
from __future__ import annotations

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







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	@abstractmethod
	def __lt__(	self		,   snake_case	):
		pass





	@abstractmethod
	def __gt__(	self		,   snake_case	):
		pass





	@abstractmethod
	def __eq__(	self		,   snake_case	):
		pass




UpperCAmelCase     				=			TypeVar('''T''', bound=Comparable)







class 	A_       (      Generic[T]       ):




	'''simple docstring'''


	def __init__(	self	):
		lowercase					  =   []
		lowercase					  =   0





	def __repr__(	self	):
		return str(self.h	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		if child_idx > 0:
			return (child_idx - 1) // 2
		return None





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   2 * parent_idx + 1
		if left_child_index < self.heap_size:
			return left_child_index
		return None





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   2 * parent_idx + 2
		if right_child_index < self.heap_size:
			return right_child_index
		return None





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		if index < self.heap_size:
			lowercase					  =   index
			lowercase					  =   self.left_child_idx(snake_case	)
			lowercase					  =   self.right_child_idx(snake_case	)
			# check which child is larger than its parent
			if left_child is not None and self.h[left_child] > self.h[violation]:
				lowercase					  =   left_child
			if right_child is not None and self.h[right_child] > self.h[violation]:
				lowercase					  =   right_child
			# if violation indeed exists
			if violation != index:
				# swap to fix the violation
				lowercase  , lowercase					  =   self.h[index], self.h[violation]
				# fix the subsequent violation recursively if any
				self.max_heapify(snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   list(snake_case	)
		lowercase					  =   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(snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		if self.heap_size >= 2:
			lowercase					  =   self.h[0]
			lowercase					  =   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      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		self.h.append(snake_case	)
		lowercase					  =   (self.heap_size - 1) // 2
		self.heap_size += 1
		while idx >= 0:
			self.max_heapify(snake_case	)
			lowercase					  =   (idx - 1) // 2





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.heap_size
		for j in range(size - 1		,   0		,   -1	):
			lowercase  , lowercase					  =   self.h[j], self.h[0]
			self.heap_size -= 1
			self.max_heapify(0	)
		lowercase					  =   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}""")

		UpperCAmelCase     				=			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""")



import bisect
import copy
from collections import defaultdict

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

from utils import logger






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   copy.deepcopy(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   sorted(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   [bisect.bisect_right(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) for y in x]
	return quantized






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







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


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





	def __iter__(	self	):
		lowercase					  =   defaultdict(snake_case	)
		lowercase					  =   defaultdict(snake_case	)

		lowercase					  =   0
		for idx in self.sampler:
			lowercase					  =   self.group_ids[idx]
			buffer_per_group[group_id].append(snake_case	)
			samples_per_group[group_id].append(snake_case	)
			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
		lowercase					  =   len(self	)
		lowercase					  =   expected_num_batches - num_batches
		if num_remaining > 0:
			# for the remaining batches, group the batches by similar lengths
			lowercase					  =   []
			for group_id, idxs in sorted(buffer_per_group.items()		,   key=lambda snake_case	: x[0]	):
				batch_idx.extend(snake_case	)
				if len(snake_case	) >= self.batch_size:
					yield batch_idx[: self.batch_size]
					lowercase					  =   batch_idx[self.batch_size :]
					num_remaining -= 1
			if len(snake_case	) > 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

