

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(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		str:



    """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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=0       )->		List[Any]:



    """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 		__magic_name__ (				snake_case							):






     def __init__(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			str:
         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     )->			str:
         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     )->			Dict:
         return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size



def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Tuple:



    """simple docstring"""






    if not root or root not in binary_tree_mirror_dictionary:
        return
    UpperCamelCase_   ,    UpperCamelCase_          =		binary_tree_mirror_dictionary[root][:2]
    UpperCamelCase_          =		[right_child, left_child]
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       )->		dict:



    """simple docstring"""






    if not binary_tree:
        raise ValueError("binary tree cannot be empty"       )
    if root not in binary_tree:
        UpperCamelCase_          =		f"root {root} is not present in the binary_tree"
        raise ValueError(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		dict(SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Dict      =			{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
 print(F'''Binary tree: {binary_tree}''')
 SCREAMING_SNAKE_CASE						:str      =			binary_tree_mirror(binary_tree, 5)
 print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')
