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__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[Any]			    =    copy.deepcopy(_snake_case						)
    _lowerCamelCase  :		List[Any]			    =    sorted(_snake_case						)
    _lowerCamelCase  :		str			    =    [bisect.bisect_right(_snake_case ,			_snake_case						) for y in x]
    return quantized







def     A__	(       __A ,			__A=0						):







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




class 						__snake_case						(			a__		):





      '''simple docstring'''






      def __init__(       self  :					List[str]     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Dict)					->Tuple:


          """simple docstring"""


          if not isinstance(UpperCamelCase_     ,	UpperCamelCase_):
              raise ValueError(
                  """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCamelCase_))
          _lowerCamelCase  :		int			    =    sampler
          _lowerCamelCase  :		Optional[Any]			    =    group_ids
          _lowerCamelCase  :		Union[str, Any]			    =    batch_size



      def __iter__(       self  :					Dict)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    defaultdict(UpperCamelCase_)
          _lowerCamelCase  :		List[str]			    =    defaultdict(UpperCamelCase_)

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




      def __len__(       self  :					Tuple)					->Optional[Any]:


          """simple docstring"""


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








"""simple docstring"""





def   _snake_case    (    _snake_case					:      int      ):
      if not isinstance(_snake_case			, _snake_case      ):
            lowerCAmelCase       :						int							= f'''Input value of [number={number}] must be an integer'''
            raise TypeError(_snake_case      )
      if number < 0 or number % 2 == 1:
            return False
      lowerCAmelCase       :						int							= int(number**0.5      )
      return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


