


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

snake_case__			 =						logging.get_logger(__name__)


@add_end_docstrings(_snake_case)
class 					lowerCAmelCase_ (       _snake_case):




						def __init__(	self						:   Optional[int]   ,     *__A						:   List[str]   ,     **__A						:   Dict						)      ->List[str]:






									"""simple docstring"""






									super().__init__(*snake_case_   ,     **snake_case_						)
									requires_backends(self   ,     "decord"						)
									self.check_model_type(snake_case_						)




						def  _snake_case					(	self						:   Tuple   ,     __A						:   List[str]=None   ,     __A						:   Optional[Any]=None   ,     __A						:   Optional[Any]=None						)      ->List[Any]:






									"""simple docstring"""






									a__ :Optional[Any]						  =    {}
									if frame_sampling_rate is not None:
												a__ :Dict						  =    frame_sampling_rate
									if num_frames is not None:
												a__ :List[str]						  =    num_frames

									a__ :Dict						  =    {}
									if top_k is not None:
												a__ :Any						  =    top_k
									return preprocess_params, {}, postprocess_params




						def __call__(	self						:   List[str]   ,     __A						:   Dict   ,     **__A						:   Dict						)      ->str:






									"""simple docstring"""






									return super().__call__(snake_case_   ,     **snake_case_						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   List[Any]   ,     __A						:   Dict=None   ,     __A						:   List[str]=1						)      ->Any:






									"""simple docstring"""






									if num_frames is None:
												a__ :Optional[int]						  =    self.model.config.num_frames

									if video.startswith("http://"						) or video.startswith("https://"						):
												a__ :str						  =    BytesIO(requests.get(snake_case_						).content						)

									a__ :Optional[int]						  =    VideoReader(snake_case_						)
									videoreader.seek(0						)

									a__ :List[Any]						  =    0
									a__ :Optional[int]						  =    num_frames * frame_sampling_rate - 1
									a__ :Union[str, Any]						  =    np.linspace(snake_case_   ,     snake_case_   ,     num=snake_case_   ,     dtype=np.intaa						)

									a__ :Optional[int]						  =    videoreader.get_batch(snake_case_						).asnumpy()
									a__ :Union[str, Any]						  =    list(snake_case_						)

									a__ :str						  =    self.image_processor(snake_case_   ,     return_tensors=self.framework						)
									return model_inputs




						def  _snake_case					(	self						:   Dict   ,     __A						:   Union[str, Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :List[Any]						  =    self.model(**snake_case_						)
									return model_outputs






						def  _snake_case					(	self						:   Dict   ,     __A						:   Any   ,     __A						:   List[Any]=5						)      ->str:






									"""simple docstring"""






									if top_k > self.model.config.num_labels:
												a__ :Tuple						  =    self.model.config.num_labels

									if self.framework == "pt":
												a__ :List[str]						  =    model_outputs.logits.softmax(-1						)[0]
												a__ :Optional[Any]						  =    probs.topk(snake_case_						)
									else:
												raise ValueError(F'''Unsupported framework: {self.framework}'''						)

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






class 					lowerCAmelCase_ :




						def __init__(	self						:   Optional[int]						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    {}




						def  _snake_case					(	self						:   Tuple						)      ->None:






									"""simple docstring"""






									print(self.vertex						)
									for i in self.vertex:
												print(__A   ,     " -> "   ,     " -> ".join([str(__A						) for j in self.vertex[i]]						)						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   int   ,     __A						:   int						)      ->None:






									"""simple docstring"""






									if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__A						)
									else:
												# else make a new vertex
												a__ :Any						  =    [to_vertex]




						def  _snake_case					(	self						:   Optional[int]						)      ->None:






									"""simple docstring"""






									a__ :List[Any]						  =    [False] * len(self.vertex						)

									# call the recursive helper function
									for i in range(len(self.vertex						)						):
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)






						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   int   ,     __A						:   list						)      ->None:






									"""simple docstring"""






									a__ :Dict						  =    True

									print(__A   ,     end=" "						)

									# Recur for all the vertices that are adjacent to this node
									for i in self.vertex:
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)


if __name__ == "__main__":
		snake_case__			 =						Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('''DFS:''')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3


