

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_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase=0  )							->			Dict:



  """simple docstring"""






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



class 						lowercase_					(						__lowercase  ):



      def __init__(    self   :       Union[str, Any]    ,      A__   :       Tuple    ,      A__   :       List[str]    ,      A__   :       int	)					->  Tuple:
        if not isinstance(UpperCAmelCase_    ,      UpperCAmelCase_	):
          raise ValueError(
              '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(UpperCAmelCase_	)	)
        _snake_case		=							sampler
        _snake_case		=							group_ids
        _snake_case		=							batch_size



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

        _snake_case		=							0
        for idx in self.sampler:
          _snake_case		=							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
        _snake_case		=							len(self	)
        _snake_case		=							expected_num_batches - num_batches
        if num_remaining > 0:
          # for the remaining batches, group the batches by similar lengths
          _snake_case		=							[]
          for group_id, idxs in sorted(buffer_per_group.items()    ,      key=lambda A__	: x[0]	):
            batch_idx.extend(UpperCAmelCase_	)
            if len(UpperCAmelCase_	) >= self.batch_size:
              yield batch_idx[: self.batch_size]
              _snake_case		=							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   :       Optional[int]	)					->  Any:
        return (len(self.sampler	) + self.batch_size - 1) // self.batch_size




from collections import namedtuple

__A    	=			namedtuple('''from_to''', '''from_ to''')

__A    	=			{
    '''atm''': from_to(1, 1),
    '''pascal''': from_to(0.000_0098, 10_13_25),
    '''bar''': from_to(0.98_6923, 1.0_1325),
    '''kilopascal''': from_to(0.0098_6923, 101.325),
    '''megapascal''': from_to(9.8_6923, 0.10_1325),
    '''psi''': from_to(0.06_8046, 14.6959),
    '''inHg''': from_to(0.033_4211, 29.9213),
    '''torr''': from_to(0.0013_1579, 7_60),
}



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			float:



  """simple docstring"""






  if from_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  if to_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  return (
      value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

