



'''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 	__lowerCAmelCase       (				lowerCamelCase :    Any					,		lowerCamelCase :    int       ):






	'''simple docstring'''




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



def 	__lowerCAmelCase       (				lowerCamelCase :    List[Any]					,		lowerCamelCase :    List[str]=0       ):






	'''simple docstring'''




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






class        UpperCAmelCase__						(		UpperCamelCase__			):



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



					def __iter__(     self  )  ->      Dict:
						__lowerCAmelCase       				=	defaultdict(UpperCamelCase  )
						__lowerCAmelCase       				=	defaultdict(UpperCamelCase  )

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



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




'''simple docstring'''



import math



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if not isinstance(lowerCamelCase					,		lowerCamelCase       ):
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be an integer'''
		raise TypeError(lowerCamelCase       )
	if number < 1:
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be a positive integer'''
		raise ValueError(lowerCamelCase       )
	__lowerCAmelCase       				=	[number]
	while number != 1:
		if number % 2 == 0:
			__lowerCAmelCase       				=	math.floor(math.sqrt(lowerCamelCase       )       )
		else:
			__lowerCAmelCase       				=	math.floor(
			    math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       )       )
		sequence.append(lowerCamelCase       )
	return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()