




"""simple docstring"""

class     lowercase :







				def __init__(							self				:   List[Any] ,       _lowerCamelCase				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Dict						=						data
					A_					:			Union[str, Any]						=						None
					A_					:			Optional[int]						=						None






def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	if node is None:
		A_					:			int						=						BinaryTreeNode(lowercase__		)
		return node

	# binary search tree is not empty,
	# so we will insert it into the tree
	# if new_value is less than value of data in node,
	#  add it to left subtree and proceed recursively
	if new_value < node.data:
		A_					:			Dict						=						insert(node.left_child					, lowercase__		)
	else:
		# if new_value is greater than value of data in node,
		#  add it to right subtree and proceed recursively
		A_					:			List[Any]						=						insert(node.right_child					, lowercase__		)
	return node






def 						lowercase_ (						_UpperCAmelCase		):  # if node is None,return




	"""simple docstring"""






	if node:
		A_					:			Union[str, Any]						=						inorder(node.left_child		)
		A_					:			Union[str, Any]						=						[*inorder_array, node.data]
		A_					:			Optional[Any]						=						inorder_array + inorder(node.right_child		)
	else:
		A_					:			str						=						[]
	return inorder_array






def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Union[str, Any]						=						insert(lowercase__					, 15		)
	insert(lowercase__					, 10		)
	insert(lowercase__					, 25		)
	insert(lowercase__					, 6		)
	insert(lowercase__					, 14		)
	insert(lowercase__					, 20		)
	insert(lowercase__					, 60		)
	return root






def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Any						=						make_tree()
	print('''Printing values of binary search tree in Inorder Traversal.'''		)
	inorder(lowercase__		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()











"""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





