code: 
  
  
  
  
  
  '''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 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      copy.deepcopy(UpperCAmelCase_       )
  	snake_case__  					=      sorted(UpperCAmelCase_       )
  	snake_case__  					=      [bisect.bisect_right(UpperCAmelCase_				,							UpperCAmelCase_       ) for y in x]
  	return quantized
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_=0       )      ->str:
  	snake_case__  					=      np.arange(start=3				,							stop=UpperCAmelCase_				,							step=4       ).tolist() if k > 0 else [10]
  	snake_case__  					=      _quantize(UpperCAmelCase_				,							UpperCAmelCase_       )
  	# count number of elements per group
  	snake_case__  					=      np.unique(UpperCAmelCase_				,							return_counts=UpperCAmelCase_       )[1]
  	snake_case__  					=      [0] + bins + [np.inf]
  	logger.info('Using {} as bins for aspect lengths quantization'.format(UpperCAmelCase_       )       )
  	logger.info('Count of instances per bin: {}'.format(UpperCAmelCase_       )       )
  	return groups
  
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Any:
  		if not isinstance(UpperCamelCase_			,					UpperCamelCase_		):
  			raise ValueError(
  			    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(UpperCamelCase_		)		)
  		snake_case__  					=      sampler
  		snake_case__  					=      group_ids
  		snake_case__  					=      batch_size
  
  
  
  
  
  	def __iter__(		self		)			->		Optional[Any]:
  		snake_case__  					=      defaultdict(UpperCamelCase_		)
  		snake_case__  					=      defaultdict(UpperCamelCase_		)
  
  		snake_case__  					=      0
  		for idx in self.sampler:
  			snake_case__  					=      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
  		snake_case__  					=      len(self		)
  		snake_case__  					=      expected_num_batches - num_batches
  		if num_remaining > 0:
  			# for the remaining batches, group the batches by similar lengths
  			snake_case__  					=      []
  			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]
  					snake_case__  					=      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		)			->		List[str]:
  		return (len(self.sampler		) + self.batch_size - 1) // self.batch_size
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  a__				: Any									=    {
      0: '''underwear''',
      1: '''pants''',
      2: '''belt''',
      3: '''suit''',
      4: '''shoe''',
      5: '''socks''',
      6: '''shirt''',
      7: '''tie''',
      8: '''watch''',
  }
  
  a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  a__				: List[str]									=    [0 for x in range(len(graph))]
  a__				: List[Any]									=    []
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	while stack:
  		snake_case__  					=      stack.pop()
  		print(UpperCAmelCase_				,							clothes[current_clothing]       )
  		order += 1
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	for v in graph[u]:
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	stack.append(UpperCAmelCase_       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
  	for v in range(len(UpperCAmelCase_       )       ):
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  
  if __name__ == "__main__":
  			topological_sort(graph, visited)
  			print(stack)
  			print_stack(stack, clothes)
  
  
  
  