





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,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							copy.deepcopy(__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							sorted(__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[bisect.bisect_right(__lowerCamelCase,				__lowerCamelCase		) for y in x]
     return quantized






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase=0		):
     _SCREAMING_SNAKE_CASE				:		Any  			=							np.arange(start=3,				stop=__lowerCamelCase,				step=4		).tolist() if k > 0 else [10]
     _SCREAMING_SNAKE_CASE				:		str  			=							_quantize(__lowerCamelCase,				__lowerCamelCase		)
     # count number of elements per group
     _SCREAMING_SNAKE_CASE				:		str  			=							np.unique(__lowerCamelCase,				return_counts=__lowerCamelCase		)[1]
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[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 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)	->   str:
            if not isinstance(__lowerCamelCase			,  __lowerCamelCase							):
                 raise ValueError(
                     "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(__lowerCamelCase							)							)
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							sampler
            _SCREAMING_SNAKE_CASE				:		str  			=							group_ids
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							batch_size




       def __iter__(					self							)	->   Union[str, Any]:
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							defaultdict(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							defaultdict(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							0
            for idx in self.sampler:
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							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
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							len(self							)
            _SCREAMING_SNAKE_CASE				:		str  			=							expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 _SCREAMING_SNAKE_CASE				:		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]
                           _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							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[str]:
            return (len(self.sampler							) + self.batch_size - 1) // self.batch_size

"""simple docstring"""


def     _lowerCamelCase(__UpperCamelCase = 4000000       )				-> int:
	_lowerCAmelCase								=1
	_lowerCAmelCase								=2
	_lowerCAmelCase								=0
	while j <= n:
		if j % 2 == 0:
			total += j
		_lowerCAmelCase						,							_lowerCAmelCase								=j, i + j

	return total


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



