




'''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'''



import math
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple						)				->				List[Any]:
   _SCREAMING_SNAKE_CASE =   0
   if n % 2 == 0:
      count += 1
      while n % 2 == 0:
         _SCREAMING_SNAKE_CASE =   int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

   _SCREAMING_SNAKE_CASE =   3

   while i <= int(math.sqrt(__A						)						):
      if n % i == 0:
         count += 1
         while n % i == 0:
            _SCREAMING_SNAKE_CASE =   int(n / i						)
      _SCREAMING_SNAKE_CASE =   i + 2

   # this condition checks the prime
   # number n is greater than 2

   if n > 2:
      count += 1
   return count


if __name__ == "__main__":
 lowerCamelCase_	 =		51_24_21_83
 print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
 print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')

