



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       :			Tuple	,						__a       :			Optional[Any]						):

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

def      A_ (		__a       :			List[str]	,						__a       :			Optional[int]=0						):

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




class 	__snake_case (				SCREAMING_SNAKE_CASE):







		'''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






def      A_ (		__a       :			float						):

				"""simple docstring"""
				return int(__a						) if x - int(__a						) >= 0 else int(__a						) - 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()

