



'''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 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							)			->						Optional[Any]:







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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]=0							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	np.arange(start=3							,		stop=_SCREAMING_SNAKE_CASE							,		step=4							).tolist() if k > 0 else [10]
	lowerCAmelCase    =	_quantize(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	# count number of elements per group
	lowerCAmelCase    =	np.unique(_SCREAMING_SNAKE_CASE							,		return_counts=_SCREAMING_SNAKE_CASE							)[1]
	lowerCAmelCase    =	[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 __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_	,       A_      )	->	List[str]:
							if not isinstance(A_	,       A_      ):
								raise ValueError(
								    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(A_      )      )
							lowerCAmelCase    =	sampler
							lowerCAmelCase    =	group_ids
							lowerCAmelCase    =	batch_size


						def __iter__(					self      )	->	Union[str, Any]:
							lowerCAmelCase    =	defaultdict(A_      )
							lowerCAmelCase    =	defaultdict(A_      )

							lowerCAmelCase    =	0
							for idx in self.sampler:
								lowerCAmelCase    =	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
							lowerCAmelCase    =	len(self      )
							lowerCAmelCase    =	expected_num_batches - num_batches
							if num_remaining > 0:
								# for the remaining batches, group the batches by similar lengths
								lowerCAmelCase    =	[]
								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]
										lowerCAmelCase    =	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      )	->	Union[str, Any]:
							return (len(self.sampler      ) + self.batch_size - 1) // self.batch_size




'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

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

class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	None:
							lowerCAmelCase    =	value
							lowerCAmelCase    =	None
							lowerCAmelCase    =	None


						@property
						def 				__snake_case    (					self      )	->	T:
							return self._value




						@staticmethod
						def 				__snake_case    (					A_	,       A_      )	->	RandomizedHeapNode[T] | None:
							if not roota:
								return roota

							if not roota:
								return roota

							if roota.value > roota.value:
								lowerCAmelCase,   lowerCAmelCase    =	roota, roota

							if random.choice([True, False]      ):
								lowerCAmelCase,   lowerCAmelCase    =	roota.right, roota.left

							lowerCAmelCase    =	RandomizedHeapNode.merge(roota.left	,       A_      )

							return roota







class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_ = ()      )	->	None:
							lowerCAmelCase    =	None

							if data:
								for item in data:
									self.insert(A_      )


						def 				__snake_case    (					self	,       A_      )	->	None:
							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root	,       RandomizedHeapNode(A_      )      )


						def 				__snake_case    (					self      )	->	T | None:
							lowerCAmelCase    =	self.top()

							if self._root is None:
								return None

							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root.left	,       self._root.right      )

							return result


						def 				__snake_case    (					self      )	->	T:
							if not self._root:
								raise IndexError("""Can't get top element for the empty heap."""      )
							return self._root.value


						def 				__snake_case    (					self      )	->	None:
							lowerCAmelCase    =	None


						def 				__snake_case    (					self      )	->	list[Any]:
							lowerCAmelCase    =	[]
							while self:
								result.append(self.pop()      )

							return result




						def __bool__(					self      )	->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()