

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       :		Any			, _SCREAMING_SNAKE_CASE       :		Tuple							):





    """simple docstring"""


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


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Any			, _SCREAMING_SNAKE_CASE       :		Union[str, Any]=0							):





    """simple docstring"""


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

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

  def __iter__(       self):
      SCREAMING_SNAKE_CASE_						=    defaultdict(_A)
      SCREAMING_SNAKE_CASE_						=    defaultdict(_A)

      SCREAMING_SNAKE_CASE_						=    0
      for idx in self.sampler:
          SCREAMING_SNAKE_CASE_						=    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
      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(_A)
              if len(_A) >= self.batch_size:
                  yield batch_idx[: self.batch_size]
                  SCREAMING_SNAKE_CASE_						=    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):
      return (len(self.sampler) + self.batch_size - 1) // self.batch_size





def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list							):





    """simple docstring"""


    if not isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
    if len(_SCREAMING_SNAKE_CASE							) == 0:
        raise ValueError('Input list must be a non empty list'							)
    if len(_SCREAMING_SNAKE_CASE							) == 1:
        return True
    try:
        SCREAMING_SNAKE_CASE_						=    series[1] / series[0]
        for index in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
            if series[index + 1] / series[index] != common_ratio:
                return False
    except ZeroDivisionError:
        return False
    return True


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list							):





    """simple docstring"""


    if not isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
    if len(_SCREAMING_SNAKE_CASE							) == 0:
        raise ValueError('Input list must be a non empty list'							)
    SCREAMING_SNAKE_CASE_						=    1
    for value in series:
        answer *= value
    return pow(_SCREAMING_SNAKE_CASE			, 1 / len(_SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


