
import bisect
import copy
from collections import defaultdict

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

from utils import logger

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Optional[Any]				,    __UpperCamelCase			:    Optional[Any]							)			->							List[str]:

				"""simple docstring"""







				A__	:						List[str]								=				copy.deepcopy(__UpperCamelCase							)
				A__	:						Any								=				sorted(__UpperCamelCase							)
				A__	:						Optional[Any]								=				[bisect.bisect_right(__UpperCamelCase				,    __UpperCamelCase							) for y in x]
				return quantized

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    List[Any]				,    __UpperCamelCase			:    List[Any]=0							)			->							Union[str, Any]:

				"""simple docstring"""







				A__	:						List[str]								=				np.arange(start=3				,    stop=__UpperCamelCase				,    step=4							).tolist() if k > 0 else [10]
				A__	:						int								=				_quantize(__UpperCamelCase				,    __UpperCamelCase							)
				# count number of elements per group
				A__	:						Tuple								=				np.unique(__UpperCamelCase				,    return_counts=__UpperCamelCase							)[1]
				A__	:						Tuple								=				[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 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		):
					if not isinstance(UpperCamelCase__					,							UpperCamelCase__		):
									raise ValueError(
									    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(UpperCamelCase__		)		)
					A__	:						Optional[Any]								=				sampler
					A__	:						Any								=				group_ids
					A__	:						Optional[Any]								=				batch_size





	def __iter__(      self		):
					A__	:						Union[str, Any]								=				defaultdict(UpperCamelCase__		)
					A__	:						str								=				defaultdict(UpperCamelCase__		)

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

				"""simple docstring"""







				if not isinstance(__UpperCamelCase				,    __UpperCamelCase							) or not all(
				    isinstance(__UpperCamelCase				,    __UpperCamelCase							) and item >= 0 for item in citations							):
								raise ValueError('''The citations should be a list of non negative integers.'''							)

				citations.sort()
				A__	:						str								=				len(__UpperCamelCase							)

				for i in range(__UpperCamelCase							):
								if citations[len_citations - 1 - i] <= i:
												return i

				return len_citations


if __name__ == "__main__":
					import doctest

					doctest.testmod()