






"""simple docstring"""



import numpy as np

from transformers import Pipeline

def 			_snake_case    (					lowerCamelCase__     :   Optional[int]  )    ->     Optional[Any]:
							lowerCamelCase_	:							Tuple									=np.max(lowerCamelCase__    ,						axis=-1    ,						keepdims=lowerCamelCase__  )
							lowerCamelCase_	:							Optional[int]									=np.exp(outputs - maxes  )
							return shifted_exp / shifted_exp.sum(axis=-1    ,						keepdims=lowerCamelCase__  )







class        lowercase__      (							snake_case__       ):


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	**snake_case__					:  Any ):
										lowerCamelCase_	:							List[Any]									={}
										if "second_text" in kwargs:
																	lowerCamelCase_	:							Any									=kwargs["second_text"]
										return preprocess_kwargs, {}, {}


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  Dict=None ):
										return self.tokenizer(snake_case__       ,	text_pair=snake_case__       ,	return_tensors=self.framework )


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  Tuple ):
										return self.model(**snake_case__ )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Dict ):
										lowerCamelCase_	:							int									=model_outputs.logits[0].numpy()
										lowerCamelCase_	:							Any									=softmax(snake_case__ )

										lowerCamelCase_	:							str									=np.argmax(snake_case__ )
										lowerCamelCase_	:							Dict									=self.model.config.idalabel[best_class]
										lowerCamelCase_	:							Tuple									=probabilities[best_class].item()
										lowerCamelCase_	:							str									=logits.tolist()
										return {"label": label, "score": score, "logits": logits}











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   list[int]  )    ->     bool:

							# 1. Validate that path exists between current and next vertices
							if graph[path[curr_ind - 1]][next_ver] == 0:
														return False

							# 2. Validate that next vertex is not already in path
							return not any(vertex == next_ver for vertex in path  )

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   list[int]    ,						lowerCamelCase__     :   int  )    ->     bool:

							# Base Case
							if curr_ind == len(lowerCamelCase__  ):
														# return whether path exists between current and starting vertices
														return graph[path[curr_ind - 1]][path[0]] == 1

							# Recursive Step
							for next_ver in range(0    ,						len(lowerCamelCase__  )  ):
														if valid_connection(lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__  ):
																					# Insert current vertex  into path as next transition
																					lowerCamelCase_	:							Tuple									=next_ver
																					# Validate created path
																					if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						curr_ind + 1  ):
																												return True
																					# Backtrack
																					lowerCamelCase_	:							int									=-1
							return False

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int = 0  )    ->     list[int]:
							lowerCamelCase_	:							Optional[Any]									=[-1] * (len(lowerCamelCase__  ) + 1)
							# initialize start and end of path with starting index
							lowerCamelCase_	:							Optional[int]									=start_index
							# evaluate and if we find answer return path either return empty array
							return path if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						1  ) else []



