





import bisect
import copy
from collections import defaultdict

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

from utils import logger




def 						A_			(  lowercase_      ,		lowercase_  )			->						Optional[Any]:
       _snake_case      :							Optional[int]     				=						copy.deepcopy(lowercase_  )
       _snake_case      :							str     				=						sorted(lowercase_  )
       _snake_case      :							Union[str, Any]     				=						[bisect.bisect_right(lowercase_      ,		lowercase_  ) for y in x]
       return quantized




def 						A_			(  lowercase_      ,		lowercase_=0  )			->						Optional[Any]:
       _snake_case      :							Dict     				=						np.arange(start=3      ,		stop=lowercase_      ,		step=4  ).tolist() if k > 0 else [10]
       _snake_case      :							List[Any]     				=						_quantize(lowercase_      ,		lowercase_  )
       # count number of elements per group
       _snake_case      :							int     				=						np.unique(lowercase_      ,		return_counts=lowercase_  )[1]
       _snake_case      :							Union[str, Any]     				=						[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     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Any:



             '''simple docstring'''

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



      def __iter__(					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						defaultdict(lowercase_					)
             _snake_case      :							int     				=						defaultdict(lowercase_					)

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



      def __len__(					self					)      ->   int:



             '''simple docstring'''

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













def 						A_			(  lowercase_  )			->						list:
       if len(lowercase_  ) <= 1:
              return data
       else:
              return [
                  *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                  data[0],
                  *quick_sort([e for e in data[1:] if e > data[0]]  ),
              ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






