



import bisect
import copy
from collections import defaultdict

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

from utils import logger



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		List[str]			,    SCREAMING_SNAKE_CASE__    :		Optional[Any]			)					->       List[str]:
 _snake_case      :					Tuple					=   copy.deepcopy(SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Dict					=   sorted(SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Any					=   [bisect.bisect_right(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			) for y in x]
 return quantized



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



   '''simple docstring'''






   def __init__(   self							:     Optional[Any]			,						lowerCAmelCase							:     List[Any]			,						lowerCAmelCase							:     List[Any]			,						lowerCAmelCase							:     Any)			->			List[Any]:

    """simple docstring"""







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

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

    """simple docstring"""







    _snake_case      :					List[str]					=   defaultdict(lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   defaultdict(lowerCAmelCase)

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

    """simple docstring"""







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





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""recursive_lucas_number accepts only integer arguments."""			)
 if n_th_number == 0:
  return 2
 if n_th_number == 1:
  return 1

 return recursive_lucas_number(n_th_number - 1			) + recursive_lucas_number(
     n_th_number - 2			)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""dynamic_lucas_number accepts only integer arguments."""			)
 _snake_case		,      _snake_case      :					Any					=   2, 1
 for _ in range(SCREAMING_SNAKE_CASE__			):
  _snake_case		,      _snake_case      :					Union[str, Any]					=   b, a + b
 return a


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		int(input("""Enter the number of terms in lucas series:\n""").strip())
  print("""Using recursive function to calculate lucas series:""")
  print(""" """.join(str(recursive_lucas_number(i)) for i in range(n)))
  print("""\nUsing dynamic function to calculate lucas series:""")
  print(""" """.join(str(dynamic_lucas_number(i)) for i in range(n)))
