

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    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Optional[int]:






    '''simple docstring'''
    __UpperCAmelCase			   =     copy.deepcopy(SCREAMING_SNAKE_CASE )
    __UpperCAmelCase			   =     sorted(SCREAMING_SNAKE_CASE )
    __UpperCAmelCase			   =     [bisect.bisect_right(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) for y in x]
    return quantized


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE=0 ) ->       str:






    '''simple docstring'''
    __UpperCAmelCase			   =     np.arange(start=3    ,				stop=SCREAMING_SNAKE_CASE    ,				step=4 ).tolist() if k > 0 else [1_0]
    __UpperCAmelCase			   =     _quantize(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
    # count number of elements per group
    __UpperCAmelCase			   =     np.unique(SCREAMING_SNAKE_CASE    ,				return_counts=SCREAMING_SNAKE_CASE )[1]
    __UpperCAmelCase			   =     [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    A_				( _a  ):


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__       , lowercase__					)       -> Any:
        if not isinstance(lowercase__       , lowercase__					):
            raise ValueError(
                '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(lowercase__					)					)
        __UpperCAmelCase			   =     sampler
        __UpperCAmelCase			   =     group_ids
        __UpperCAmelCase			   =     batch_size


    def __iter__(self					)       -> int:
        __UpperCAmelCase			   =     defaultdict(lowercase__					)
        __UpperCAmelCase			   =     defaultdict(lowercase__					)

        __UpperCAmelCase			   =     0
        for idx in self.sampler:
            __UpperCAmelCase			   =     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
        __UpperCAmelCase			   =     len(self					)
        __UpperCAmelCase			   =     expected_num_batches - num_batches
        if num_remaining > 0:
            # for the remaining batches, group the batches by similar lengths
            __UpperCAmelCase			   =     []
            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]
                    __UpperCAmelCase			   =     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					)       -> str:
        return (len(self.sampler					) + self.batch_size - 1) // self.batch_size






def  __a    (				SCREAMING_SNAKE_CASE ) ->       float:






    '''simple docstring'''
    return -num if num < 0 else num


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_min() arg is an empty sequence''' )
    __UpperCAmelCase			   =     x[0]
    for i in x:
        if abs_val(SCREAMING_SNAKE_CASE ) < abs_val(SCREAMING_SNAKE_CASE ):
            __UpperCAmelCase			   =     i
    return j


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_max() arg is an empty sequence''' )
    __UpperCAmelCase			   =     x[0]
    for i in x:
        if abs(SCREAMING_SNAKE_CASE ) > abs(SCREAMING_SNAKE_CASE ):
            __UpperCAmelCase			   =     i
    return j


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_max_sort() arg is an empty sequence''' )
    return sorted(SCREAMING_SNAKE_CASE    ,				key=SCREAMING_SNAKE_CASE )[-1]


def  __a    (				) ->       Union[str, Any]:






    '''simple docstring'''
    assert abs_val(0 ) == 0
    assert abs_val(3_4 ) == 3_4
    assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0 ) == 1_0_0_0_0_0_0_0_0_0_0_0

    __UpperCAmelCase			   =     [-3, -1, 2, -1_1]
    assert abs_max(SCREAMING_SNAKE_CASE ) == -1_1
    assert abs_max_sort(SCREAMING_SNAKE_CASE ) == -1_1
    assert abs_min(SCREAMING_SNAKE_CASE ) == -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       test_abs_val()
       print(abs_val(-34))  # --> 34




