




"""simple docstring"""

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	if not isinstance(_UpperCAmelCase					, _UpperCAmelCase		):
		raise ValueError('''Input series is not valid, valid series - [1, 2/3, 2]'''		)
	if len(_UpperCAmelCase		) == 0:
		raise ValueError('''Input list must be a non empty list'''		)
	if len(_UpperCAmelCase		) == 1 and series[0] != 0:
		return True
	A_					:			str						=						[]
	A_					:			Optional[Any]						=						len(_UpperCAmelCase		)
	for i in range(0					, _UpperCAmelCase		):
		if series[i] == 0:
			raise ValueError('''Input series cannot have 0 as an element'''		)
		rec_series.append(1 / series[i]		)
	A_					:			Tuple						=						rec_series[1] - rec_series[0]
	for index in range(2					, _UpperCAmelCase		):
		if rec_series[index] - rec_series[index - 1] != common_diff:
			return False
	return True

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	if not isinstance(_UpperCAmelCase					, _UpperCAmelCase		):
		raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''		)
	if len(_UpperCAmelCase		) == 0:
		raise ValueError('''Input list must be a non empty list'''		)
	A_					:			Optional[int]						=						0
	for val in series:
		answer += 1 / val
	return len(_UpperCAmelCase		) / answer


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 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						copy.deepcopy(_UpperCAmelCase		)
	A_					:			int						=						sorted(_UpperCAmelCase		)
	A_					:			Tuple						=						[bisect.bisect_right(_UpperCAmelCase					, _UpperCAmelCase		) for y in x]
	return quantized

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=0		):




	"""simple docstring"""






	A_					:			List[Any]						=						np.arange(start=3					, stop=_UpperCAmelCase					, step=4		).tolist() if k > 0 else [10]
	A_					:			List[Any]						=						_quantize(_UpperCAmelCase					, _UpperCAmelCase		)
	# count number of elements per group
	A_					:			Tuple						=						np.unique(_UpperCAmelCase					, return_counts=_UpperCAmelCase		)[1]
	A_					:			Optional[int]						=						[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     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Union[str, Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Dict    ):



					"""simple docstring"""

					if not isinstance(_lowerCamelCase ,       _lowerCamelCase    ):
						raise ValueError(
						    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_lowerCamelCase    )    )
					A_					:			List[str]						=						sampler
					A_					:			str						=						group_ids
					A_					:			Any						=						batch_size







				def __iter__(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			List[str]						=						defaultdict(_lowerCamelCase    )
					A_					:			Dict						=						defaultdict(_lowerCamelCase    )

					A_					:			List[str]						=						0
					for idx in self.sampler:
						A_					:			Union[str, Any]						=						self.group_ids[idx]
						buffer_per_group[group_id].append(_lowerCamelCase    )
						samples_per_group[group_id].append(_lowerCamelCase    )
						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_					:			int						=						len(self    )
					A_					:			Dict						=						expected_num_batches - num_batches
					if num_remaining > 0:
						# for the remaining batches, group the batches by similar lengths
						A_					:			Optional[Any]						=						[]
						for group_id, idxs in sorted(buffer_per_group.items() ,       key=lambda _lowerCamelCase    : x[0]    ):
							batch_idx.extend(_lowerCamelCase    )
							if len(_lowerCamelCase    ) >= self.batch_size:
								yield batch_idx[: self.batch_size]
								A_					:			str						=						batch_idx[self.batch_size :]
								num_remaining -= 1
						if len(_lowerCamelCase    ) > 0:
							yield batch_idx
							num_remaining -= 1
					assert num_remaining == 0







				def __len__(							self				:   List[Any]    ):



					"""simple docstring"""

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





