



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def       A_   (						__a  :					Union[str, Any]			,		__a  :					int		):

				"""simple docstring"""
				a__					   =		copy.deepcopy(_lowercase		)
				a__					   =		sorted(_lowercase		)
				a__					   =		[bisect.bisect_right(_lowercase			,		_lowercase		) for y in x]
				return quantized

def       A_   (						__a  :					Dict			,		__a  :					Optional[Any]=0		):

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




class 			__snake_case   ( _a):







							'''simple docstring'''






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


							def __iter__(				self		):
											a__					   =		defaultdict(a_		)
											a__					   =		defaultdict(a_		)

											a__					   =		0
											for idx in self.sampler:
															a__					   =		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
											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 a_		: x[0]		):
																			batch_idx.extend(a_		)
																			if len(a_		) >= self.batch_size:
																							yield batch_idx[: self.batch_size]
																							a__					   =		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		):
											return (len(self.sampler		) + self.batch_size - 1) // self.batch_size





'''simple docstring'''



import math
from decimal import Decimal, getcontext




def       _a      (		_lowercase   :      int = 4000000    ):





     '''simple docstring'''






     try:
          __UpperCAmelCase      :					List[str]    				=     int(_lowercase    )
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''    )
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''    )
     __UpperCAmelCase      :					Tuple    				=     100
     __UpperCAmelCase      :					Optional[Any]    				=     (Decimal(5    ) ** Decimal(0.5    ) + 1) / Decimal(2    )

     __UpperCAmelCase      :					Tuple    				=     (math.floor(math.log(n * (phi + 2)     ,   _lowercase    ) - 1    ) // 3) * 3 + 2
     __UpperCAmelCase      :					List[Any]    				=     Decimal(round(phi ** Decimal(index + 1    )    )    ) / (phi + 2)
     __UpperCAmelCase      :					List[str]    				=     num // 2
     return int(_lowercase    )


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