'''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__			:   Any , UpperCAmelCase__			:   Optional[int]					)     -> Tuple:
							lowercase_   :     Optional[Any]									=			copy.deepcopy(UpperCAmelCase__					)
							lowercase_   :     Tuple									=			sorted(UpperCAmelCase__					)
							lowercase_   :     Tuple									=			[bisect.bisect_right(UpperCAmelCase__ , UpperCAmelCase__					) for y in x]
							return quantized




def 				lowerCamelCase      ( UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   List[str]=0					)     -> int:
							lowercase_   :     Optional[int]									=			np.arange(start=3 , stop=UpperCAmelCase__ , step=4					).tolist() if k > 0 else [10]
							lowercase_   :     Optional[Any]									=			_quantize(UpperCAmelCase__ , UpperCAmelCase__					)
							# count number of elements per group
							lowercase_   :     List[Any]									=			np.unique(UpperCAmelCase__ , return_counts=UpperCAmelCase__					)[1]
							lowercase_   :     Optional[int]									=			[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        __magic_name__		(       _UpperCAmelCase):







			def __init__(      self							:    Optional[Any]							,     lowercase_							:    Any							,     lowercase_							:    Optional[int]							,     lowercase_							:    Optional[int]       ):
										if not isinstance(lowercase_							,     lowercase_       ):
																	raise ValueError(
																	    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowercase_       )       )
										lowercase_   :     Dict									=			sampler
										lowercase_   :     Union[str, Any]									=			group_ids
										lowercase_   :     int									=			batch_size







			def __iter__(      self							:    List[Any]       ):
										lowercase_   :     Optional[Any]									=			defaultdict(lowercase_       )
										lowercase_   :     Optional[Any]									=			defaultdict(lowercase_       )

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






'''simple docstring'''




from __future__ import annotations





class        __magic_name__		:







			def __init__(      self							:    List[str]							,     lowercase_							:    int       ):
										lowercase_   :     List[str]									=			vertices
										lowercase_   :     Optional[Any]									=			[[0] * vertices for _ in range(lowercase_       )]







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    list[int]       ):
										print("""Vertex \t Distance from Source"""       )
										for vertex in range(self.vertices       ):
																	print(lowercase_							,     """\t\t"""							,     distances_from_source[vertex]       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    list[int]							,     lowercase_							:    list[bool]       ):
										lowercase_   :     Dict									=			1E7
										lowercase_   :     int									=			0

										# Search not nearest vertex not in the shortest path tree
										for vertex in range(self.vertices       ):
																	if distances_from_source[vertex] < minimum and visited[vertex] is False:
																								lowercase_   :     Tuple									=			distances_from_source[vertex]
																								lowercase_   :     str									=			vertex
										return min_index







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    int       ):
										lowercase_   :     Optional[Any]									=			[int(1E7       )] * self.vertices  # distances from the source
										lowercase_   :     List[Any]									=			0
										lowercase_   :     Any									=			[False] * self.vertices

										for _ in range(self.vertices       ):
																	lowercase_   :     Tuple									=			self.minimum_distance(lowercase_							,     lowercase_       )
																	lowercase_   :     Union[str, Any]									=			True

																	# Update dist value of the adjacent vertices
																	# of the picked vertex only if the current
																	# distance is greater than new distance and
																	# the vertex in not in the shortest path tree
																	for v in range(self.vertices       ):
																								if (
																								    self.graph[u][v] > 0
																								    and visited[v] is False
																								    and distances[v] > distances[u] + self.graph[u][v]
																								):
																															lowercase_   :     Any									=			distances[u] + self.graph[u][v]

										self.print_solution(lowercase_       )


if __name__ == "__main__":
						_lowercase		:					Any													=		Graph(9)
						_lowercase		:					Any													=		[
						    [0, 4, 0, 0, 0, 0, 0, 8, 0],
						    [4, 0, 8, 0, 0, 0, 0, 11, 0],
						    [0, 8, 0, 7, 0, 4, 0, 0, 2],
						    [0, 0, 7, 0, 9, 14, 0, 0, 0],
						    [0, 0, 0, 9, 0, 10, 0, 0, 0],
						    [0, 0, 4, 14, 10, 0, 2, 0, 0],
						    [0, 0, 0, 0, 0, 2, 0, 1, 6],
						    [8, 11, 0, 0, 0, 0, 1, 0, 7],
						    [0, 0, 2, 0, 0, 0, 6, 7, 0],
						]
						graph.dijkstra(0)





