

'''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  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  Optional[int]       )   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=copy.deepcopy(lowercase__       )
     _SCREAMING_SNAKE_CASE												=sorted(lowercase__       )
     _SCREAMING_SNAKE_CASE												=[bisect.bisect_right(lowercase__							,				lowercase__       ) for y in x]
     return quantized


def  _lowerCAmelCase      (		_UpperCamelCase		:  int							,				_UpperCamelCase		:  Tuple=0       )   ->    Dict:
     """simple docstring"""




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







class 							A__					( __UpperCAmelCase						):





  def __init__(	self   :			Optional[Any]							,      _a   :			List[str]							,      _a   :			Optional[Any]							,      _a   :			Tuple				)			->					Optional[int]:





       '''simple docstring'''






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





  def __iter__(	self   :			List[str]				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=defaultdict(lowerCamelCase__				)
       _SCREAMING_SNAKE_CASE												=defaultdict(lowerCamelCase__				)

       _SCREAMING_SNAKE_CASE												=0
       for idx in self.sampler:
            _SCREAMING_SNAKE_CASE												=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
       _SCREAMING_SNAKE_CASE												=len(self				)
       _SCREAMING_SNAKE_CASE												=expected_num_batches - num_batches
       if num_remaining > 0:
            # for the remaining batches, group the batches by similar lengths
            _SCREAMING_SNAKE_CASE												=[]
            for group_id, idxs in sorted(buffer_per_group.items()							,      key=lambda _a				: x[0]				):
                 batch_idx.extend(lowerCamelCase__				)
                 if len(lowerCamelCase__				) >= self.batch_size:
                      yield batch_idx[: self.batch_size]
                      _SCREAMING_SNAKE_CASE												=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   :			Tuple				)			->					Any:





       '''simple docstring'''






       return (len(self.sampler				) + self.batch_size - 1) // self.batch_size



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  list       )   ->    list:
     """simple docstring"""




     for i, _ in enumerate(_UpperCamelCase       ):
          if (i % 2 == 1) == (nums[i - 1] > nums[i]):
               _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=nums[i], nums[i - 1]

     return nums


if __name__ == "__main__":
     print("Enter the array elements:")
     lowerCamelCase				:   Tuple      				=  list(map(int, input().split()))
     print("The unsorted array is:")
     print(array)
     print("Array after Wiggle sort:")
     print(wiggle_sort(array))
