


"""simple docstring"""







import functools
import operator

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


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       """sew-d"""






    def __init__(					self				:    Optional[int]				, __UpperCAmelCase				:    Any=32				, __UpperCAmelCase				:    Union[str, Any]=768				, __UpperCAmelCase				:    Tuple=12				, __UpperCAmelCase				:    Dict=12				, __UpperCAmelCase				:    str=3072				, __UpperCAmelCase				:    Tuple=2				, __UpperCAmelCase				:    Optional[int]=512				, __UpperCAmelCase				:    int=256				, __UpperCAmelCase				:    Union[str, Any]=True				, __UpperCAmelCase				:    int=True				, __UpperCAmelCase				:    List[Any]=("p2c", "c2p")				, __UpperCAmelCase				:    Any="layer_norm"				, __UpperCAmelCase				:    Optional[Any]="gelu_python"				, __UpperCAmelCase				:    Optional[int]=0.1				, __UpperCAmelCase				:    Optional[int]=0.1				, __UpperCAmelCase				:    Optional[Any]=0.1				, __UpperCAmelCase				:    Dict=0.0				, __UpperCAmelCase				:    str=0.1				, __UpperCAmelCase				:    Dict=0.02				, __UpperCAmelCase				:    List[Any]=1e-7				, __UpperCAmelCase				:    Dict=1e-5				, __UpperCAmelCase				:    Dict="group"				, __UpperCAmelCase				:    Optional[Any]="gelu"				, __UpperCAmelCase				:    Optional[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				, __UpperCAmelCase				:    Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				, __UpperCAmelCase				:    Optional[int]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				, __UpperCAmelCase				:    Union[str, Any]=False				, __UpperCAmelCase				:    Union[str, Any]=128				, __UpperCAmelCase				:    Any=16				, __UpperCAmelCase				:    List[Any]=True				, __UpperCAmelCase				:    int=0.05				, __UpperCAmelCase				:    Any=10				, __UpperCAmelCase				:    Any=2				, __UpperCAmelCase				:    int=0.0				, __UpperCAmelCase				:    Optional[Any]=10				, __UpperCAmelCase				:    Optional[Any]=0				, __UpperCAmelCase				:    Optional[Any]="mean"				, __UpperCAmelCase				:    Dict=False				, __UpperCAmelCase				:    Dict=False				, __UpperCAmelCase				:    List[Any]=256				, __UpperCAmelCase				:    Optional[Any]=0				, __UpperCAmelCase				:    List[str]=1				, __UpperCAmelCase				:    List[Any]=2				, **__UpperCAmelCase				:    str				, ):
      super().__init__(**__UpperCAmelCase				, pad_token_id=__UpperCAmelCase				, bos_token_id=__UpperCAmelCase				, eos_token_id=__UpperCAmelCase)
      a						:		Tuple      							=			hidden_size
      a						:		Optional[Any]      							=			feat_extract_norm
      a						:		List[str]      							=			feat_extract_activation
      a						:		str      							=			list(__UpperCAmelCase)
      a						:		Dict      							=			list(__UpperCAmelCase)
      a						:		str      							=			list(__UpperCAmelCase)
      a						:		int      							=			conv_bias
      a						:		Optional[int]      							=			num_conv_pos_embeddings
      a						:		Union[str, Any]      							=			num_conv_pos_embedding_groups
      a						:		str      							=			len(self.conv_dim)
      a						:		int      							=			num_hidden_layers
      a						:		Dict      							=			intermediate_size
      a						:		Optional[Any]      							=			squeeze_factor
      a						:		Union[str, Any]      							=			max_position_embeddings
      a						:		Any      							=			position_buckets
      a						:		Tuple      							=			share_att_key
      a						:		str      							=			relative_attention
      a						:		Tuple      							=			norm_rel_ebd
      a						:		Tuple      							=			list(__UpperCAmelCase)
      a						:		Tuple      							=			hidden_act
      a						:		List[str]      							=			num_attention_heads
      a						:		Optional[int]      							=			hidden_dropout
      a						:		int      							=			attention_dropout
      a						:		Dict      							=			activation_dropout
      a						:		str      							=			feat_proj_dropout
      a						:		Any      							=			final_dropout
      a						:		Optional[Any]      							=			layer_norm_eps
      a						:		List[Any]      							=			feature_layer_norm_eps
      a						:		str      							=			initializer_range
      a						:		Dict      							=			vocab_size

      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)`,"
            f'''but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)'''
            f'''= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')

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

      # ctc loss
      a						:		Tuple      							=			ctc_loss_reduction
      a						:		str      							=			ctc_zero_infinity

      # sequence classification
      a						:		int      							=			use_weighted_layer_sum
      a						:		Any      							=			classifier_proj_size






    @property
    def 	__snake_case							(					self				:    Optional[Any]):
      return functools.reduce(operator.mul				, self.conv_stride				, 1)












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()

