


'''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 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Dict			,						_UpperCamelCase					: Dict      )						->    int:





   '''simple docstring'''







   UpperCamelCase__         =  copy.deepcopy(_UpperCamelCase      )
   UpperCamelCase__         =  sorted(_UpperCamelCase      )
   UpperCamelCase__         =  [bisect.bisect_right(_UpperCamelCase			,						_UpperCamelCase      ) for y in x]
   return quantized



def 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Optional[int]			,						_UpperCamelCase					: Optional[Any]=0      )						->    int:





   '''simple docstring'''







   UpperCamelCase__         =  np.arange(start=3			,						stop=_UpperCamelCase			,						step=4      ).tolist() if k > 0 else [10]
   UpperCamelCase__         =  _quantize(_UpperCamelCase			,						_UpperCamelCase      )
   # count number of elements per group
   UpperCamelCase__         =  np.unique(_UpperCamelCase			,						return_counts=_UpperCamelCase      )[1]
   UpperCamelCase__         =  [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       (				__SCREAMING_SNAKE_CASE):

 def __init__(       self       :		int,    a_       :		Union[str, Any],    a_       :		Any,    a_       :		List[str]    ):





    """simple docstring"""



    if not isinstance(a_,    a_    ):
       raise ValueError(
           "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(a_    )    )
    UpperCamelCase__         =  sampler
    UpperCamelCase__         =  group_ids
    UpperCamelCase__         =  batch_size

 def __iter__(       self       :		Tuple    ):





    """simple docstring"""



    UpperCamelCase__         =  defaultdict(a_    )
    UpperCamelCase__         =  defaultdict(a_    )

    UpperCamelCase__         =  0
    for idx in self.sampler:
       UpperCamelCase__         =  self.group_ids[idx]
       buffer_per_group[group_id].append(a_    )
       samples_per_group[group_id].append(a_    )
       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
    UpperCamelCase__         =  len(self    )
    UpperCamelCase__         =  expected_num_batches - num_batches
    if num_remaining > 0:
       # for the remaining batches, group the batches by similar lengths
       UpperCamelCase__         =  []
       for group_id, idxs in sorted(buffer_per_group.items(),    key=lambda a_    : x[0]    ):
          batch_idx.extend(a_    )
          if len(a_    ) >= self.batch_size:
             yield batch_idx[: self.batch_size]
             UpperCamelCase__         =  batch_idx[self.batch_size :]
             num_remaining -= 1
       if len(a_    ) > 0:
          yield batch_idx
          num_remaining -= 1
    assert num_remaining == 0

 def __len__(       self       :		Tuple    ):





    """simple docstring"""



    return (len(self.sampler    ) + self.batch_size - 1) // self.batch_size





def  lowerCAmelCase  (	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					return n * (2 * n - 1)







def  lowerCAmelCase  (	UpperCAmelCase       )  ->bool:






					"""simple docstring"""






					__magic_name__					:							Any		   =							(1 + 24 * n) ** 0.5
					return ((1 + root) / 6) % 1 == 0







def  lowerCAmelCase  (	UpperCAmelCase = 144       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							start
					__magic_name__					:							int		   =							hexagonal_num(UpperCAmelCase       )
					while not is_pentagonal(UpperCAmelCase       ):
										n += 1
										__magic_name__					:							Optional[int]		   =							hexagonal_num(UpperCAmelCase       )
					return num


if __name__ == "__main__":
				print(f"{solution()} = ")



