code: 
  
  
  
  
  
  
  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	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> int:
  
  					'''simple docstring'''
  					UpperCAmelCase__			:     Union[str, Any]										= copy.deepcopy(__lowerCamelCase					)
  					UpperCAmelCase__			:     Any										= sorted(__lowerCamelCase					)
  					UpperCAmelCase__			:     Optional[int]										= [bisect.bisect_right(__lowerCamelCase   ,       __lowerCamelCase					) for y in x]
  					return quantized
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase=0					)   -> int:
  
  					'''simple docstring'''
  					UpperCAmelCase__			:     Dict										= np.arange(start=3   ,       stop=__lowerCamelCase   ,       step=4					).tolist() if k > 0 else [10]
  					UpperCAmelCase__			:     Union[str, Any]										= _quantize(__lowerCamelCase   ,       __lowerCamelCase					)
  					# count number of elements per group
  					UpperCAmelCase__			:     Any										= np.unique(__lowerCamelCase   ,       return_counts=__lowerCamelCase					)[1]
  					UpperCAmelCase__			:     List[Any]										= [0] + bins + [np.inf]
  					logger.info("""Using {} as bins for aspect lengths quantization""".format(__lowerCamelCase					)					)
  					logger.info("""Count of instances per bin: {}""".format(__lowerCamelCase					)					)
  					return groups
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
  					def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
  										if not isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
  															raise ValueError(
  															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(_lowerCAmelCase		)		)
  										UpperCAmelCase__			:     List[str]										= sampler
  										UpperCAmelCase__			:     Any										= group_ids
  										UpperCAmelCase__			:     List[str]										= batch_size
  
  
  
  
  					def __iter__(							self		):
  										UpperCAmelCase__			:     List[str]										= defaultdict(_lowerCAmelCase		)
  										UpperCAmelCase__			:     str										= defaultdict(_lowerCAmelCase		)
  
  										UpperCAmelCase__			:     int										= 0
  										for idx in self.sampler:
  															UpperCAmelCase__			:     Union[str, Any]										= self.group_ids[idx]
  															buffer_per_group[group_id].append(_lowerCAmelCase		)
  															samples_per_group[group_id].append(_lowerCAmelCase		)
  															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__			:     Union[str, Any]										= len(self		)
  										UpperCAmelCase__			:     List[Any]										= expected_num_batches - num_batches
  										if num_remaining > 0:
  															# for the remaining batches, group the batches by similar lengths
  															UpperCAmelCase__			:     List[Any]										= []
  															for group_id, idxs in sorted(buffer_per_group.items()  ,	key=lambda _lowerCAmelCase		: x[0]		):
  																				batch_idx.extend(_lowerCAmelCase		)
  																				if len(_lowerCAmelCase		) >= self.batch_size:
  																									yield batch_idx[: self.batch_size]
  																									UpperCAmelCase__			:     Tuple										= batch_idx[self.batch_size :]
  																									num_remaining -= 1
  															if len(_lowerCAmelCase		) > 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
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from .stack import Stack
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> bool:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Stack[str]										= Stack()
       UpperCAmelCase__			:     str										= {"""(""": """)""", """[""": """]""", """{""": """}"""}
       for bracket in parentheses:
            if bracket in bracket_pairs:
                 stack.push(__lowerCamelCase					)
            elif bracket in (")", "]", "}"):
                 if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                      return False
       return stack.is_empty()
  
  
  if __name__ == "__main__":
        from doctest import testmod
  
        testmod()
  
        SCREAMING_SNAKE_CASE__		:       Any   							=							["""((()))""", """((())""", """(()))"""]
        print("""Balanced parentheses demonstration:\n""")
        for example in examples:
              SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""""" if balanced_parentheses(example) else """not """
              print(f'''{example} is {not_str}balanced''')
  
  
  
  
  
  
  