelCase   =						mask_feature_min_masks

             # ctc loss
             __lowerCamelCase   =						ctc_loss_reduction
             __lowerCamelCase   =						ctc_zero_infinity

             # adapter
             __lowerCamelCase   =						add_adapter
             __lowerCamelCase   =						adapter_kernel_size
             __lowerCamelCase   =						adapter_stride
             __lowerCamelCase   =						num_adapter_layers
             __lowerCamelCase   =						output_hidden_size or hidden_size

             # SequenceClassification-specific parameter. Feel free to ignore for other classes.
             __lowerCamelCase   =						classifier_proj_size

             # XVector-specific parameters. Feel free to ignore for other classes.
             __lowerCamelCase   =						list(UpperCamelCase_   )
             __lowerCamelCase   =						list(UpperCamelCase_   )
             __lowerCamelCase   =						list(UpperCamelCase_   )
             __lowerCamelCase   =						xvector_output_dim




      @property
      def 			lowerCAmelCase__						(				self: str   ):
             return math.prod(self.conv_stride   )







from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	int
      UpperCAmelCase__      :	TreeNode | None    		=   None
      UpperCAmelCase__      :	TreeNode | None    		=   None



UpperCAmelCase_									=	namedtuple('CoinsDistribResult', 'moves excess')




def 					lowerCamelCase__       (						A__       :					TreeNode | None	):





       '''simple docstring'''



       if root is None:
              return 0

       # Validation
       def count_nodes(A__       :					TreeNode | None	) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left	) + count_nodes(node.right	) + 1

       def count_coins(A__       :					TreeNode | None	) -> int:
              if node is None:
                     return 0

              return count_coins(node.left	) + count_coins(node.right	) + node.data

       if count_nodes(A__	) != count_coins(A__	):
              raise ValueError("""The nodes number should be same as the number of coins"""	)

       # Main calculation
       def get_distrib(A__       :					TreeNode | None	) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0  ,			1	)

              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.left	)
              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.right	)

              __lowerCamelCase   =						1 - left_distrib_excess
              __lowerCamelCase   =						1 - right_distrib_excess

              __lowerCamelCase   =						(
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(A__	)
                  + abs(A__	)
              )
              __lowerCamelCase   =						node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(A__  ,			A__	)

       return get_distrib(A__	)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

