




'''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   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=0    ):
    """simple docstring"""






    snake_case_	:      List[Any]            =  np.arange(start=3,						stop=SCREAMING_SNAKE_CASE__,						step=4    ).tolist() if k > 0 else [1_0]
    snake_case_	:      List[Any]            =  _quantize(SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__    )
    # count number of elements per group
    snake_case_	:      Union[str, Any]            =  np.unique(SCREAMING_SNAKE_CASE__,						return_counts=SCREAMING_SNAKE_CASE__    )[1]
    snake_case_	:      int            =  [0] + bins + [np.inf]
    logger.info("Using {} as bins for aspect lengths quantization".format(SCREAMING_SNAKE_CASE__    )    )
    logger.info("Count of instances per bin: {}".format(SCREAMING_SNAKE_CASE__    )    )
    return groups






class 				UpperCAmelCase_			(		UpperCamelCase_   ):





 def __init__(      self	,    lowercase_	,    lowercase_	,    lowercase_):




     if not isinstance(lowercase_	,    lowercase_):
         raise ValueError(
             "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_))
     snake_case_	:      Any            =  sampler
     snake_case_	:      Dict            =  group_ids
     snake_case_	:      Dict            =  batch_size





 def __iter__(      self):
     snake_case_	:      Union[str, Any]            =  defaultdict(lowercase_)
     snake_case_	:      Union[str, Any]            =  defaultdict(lowercase_)

     snake_case_	:      Any            =  0
     for idx in self.sampler:
         snake_case_	:      Any            =  self.group_ids[idx]
         buffer_per_group[group_id].append(lowercase_)
         samples_per_group[group_id].append(lowercase_)
         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
     snake_case_	:      Union[str, Any]            =  len(self)
     snake_case_	:      Dict            =  expected_num_batches - num_batches
     if num_remaining > 0:
         # for the remaining batches, group the batches by similar lengths
         snake_case_	:      Union[str, Any]            =  []
         for group_id, idxs in sorted(buffer_per_group.items()	,    key=lambda lowercase_: x[0]):
             batch_idx.extend(lowercase_)
             if len(lowercase_) >= self.batch_size:
                 yield batch_idx[: self.batch_size]
                 snake_case_	:      str            =  batch_idx[self.batch_size :]
                 num_remaining -= 1
         if len(lowercase_) > 0:
             yield batch_idx
             num_remaining -= 1
     assert num_remaining == 0





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






'''simple docstring'''
def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Optional[int]            =  len(__SCREAMING_SNAKE_CASE    )

    # Set the gap value to be decreased by a factor of 1.3
    # after each iteration
    snake_case_	:      Optional[int]            =  1.3

    # Continue sorting until the gap is 1
    while gap > 1:
        # Decrease the gap value
        snake_case_	:      Optional[Any]            =  int(gap / shrink    )

        # Sort the elements using insertion sort
        for i in range(__SCREAMING_SNAKE_CASE,						len(__SCREAMING_SNAKE_CASE    )    ):
            snake_case_	:      Any            =  collection[i]
            snake_case_	:      int            =  i
            while j >= gap and collection[j - gap] > temp:
                snake_case_	:      Dict            =  collection[j - gap]
                j -= gap
            snake_case_	:      Optional[int]            =  temp

    return collection


if __name__ == "__main__":
  import doctest

  doctest.testmod()




