




"""simple docstring"""

from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar

class     lowercase (      __UpperCAmelCase):







				@abstractmethod
				def __lt__(							self				:   T ,       _lowerCamelCase				:   T    ):



					"""simple docstring"""

					pass







				@abstractmethod
				def __gt__(							self				:   T ,       _lowerCamelCase				:   T    ):



					"""simple docstring"""

					pass







				@abstractmethod
				def __eq__(							self				:   T ,       _lowerCamelCase				:   object    ):



					"""simple docstring"""

					pass
_lowerCamelCase					:     int				       =    TypeVar('T', bound=Comparable)

class     lowercase (      Generic[T]):







				def __init__(							self				:   str    ):



					"""simple docstring"""

					A_					:			list[T]						=						[]
					A_					:			int						=						0







				def __repr__(							self				:   List[str]    ):



					"""simple docstring"""

					return str(self.h    )







				def    a_					(							self				:   Union[str, Any] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					if child_idx > 0:
						return (child_idx - 1) // 2
					return None







				def    a_					(							self				:   Optional[int] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					A_					:			Dict						=						2 * parent_idx + 1
					if left_child_index < self.heap_size:
						return left_child_index
					return None







				def    a_					(							self				:   Any ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					A_					:			Tuple						=						2 * parent_idx + 2
					if right_child_index < self.heap_size:
						return right_child_index
					return None







				def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					if index < self.heap_size:
						A_					:			int						=						index
						A_					:			List[str]						=						self.left_child_idx(_lowerCamelCase    )
						A_					:			Tuple						=						self.right_child_idx(_lowerCamelCase    )
						# check which child is larger than its parent
						if left_child is not None and self.h[left_child] > self.h[violation]:
							A_					:			Dict						=						left_child
						if right_child is not None and self.h[right_child] > self.h[violation]:
							A_					:			Dict						=						right_child
						# if violation indeed exists
						if violation != index:
							# swap to fix the violation
							A_				,	A_					:			str						=						self.h[index], self.h[violation]
							# fix the subsequent violation recursively if any
							self.max_heapify(_lowerCamelCase    )







				def    a_					(							self				:   int ,       _lowerCamelCase				:   Iterable[T]    ):



					"""simple docstring"""

					A_					:			Tuple						=						list(_lowerCamelCase    )
					A_					:			Tuple						=						len(self.h    )
					if self.heap_size > 1:
						# max_heapify from right to left but exclude leaves (last level)
						for i in range(self.heap_size // 2 - 1 ,       -1 ,       -1    ):
							self.max_heapify(_lowerCamelCase    )







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					if self.heap_size >= 2:
						A_					:			Optional[int]						=						self.h[0]
						A_					:			str						=						self.h.pop(-1    )
						self.heap_size -= 1
						self.max_heapify(0    )
						return me
					elif self.heap_size == 1:
						self.heap_size -= 1
						return self.h.pop(-1    )
					else:
						raise Exception('''Empty heap'''    )







				def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   T    ):



					"""simple docstring"""

					self.h.append(_lowerCamelCase    )
					A_					:			int						=						(self.heap_size - 1) // 2
					self.heap_size += 1
					while idx >= 0:
						self.max_heapify(_lowerCamelCase    )
						A_					:			Optional[Any]						=						(idx - 1) // 2







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.heap_size
					for j in range(size - 1 ,       0 ,       -1    ):
						A_				,	A_					:			Tuple						=						self.h[j], self.h[0]
						self.heap_size -= 1
						self.max_heapify(0    )
					A_					:			Tuple						=						size


if __name__ == "__main__":
		import doctest

		# run doc test
		doctest.testmod()

		# demo
		for unsorted in [
		    [0],
		    [2],
		    [3, 5],
		    [5, 3],
		    [5, 5],
		    [0, 0, 0, 0],
		    [1, 1, 1, 1],
		    [2, 2, 3, 5],
		    [0, 2, 2, 3, 5],
		    [2, 5, 3, 0, 2, 3, 0, 3],
		    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
		    [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
		    [-45, -2, -5],
		]:
				print(f'unsorted array: {unsorted}')

				_lowerCamelCase					:     Heap[int]				       =    Heap()
				heap.build_max_heap(unsorted)
				print(f'after build heap: {heap}')

				print(f'max value: {heap.extract_max()}')
				print(f'after max value removed: {heap}')

				heap.insert(100)
				print(f'after new value 100 inserted: {heap}')

				heap.heap_sort()
				print(f'heap-sorted array: {heap}\n')











"""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





