






"""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    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







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



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase=0					):




  """simple docstring"""







  __A		     =					np.arange(start=3						,			stop=__UpperCamelCase						,			step=4					).tolist() if k > 0 else [1_0]
  __A		     =					_quantize(__UpperCamelCase						,			__UpperCamelCase					)
  # count number of elements per group
  __A		     =					np.unique(__UpperCamelCase						,			return_counts=__UpperCamelCase					)[1]
  __A		     =					[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    snake_case						(				UpperCamelCase__		):


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Optional[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    )    )
     __A		     =					sampler
     __A		     =					group_ids
     __A		     =					batch_size


   def __iter__(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					defaultdict(_lowerCamelCase    )
     __A		     =					defaultdict(_lowerCamelCase    )

     __A		     =					0
     for idx in self.sampler:
       __A		     =					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
     __A		     =					len(self    )
     __A		     =					expected_num_batches - num_batches
     if num_remaining > 0:
       # for the remaining batches, group the batches by similar lengths
       __A		     =					[]
       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]
           __A		     =					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					:	str    ):

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









'''simple docstring'''



def 	__lowerCAmelCase       (				lowerCamelCase :    list[list[int]]					,		lowerCamelCase :    list[list[int]]       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	[]
	__lowerCAmelCase       				=	len(lowerCamelCase       )
	for i in range(lowerCamelCase       ):
		__lowerCAmelCase       				=	[]
		for j in range(lowerCamelCase       ):
			__lowerCAmelCase       				=	0
			for k in range(lowerCamelCase       ):
				__lowerCAmelCase       				=	val + matrix_a[i][k] * matrix_b[k][j]
			list_a.append(lowerCamelCase       )
		matrix_c.append(lowerCamelCase       )
	return matrix_c



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	return [[int(row == column       ) for column in range(lowerCamelCase       )] for row in range(lowerCamelCase       )]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	identity(2       )
	__lowerCAmelCase       				=	[[1, 1], [1, 0]]
	__lowerCAmelCase       				=	n - 1
	while n > 0:
		if n % 2 == 1:
			__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	int(n / 2       )
	return res_matrix[0][0]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	0
	__lowerCAmelCase       				=	1
	for _ in range(2					,		n + 1       ):
		__lowerCAmelCase				,							__lowerCAmelCase       				=	fiba, fiba + fiba
	return fiba



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
		__lowerCAmelCase       				=	int("".join(c for c in ordinal if c in "0123456789"       )       )  # 1000th --> 1000
		print(
		    f'''{ordinal} fibonacci number using matrix exponentiation is '''
		    f'''{nth_fibonacci_matrix(lowerCamelCase       )} and using bruteforce is '''
		    f'''{nth_fibonacci_bruteforce(lowerCamelCase       )}\n'''       )
	# from timeit import timeit
	# print(timeit("nth_fibonacci_matrix(1000000)",
	#              "from main import nth_fibonacci_matrix", number=5))
	# print(timeit("nth_fibonacci_bruteforce(1000000)",
	#              "from main import nth_fibonacci_bruteforce", number=5))
	# 2.3342058970001744
	# 57.256506615000035


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()