from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass
@dataclass
class 							a_					:






 '''simple docstring'''






 UpperCAmelCase_      					=   42
 UpperCAmelCase_      					=   None
 UpperCAmelCase_      					=   None






def  __lowerCamelCase						(		):
       return TreeNode(3      ,    TreeNode(9      )      ,    TreeNode(2_0      ,    TreeNode(1_5      )      ,    TreeNode(7      )      )      )






def  __lowerCamelCase						(		lowerCAmelCase__      ):




       def depth_first_search(
           lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ) -> None:
              if not root:
                     return

              if depth == len(UpperCAmelCase_      ):
                     right_view.append(root.val      )

              depth_first_search(root.right      ,    depth + 1      ,    UpperCAmelCase_      )
              depth_first_search(root.left      ,    depth + 1      ,    UpperCAmelCase_      )

       lowerCAmelCase__	      =   []
       if not root:
              return right_view

       depth_first_search(UpperCAmelCase_      ,    0      ,    UpperCAmelCase_      )
       return right_view






def  __lowerCamelCase						(		lowerCAmelCase__      ):




       def depth_first_search(
           lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ) -> None:
              if not root:
                     return

              if depth == len(UpperCAmelCase_      ):
                     left_view.append(root.val      )

              depth_first_search(root.left      ,    depth + 1      ,    UpperCAmelCase_      )
              depth_first_search(root.right      ,    depth + 1      ,    UpperCAmelCase_      )

       lowerCAmelCase__	      =   []
       if not root:
              return left_view

       depth_first_search(UpperCAmelCase_      ,    0      ,    UpperCAmelCase_      )
       return left_view






def  __lowerCamelCase						(		lowerCAmelCase__      ):




       def breadth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ) -> None:
              lowerCAmelCase__	      =   [(root, 0)]
              lowerCAmelCase__	      =   defaultdict(UpperCAmelCase_      )

              while queue:
                     lowerCAmelCase__	      =   queue.pop(0      )
                     lowerCAmelCase__      ,   lowerCAmelCase__	      =   first

                     lookup[hd].append(node.val      )

                     if node.left:
                            queue.append((node.left, hd - 1)      )
                     if node.right:
                            queue.append((node.right, hd + 1)      )

              for pair in sorted(lookup.items()      ,    key=lambda lowerCAmelCase__      : each[0]      ):
                     top_view.append(pair[1][0]      )

       lowerCAmelCase__	      =   []
       if not root:
              return top_view

       breadth_first_search(UpperCAmelCase_      ,    UpperCAmelCase_      )
       return top_view






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       from collections import defaultdict

       def breadth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ) -> None:
              lowerCAmelCase__	      =   [(root, 0)]
              lowerCAmelCase__	      =   defaultdict(UpperCAmelCase_      )

              while queue:
                     lowerCAmelCase__	      =   queue.pop(0      )
                     lowerCAmelCase__      ,   lowerCAmelCase__	      =   first
                     lookup[hd].append(node.val      )

                     if node.left:
                            queue.append((node.left, hd - 1)      )
                     if node.right:
                            queue.append((node.right, hd + 1)      )

              for pair in sorted(lookup.items()      ,    key=lambda lowerCAmelCase__      : each[0]      ):
                     bottom_view.append(pair[1][-1]      )

       lowerCAmelCase__	      =   []
       if not root:
              return bottom_view

       breadth_first_search(UpperCAmelCase_      ,    UpperCAmelCase_      )
       return bottom_view


if __name__ == "__main__":
    import doctest

    doctest.testmod()






import functools
import operator

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


_lowerCamelCase							=      logging.get_logger(__name__)

_lowerCamelCase							=      {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}


class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Dict     					=    "hubert"
     def __init__(				self ,    _lowerCAmelCase=32 ,    _lowerCAmelCase=768 ,    _lowerCAmelCase=12 ,    _lowerCAmelCase=12 ,    _lowerCAmelCase=3_072 ,    _lowerCAmelCase="gelu" ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=True ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.02 ,    _lowerCAmelCase=1E-5 ,    _lowerCAmelCase="group" ,    _lowerCAmelCase="gelu" ,    _lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512) ,    _lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2) ,    _lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2) ,    _lowerCAmelCase=False ,    _lowerCAmelCase=128 ,    _lowerCAmelCase=16 ,    _lowerCAmelCase=False ,    _lowerCAmelCase=True ,    _lowerCAmelCase=0.05 ,    _lowerCAmelCase=10 ,    _lowerCAmelCase=2 ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=10 ,    _lowerCAmelCase=0 ,    _lowerCAmelCase="sum" ,    _lowerCAmelCase=False ,    _lowerCAmelCase=False ,    _lowerCAmelCase=256 ,    _lowerCAmelCase=0 ,    _lowerCAmelCase=1 ,    _lowerCAmelCase=2 ,    **_lowerCAmelCase ,    ):
            super().__init__(**_lowerCAmelCase ,    pad_token_id=_lowerCAmelCase ,    bos_token_id=_lowerCAmelCase ,    eos_token_id=_lowerCAmelCase	)
            a			   =hidden_size
            a			   =feat_extract_norm
            a			   =feat_extract_activation
            a			   =list(_lowerCAmelCase	)
            a			   =list(_lowerCAmelCase	)
            a			   =list(_lowerCAmelCase	)
            a			   =conv_bias
            a			   =num_conv_pos_embeddings
            a			   =num_conv_pos_embedding_groups
            a			   =len(self.conv_dim	)
            a			   =num_hidden_layers
            a			   =intermediate_size
            a			   =hidden_act
            a			   =num_attention_heads
            a			   =hidden_dropout
            a			   =attention_dropout
            a			   =activation_dropout
            a			   =feat_proj_layer_norm
            a			   =feat_proj_dropout
            a			   =final_dropout
            a			   =layerdrop
            a			   =layer_norm_eps
            a			   =initializer_range
            a			   =vocab_size
            a			   =do_stable_layer_norm
            a			   =use_weighted_layer_sum
            a			   =classifier_proj_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)`, 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
            a			   =apply_spec_augment
            a			   =mask_time_prob
            a			   =mask_time_length
            a			   =mask_time_min_masks
            a			   =mask_feature_prob
            a			   =mask_feature_length
            a			   =mask_feature_min_masks

            # ctc loss
            a			   =ctc_loss_reduction
            a			   =ctc_zero_infinity






     @property
     def        lowerCAmelCase__  (				self	):
            return functools.reduce(operator.mul ,    self.conv_stride ,    1	)
