'''simple docstring'''




from __future__ import annotations

__snake_case   :      List[str]						    =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]




class 	__UpperCAmelCase				:





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE )		->     None:
           A_     =   -1
           A_     =   -1
           A_     =   [-1] * length
           A_     =   None
           A_     =   None

       def __repr__(      self )		->     str:
           return F'''Node(min_value={self.minn} max_value={self.maxx})'''





def      _UpperCAmelCase    (							_UpperCamelCase    :	list[int]   )	->      Node | None:
    A_     =   Node(len(_UpperCamelCase   )   )
    A_   ,A_     =   min(_UpperCamelCase   ), max(_UpperCamelCase   )
    # Leaf node case where the node contains only one unique value
    if root.minn == root.maxx:
        return root
    A_     =   (root.minn + root.maxx) // 2

    A_     =   []
    A_     =   []

    for index, num in enumerate(_UpperCamelCase   ):
        if num <= pivot:
            left_arr.append(_UpperCamelCase   )
        else:
            right_arr.append(_UpperCamelCase   )
        A_     =   len(_UpperCamelCase   )
    A_     =   build_tree(_UpperCamelCase   )
    A_     =   build_tree(_UpperCamelCase   )
    return root





def      _UpperCAmelCase    (							_UpperCamelCase    :	Node | None,   _UpperCamelCase    :	int,   _UpperCamelCase    :	int   )	->      int:
    if index < 0 or node is None:
        return 0
    # Leaf node cases
    if node.minn == node.maxx:
        return index + 1 if node.minn == num else 0
    A_     =   (node.minn + node.maxx) // 2
    if num <= pivot:
        # go the left subtree and map index to the left subtree
        return rank_till_index(node.left,   _UpperCamelCase,   node.map_left[index] - 1   )
    else:
        # go to the right subtree and map index to the right subtree
        return rank_till_index(node.right,   _UpperCamelCase,   index - node.map_left[index]   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Node | None,   _UpperCamelCase    :	int,   _UpperCamelCase    :	int,   _UpperCamelCase    :	int   )	->      int:
    if start > end:
        return 0
    A_     =   rank_till_index(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
    A_     =   rank_till_index(_UpperCamelCase,   _UpperCamelCase,   start - 1   )
    return rank_till_end - rank_before_start





def      _UpperCAmelCase    (							_UpperCamelCase    :	Node | None,   _UpperCamelCase    :	int,   _UpperCamelCase    :	int,   _UpperCamelCase    :	int   )	->      int:
    if index > (end - start) or start > end or node is None:
        return -1
    # Leaf node case
    if node.minn == node.maxx:
        return node.minn
    # Number of elements in the left subtree in interval [start, end]
    A_     =   node.map_left[end] - (
        node.map_left[start - 1] if start else 0
    )
    if num_elements_in_left_tree > index:
        return quantile(
            node.left,   _UpperCamelCase,   (node.map_left[start - 1] if start else 0),   node.map_left[end] - 1,   )
    else:
        return quantile(
            node.right,   index - num_elements_in_left_tree,   start - (node.map_left[start - 1] if start else 0),   end - node.map_left[end],   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Node | None,   _UpperCamelCase    :	int,   _UpperCamelCase    :	int,   _UpperCamelCase    :	int,   _UpperCamelCase    :	int   )	->      int:
    if (
        start > end
        or node is None
        or start_num > end_num
        or node.minn > end_num
        or node.maxx < start_num
    ):
        return 0
    if start_num <= node.minn and node.maxx <= end_num:
        return end - start + 1
    A_     =   range_counting(
        node.left,   (node.map_left[start - 1] if start else 0),   node.map_left[end] - 1,   _UpperCamelCase,   _UpperCamelCase,   )
    A_     =   range_counting(
        node.right,   start - (node.map_left[start - 1] if start else 0),   end - node.map_left[end],   _UpperCamelCase,   _UpperCamelCase,   )
    return left + right


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






