





"""simple docstring"""




import heapq as hq
import math
from collections.abc import Iterator
class     UpperCamelCase__:





						def __init__(       self		,__UpperCAmelCase     )					->				int:
													A__	     =							str(id_     )
													A__	     =							None
													A__	     =							None
													A__	     =							[]
													A__	     =							{}  # {vertex:distance}





						def __lt__(       self		,__UpperCAmelCase     )					->				Optional[int]:
													return self.key < other.key





						def __repr__(       self     )					->				Any:
													return self.id





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[Any]:
													self.neighbors.append(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				str:
													A__	     =							weight






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							graph[a - 1].add_neighbor(graph[b - 1]				)
							graph[b - 1].add_neighbor(graph[a - 1]				)
							# add the edges:
							graph[a - 1].add_edge(graph[b - 1]       ,    UpperCamelCase__				)
							graph[b - 1].add_edge(graph[a - 1]       ,    UpperCamelCase__				)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[]
							for u in graph:
														A__	     =							math.inf
														A__	     =							None
							A__	     =							0
							A__	     =							graph[:]
							while q:
														A__	     =							min(UpperCamelCase__				)
														q.remove(UpperCamelCase__				)
														for v in u.neighbors:
																					if (v in q) and (u.edges[v.id] < v.key):
																												A__	     =							u
																												A__	     =							u.edges[v.id]
							for i in range(1       ,    len(UpperCamelCase__				)				):
														a.append((int(graph[i].id				) + 1, int(graph[i].pi.id				) + 1)				)
							return a






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							for u in graph:
														A__	     =							math.inf
														A__	     =							None
							A__	     =							0

							A__	     =							list(UpperCamelCase__				)
							hq.heapify(UpperCamelCase__				)

							while h:
														A__	     =							hq.heappop(UpperCamelCase__				)
														for v in u.neighbors:
																					if (v in h) and (u.edges[v.id] < v.key):
																												A__	     =							u
																												A__	     =							u.edges[v.id]
																												hq.heapify(UpperCamelCase__				)

							for i in range(1       ,    len(UpperCamelCase__				)				):
														yield (int(graph[i].id				) + 1, int(graph[i].pi.id				) + 1)






def   UpperCAmelCase					(	):






							"""simple docstring"""












if __name__ == "__main__":
				import doctest

				doctest.testmod()






"""simple docstring"""




from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
				import numpy as np
				from decord import VideoReader


if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

__lowerCamelCase											=  logging.get_logger(__name__)
@add_end_docstrings(__A )
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )
													requires_backends(self		,'decord'     )
													self.check_model_type(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None     )					->				Any:
													A__	     =							{}
													if frame_sampling_rate is not None:
																				A__	     =							frame_sampling_rate
													if num_frames is not None:
																				A__	     =							num_frames

													A__	     =							{}
													if top_k is not None:
																				A__	     =							top_k
													return preprocess_params, {}, postprocess_params





						def __call__(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
													return super().__call__(__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=None		,__UpperCAmelCase=1     )					->				List[Any]:
													if num_frames is None:
																				A__	     =							self.model.config.num_frames

													if video.startswith('http://'     ) or video.startswith('https://'     ):
																				A__	     =							BytesIO(requests.get(__UpperCAmelCase     ).content     )

													A__	     =							VideoReader(__UpperCAmelCase     )
													videoreader.seek(0     )

													A__	     =							0
													A__	     =							num_frames * frame_sampling_rate - 1
													A__	     =							np.linspace(__UpperCAmelCase		,__UpperCAmelCase		,num=__UpperCAmelCase		,dtype=np.intaa     )

													A__	     =							videoreader.get_batch(__UpperCAmelCase     ).asnumpy()
													A__	     =							list(__UpperCAmelCase     )

													A__	     =							self.image_processor(__UpperCAmelCase		,return_tensors=self.framework     )
													return model_inputs





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[int]:
													A__	     =							self.model(**__UpperCAmelCase     )
													return model_outputs





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=5     )					->				int:
													if top_k > self.model.config.num_labels:
																				A__	     =							self.model.config.num_labels

													if self.framework == "pt":
																				A__	     =							model_outputs.logits.softmax(-1     )[0]
																				A__     ,  A__	     =							probs.topk(__UpperCAmelCase     )
													else:
																				raise ValueError(f'''Unsupported framework: {self.framework}'''     )

													A__	     =							scores.tolist()
													A__	     =							ids.tolist()
													return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCAmelCase		,__UpperCAmelCase     )]





