'''simple docstring'''




from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass




@dataclass
class 	__UpperCAmelCase				:





       '''simple docstring'''



       __lowercase					:					int
       __lowercase					:					TreeNode | None    =				None
       __lowercase					:					TreeNode | None    =				None





def      _UpperCAmelCase    (							)	->      TreeNode:
    return TreeNode(3,   TreeNode(9   ),   TreeNode(20,   TreeNode(15   ),   TreeNode(7   )   )   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	TreeNode   )	->      list[int]:

    def depth_first_search(
        _UpperCamelCase    :	TreeNode | None,   _UpperCamelCase    :	int,   _UpperCamelCase    :	list[int]   ) -> 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   )

    A_     =   []
    if not root:
        return right_view

    depth_first_search(_UpperCamelCase,   0,   _UpperCamelCase   )
    return right_view





def      _UpperCAmelCase    (							_UpperCamelCase    :	TreeNode   )	->      list[int]:

    def depth_first_search(
        _UpperCamelCase    :	TreeNode | None,   _UpperCamelCase    :	int,   _UpperCamelCase    :	list[int]   ) -> 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   )

    A_     =   []
    if not root:
        return left_view

    depth_first_search(_UpperCamelCase,   0,   _UpperCamelCase   )
    return left_view





def      _UpperCAmelCase    (							_UpperCamelCase    :	TreeNode   )	->      list[int]:

    def breadth_first_search(_UpperCamelCase    :	TreeNode,   _UpperCamelCase    :	list[int]   ) -> None:
        A_     =   [(root, 0)]
        A_     =   defaultdict(_UpperCamelCase   )

        while queue:
            A_     =   queue.pop(0   )
            A_   ,A_     =   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 _UpperCamelCase   : each[0]   ):
            top_view.append(pair[1][0]   )

    A_     =   []
    if not root:
        return top_view

    breadth_first_search(_UpperCamelCase,   _UpperCamelCase   )
    return top_view





def      _UpperCAmelCase    (							_UpperCamelCase    :	TreeNode   )	->      list[int]:
    from collections import defaultdict

    def breadth_first_search(_UpperCamelCase    :	TreeNode,   _UpperCamelCase    :	list[int]   ) -> None:
        A_     =   [(root, 0)]
        A_     =   defaultdict(_UpperCamelCase   )

        while queue:
            A_     =   queue.pop(0   )
            A_   ,A_     =   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 _UpperCamelCase   : each[0]   ):
            bottom_view.append(pair[1][-1]   )

    A_     =   []
    if not root:
        return bottom_view

    breadth_first_search(_UpperCamelCase,   _UpperCamelCase   )
    return bottom_view


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''simple docstring'''




import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	int   )	->      Union[str, Any]:
    A_     =   copy.deepcopy(_UpperCamelCase   )
    A_     =   sorted(_UpperCamelCase   )
    A_     =   [bisect.bisect_right(_UpperCamelCase,   _UpperCamelCase   ) for y in x]
    return quantized





def      _UpperCAmelCase    (							_UpperCamelCase    :	str,   _UpperCamelCase    :	Tuple=0   )	->      str:
    A_     =   np.arange(start=3,   stop=_UpperCamelCase,   step=4   ).tolist() if k > 0 else [10]
    A_     =   _quantize(_UpperCamelCase,   _UpperCamelCase   )
    # count number of elements per group
    A_     =   np.unique(_UpperCamelCase,   return_counts=_UpperCamelCase   )[1]
    A_     =   [0] + bins + [np.inf]
    logger.info('''Using {} as bins for aspect lengths quantization'''.format(_UpperCamelCase   )   )
    logger.info('''Count of instances per bin: {}'''.format(_UpperCamelCase   )   )
    return groups




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           if not isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
               raise ValueError(
                   '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_SCREAMING_SNAKE_CASE ) )
           A_     =   sampler
           A_     =   group_ids
           A_     =   batch_size

       def __iter__(      self )		->     Tuple:
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )

           A_     =   0
           for idx in self.sampler:
               A_     =   self.group_ids[idx]
               buffer_per_group[group_id].append(_SCREAMING_SNAKE_CASE )
               samples_per_group[group_id].append(_SCREAMING_SNAKE_CASE )
               if len(buffer_per_group[group_id] ) == self.batch_size:
                   yield buffer_per_group[group_id]  # TODO
                   num_batches += 1
                   del buffer_per_group[group_id]
               assert len(buffer_per_group[group_id] ) < self.batch_size

           # now we have run out of elements that satisfy
           # the group criteria, let's return the remaining
           # elements so that the size of the sampler is
           # deterministic
           A_     =   len(self )
           A_     =   expected_num_batches - num_batches
           if num_remaining > 0:
               # for the remaining batches, group the batches by similar lengths
               A_     =   []
               for group_id, idxs in sorted(buffer_per_group.items()    ,     key=lambda _SCREAMING_SNAKE_CASE : x[0] ):
                   batch_idx.extend(_SCREAMING_SNAKE_CASE )
                   if len(_SCREAMING_SNAKE_CASE ) >= self.batch_size:
                       yield batch_idx[: self.batch_size]
                       A_     =   batch_idx[self.batch_size :]
                       num_remaining -= 1
               if len(_SCREAMING_SNAKE_CASE ) > 0:
                   yield batch_idx
                   num_remaining -= 1
           assert num_remaining == 0

       def __len__(      self )		->     Optional[int]:
           return (len(self.sampler ) + self.batch_size - 1) // self.batch_size






