




'''simple docstring'''



import bisect
import copy
from collections import defaultdict

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

from utils import logger
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int ,  __A       :				Any						)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   copy.deepcopy(__A						)
   _SCREAMING_SNAKE_CASE =   sorted(__A						)
   _SCREAMING_SNAKE_CASE =   [bisect.bisect_right(__A ,  __A						) for y in x]
   return quantized
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[str] ,  __A       :				Optional[int]=0						)				->				Tuple:
   _SCREAMING_SNAKE_CASE =   np.arange(start=3 ,  stop=__A ,  step=4						).tolist() if k > 0 else [10]
   _SCREAMING_SNAKE_CASE =   _quantize(__A ,  __A						)
   # count number of elements per group
   _SCREAMING_SNAKE_CASE =   np.unique(__A ,  return_counts=__A						)[1]
   _SCREAMING_SNAKE_CASE =   [0] + bins + [np.inf]
   logger.info("Using {} as bins for aspect lengths quantization".format(__A						)						)
   logger.info("Count of instances per bin: {}".format(__A						)						)
   return groups







class 	lowercase_					(				A     ):


   """simple docstring"""

   def __init__(					self      :			str						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Union[str, 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					)					)
      _SCREAMING_SNAKE_CASE =   sampler
      _SCREAMING_SNAKE_CASE =   group_ids
      _SCREAMING_SNAKE_CASE =   batch_size



   def __iter__(					self      :			Union[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   defaultdict(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   defaultdict(__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   0
      for idx in self.sampler:
         _SCREAMING_SNAKE_CASE =   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
      _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 __lowerCamelCase					: x[0]					):
            batch_idx.extend(__lowerCamelCase					)
            if len(__lowerCamelCase					) >= self.batch_size:
               yield batch_idx[: self.batch_size]
               _SCREAMING_SNAKE_CASE =   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      :			Union[str, Any]					):

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







'''simple docstring'''



lowerCamelCase_	 =		(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCamelCase_	 =		(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Initialization of sum and number of digits.
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   # Calculation of digits of the number
   while temp > 0:
      number_of_digits += 1
      temp //= 10
   # Dividing number into separate digits and find Armstrong number
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE =   temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Init a "histogram" of the digits
   _SCREAMING_SNAKE_CASE =   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   divmod(__A ,  10						)
      digit_histogram[rem] += 1
      digit_total += 1

   for cnt, i in zip(__A ,  range(len(__A						)						)						):
      total += cnt * i**digit_total

   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False
   _SCREAMING_SNAKE_CASE =   len(str(__A						)						)  # the power that all digits will be raised to
   # check if sum of each digit multiplied expo times is equal to number
   return n == sum(int(__A						) ** expo for i in str(__A						)						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   int(input("Enter an integer to see if it is an Armstrong number: "						).strip()						)
   print(f"""{num} is {"" if armstrong_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if narcissistic_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if pluperfect_number(__A						) else "not "}an Armstrong number."""						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

