


"""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   _snake_case    (    _snake_case					:      List[Any]			, _snake_case					:      Tuple      ):
      lowerCAmelCase       :						Optional[Any]							= copy.deepcopy(_snake_case      )
      lowerCAmelCase       :						List[Any]							= sorted(_snake_case      )
      lowerCAmelCase       :						str							= [bisect.bisect_right(_snake_case			, _snake_case      ) for y in x]
      return quantized


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






class 		snake_case_(							a__      ):






     def __init__(						self	:       List[str]       ,					UpperCamelCase_	:       List[Any]       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       Dict					):
           if not isinstance(UpperCamelCase_       ,					UpperCamelCase_					):
                 raise ValueError(
                     '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(UpperCamelCase_					)					)
           lowerCAmelCase       :						int							= sampler
           lowerCAmelCase       :						Optional[Any]							= group_ids
           lowerCAmelCase       :						Union[str, Any]							= batch_size






     def __iter__(						self	:       Dict					):
           lowerCAmelCase       :						List[Any]							= defaultdict(UpperCamelCase_					)
           lowerCAmelCase       :						List[str]							= defaultdict(UpperCamelCase_					)

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






"""simple docstring"""





from collections.abc import Callable

snake_case__		:  Optional[int]     					=					Callable[[float], float]  # type alias for a real -> real function


def   _snake_case    (    _snake_case					:      RealFunc			, _snake_case					:      RealFunc			, _snake_case					:      int			, ):
      lowerCAmelCase       :						Dict							= float(_snake_case      )
      while True:
            try:
                  lowerCAmelCase       :						Optional[Any]							= prev_guess - function(_snake_case      ) / derivative(_snake_case      )
            except ZeroDivisionError:
                  raise ZeroDivisionError('''Could not find root'''      ) from None
            if abs(prev_guess - next_guess      ) < 10**-5:
                  return next_guess
            lowerCAmelCase       :						Optional[int]							= next_guess


def   _snake_case    (    _snake_case					:      float      ):
      return (x**3) - (2 * x) - 5


def   _snake_case    (    _snake_case					:      float      ):
      return 3 * (x**2) - 2


if __name__ == "__main__":
 print(newton(f, fa, 3))


