





'''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










'''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)



