




'''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   (       snake_case__   , snake_case__		):
			__UpperCamelCase :     Optional[Any]										=  copy.deepcopy(_UpperCamelCase		)
			__UpperCamelCase :     Any										=  sorted(_UpperCamelCase		)
			__UpperCamelCase :     Union[str, Any]										=  [bisect.bisect_right(_UpperCamelCase   , _UpperCamelCase		) for y in x]
			return quantized

def      __lowerCAmelCase   (       snake_case__   , snake_case__=0		):
			__UpperCamelCase :     int										=  np.arange(start=3   , stop=_UpperCamelCase   , step=4		).tolist() if k > 0 else [10]
			__UpperCamelCase :     Any										=  _quantize(_UpperCamelCase   , _UpperCamelCase		)
			# count number of elements per group
			__UpperCamelCase :     Dict										=  np.unique(_UpperCamelCase   , return_counts=_UpperCamelCase		)[1]
			__UpperCamelCase :     Union[str, Any]										=  [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    A						(					_lowerCamelCase						):




				'''simple docstring'''





				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Optional[Any]:
							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     )     )
							__UpperCamelCase :     List[Any]										=  sampler
							__UpperCamelCase :     int										=  group_ids
							__UpperCamelCase :     Optional[Any]										=  batch_size
				def __iter__(self     )      ->			Union[str, Any]:
							__UpperCamelCase :     Any										=  defaultdict(_SCREAMING_SNAKE_CASE     )
							__UpperCamelCase :     Tuple										=  defaultdict(_SCREAMING_SNAKE_CASE     )

							__UpperCamelCase :     Union[str, Any]										=  0
							for idx in self.sampler:
										__UpperCamelCase :     Any										=  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
							__UpperCamelCase :     Any										=  len(self     )
							__UpperCamelCase :     Tuple										=  expected_num_batches - num_batches
							if num_remaining > 0:
										# for the remaining batches, group the batches by similar lengths
										__UpperCamelCase :     Any										=  []
										for group_id, idxs in sorted(buffer_per_group.items()					,    key=lambda _UpperCAmelCase     : x[0]     ):
													batch_idx.extend(_SCREAMING_SNAKE_CASE     )
													if len(_SCREAMING_SNAKE_CASE     ) >= self.batch_size:
																yield batch_idx[: self.batch_size]
																__UpperCamelCase :     List[str]										=  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     )      ->			Tuple:
							return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size





"""simple docstring"""







def        __lowerCAmelCase							(_UpperCamelCase = 10**6						):

	if n <= 0:
		raise ValueError('Please enter an integer greater than 0'						)

	__lowerCAmelCase     :		Optional[Any]		     = list(range(n + 1						)						)
	for number in range(2			,    n + 1						):
		if phi[number] == number:
			phi[number] -= 1
			for multiple in range(number * 2			,    n + 1			,    _UpperCamelCase						):
				__lowerCAmelCase     :		Tuple		     = (phi[multiple] // number) * (number - 1)

	__lowerCAmelCase     :		Any		     = 1
	for number in range(1			,    n + 1						):
		if (answer / phi[answer]) < (number / phi[number]):
			__lowerCAmelCase     :		List[Any]		     = number

	return answer


if __name__ == "__main__":
							print(solution())