






"""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     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__:    str        =      copy.deepcopy(snake_case_		)
       lowercase__:    Any        =      sorted(snake_case_		)
       lowercase__:    str        =      [bisect.bisect_right(snake_case_						,       snake_case_		) for y in x]
       return quantized






def     A(  snake_case_						,       snake_case_=0		):
       """simple docstring"""





       lowercase__:    List[Any]        =      np.arange(start=3						,       stop=snake_case_						,       step=4		).tolist() if k > 0 else [10]
       lowercase__:    str        =      _quantize(snake_case_						,       snake_case_		)
       # count number of elements per group
       lowercase__:    str        =      np.unique(snake_case_						,       return_counts=snake_case_		)[1]
       lowercase__:    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 		_a       (						lowercase_   ):





   '''simple docstring'''






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




          '''simple docstring'''




          if not isinstance(UpperCAmelCase_			,				UpperCAmelCase_):
                 raise ValueError(
                     "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(UpperCAmelCase_))
          lowercase__:    List[str]        =      sampler
          lowercase__:    Optional[int]        =      group_ids
          lowercase__:    List[str]        =      batch_size





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




          '''simple docstring'''




          lowercase__:    Optional[Any]        =      defaultdict(UpperCAmelCase_)
          lowercase__:    List[Any]        =      defaultdict(UpperCAmelCase_)

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




          '''simple docstring'''




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














"""simple docstring"""





from __future__ import annotations






def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__  ,     lowercase__:    Optional[int]        =      0, len(snake_case_		) - 1
       while start_ind <= end_ind:
              if array[start_ind] == search_item:
                     return start_ind
              elif array[end_ind] == search_item:
                     return end_ind
              else:
                     start_ind += 1
                     end_ind -= 1
    # returns -1 if search_item is not found in array
       return -1


if __name__ == "__main__":
  print(double_linear_search(list(range(1_00)), 40))






