



"""simple docstring"""







import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		return 1.0 / (1.0 + np.exp(-_outputs				))

def 						snake_case__       (					_lowerCamelCase				)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				np.max(_outputs,  axis=-1,  keepdims=_lowerCamelCase				)
		__lowercase				:		Tuple												=				np.exp(_outputs - maxes				)
		return shifted_exp / shifted_exp.sum(axis=-1,  keepdims=_lowerCamelCase				)




class       lowerCAmelCase__       (			_snake_case    ):


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      """sigmoid"""
				__UpperCAmelCase :     Any       =      """softmax"""
				__UpperCAmelCase :     Optional[int]       =      """none"""





@add_end_docstrings(
    _snake_case  ,			r"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "  ,			)
class       lowerCAmelCase__       (			_snake_case    ):


				"""simple docstring"""





				__UpperCAmelCase :     Any       =      False
				__UpperCAmelCase :     Dict       =      ClassificationFunction.NONE



				def __init__(     self					:	Any    ,   **lowercase__					:	List[Any]						):
						super().__init__(**snake_case_						)

						self.check_model_type(
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						    if self.framework == "tf"
						    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Optional[Any]=None    ,   lowercase__					:	Optional[int]=None    ,   lowercase__					:	Optional[int]=""    ,   **lowercase__					:	List[str]						):
						# Using "" as default argument because we're going to use `top_k=None` in user code to declare
						# "No top_k"
						__lowercase				:		str												=				tokenizer_kwargs

						__lowercase				:		str												=				{}
						if hasattr(self.model.config    ,   "return_all_scores"						) and return_all_scores is None:
								__lowercase				:		str												=				self.model.config.return_all_scores

						if isinstance(snake_case_    ,   snake_case_						) or top_k is None:
								__lowercase				:		Optional[Any]												=				top_k
								__lowercase				:		List[str]												=				False
						elif return_all_scores is not None:
								warnings.warn(
								    "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
								    " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."    ,   snake_case_    ,   )
								if return_all_scores:
										__lowercase				:		Optional[int]												=				None
								else:
										__lowercase				:		Dict												=				1

						if isinstance(snake_case_    ,   snake_case_						):
								__lowercase				:		List[Any]												=				ClassificationFunction[function_to_apply.upper()]

						if function_to_apply is not None:
								__lowercase				:		List[str]												=				function_to_apply
						return preprocess_params, {}, postprocess_params



				def __call__(     self					:	Dict    ,   *lowercase__					:	List[Any]    ,   **lowercase__					:	List[Any]						):
						__lowercase				:		Dict												=				super().__call__(*snake_case_    ,   **snake_case_						)
						# TODO try and retrieve it in a nicer way from _sanitize_parameters.
						__lowercase				:		Tuple												=				"top_k" not in kwargs
						if isinstance(args[0]    ,   snake_case_						) and _legacy:
								# This pipeline is odd, and return a list when single item is run
								return [result]
						else:
								return result



				def       snake_case    (     self					:	str    ,   lowercase__					:	Optional[Any]    ,   **lowercase__					:	Optional[int]						):
						__lowercase				:		str												=				self.framework
						if isinstance(snake_case_    ,   snake_case_						):
								return self.tokenizer(**snake_case_    ,   return_tensors=snake_case_    ,   **snake_case_						)
						elif isinstance(snake_case_    ,   snake_case_						) and len(snake_case_						) == 1 and isinstance(inputs[0]    ,   snake_case_						) and len(inputs[0]						) == 2:
								# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
								return self.tokenizer(
								    text=inputs[0][0]    ,   text_pair=inputs[0][1]    ,   return_tensors=snake_case_    ,   **snake_case_						)
						elif isinstance(snake_case_    ,   snake_case_						):
								# This is likely an invalid usage of the pipeline attempting to pass text pairs.
								raise ValueError(
								    "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
								    " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."						)
						return self.tokenizer(snake_case_    ,   return_tensors=snake_case_    ,   **snake_case_						)



				def       snake_case    (     self					:	List[Any]    ,   lowercase__					:	int						):
						return self.model(**snake_case_						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	str    ,   lowercase__					:	List[Any]=None    ,   lowercase__					:	int=1    ,   lowercase__					:	List[str]=True						):
						# `_legacy` is used to determine if we're running the naked pipeline and in backward
						# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
						# the more natural result containing the list.
						# Default value before `set_parameters`
						if function_to_apply is None:
								if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
										__lowercase				:		Dict												=				ClassificationFunction.SIGMOID
								elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
										__lowercase				:		Union[str, Any]												=				ClassificationFunction.SOFTMAX
								elif hasattr(self.model.config    ,   "function_to_apply"						) and function_to_apply is None:
										__lowercase				:		Any												=				self.model.config.function_to_apply
								else:
										__lowercase				:		str												=				ClassificationFunction.NONE

						__lowercase				:		Optional[Any]												=				model_outputs["logits"][0]
						__lowercase				:		Optional[Any]												=				outputs.numpy()

						if function_to_apply == ClassificationFunction.SIGMOID:
								__lowercase				:		Optional[Any]												=				sigmoid(snake_case_						)
						elif function_to_apply == ClassificationFunction.SOFTMAX:
								__lowercase				:		Tuple												=				softmax(snake_case_						)
						elif function_to_apply == ClassificationFunction.NONE:
								__lowercase				:		Dict												=				outputs
						else:
								raise ValueError(f'Unrecognized `function_to_apply` argument: {function_to_apply}'						)

						if top_k == 1 and _legacy:
								return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

						__lowercase				:		List[Any]												=				[
						    {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(snake_case_						)
						]
						if not _legacy:
								dict_scores.sort(key=lambda lowercase__						: x["score"]    ,   reverse=snake_case_						)
								if top_k is not None:
										__lowercase				:		int												=				dict_scores[:top_k]
						return dict_scores






"""simple docstring"""







from __future__ import annotations

import os
from collections.abc import Mapping

__A   :  str					=      tuple[int, int]




class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	set[int]    ,   lowercase__					:	Mapping[EdgeT, int]						):
						__lowercase				:		set[int]												=				vertices
						__lowercase				:		dict[EdgeT, int]												=				{
						    (min(lowercase__						), max(lowercase__						)): weight for edge, weight in edges.items()
						}



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	EdgeT    ,   lowercase__					:	int						):
						self.vertices.add(edge[0]						)
						self.vertices.add(edge[1]						)
						__lowercase				:		Union[str, Any]												=				weight



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Graph												=				Graph({min(self.vertices						)}    ,   {}						)
						__lowercase				:		EdgeT
						__lowercase				:		int
						__lowercase				:		EdgeT
						__lowercase				:		int

						while len(subgraph.vertices						) < len(self.vertices						):
								__lowercase				:		int												=				max(self.edges.values()						) + 1
								for edge, weight in self.edges.items():
										if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
												if weight < min_weight:
														__lowercase				:		Optional[Any]												=				edge
														__lowercase				:		List[str]												=				weight

								subgraph.add_edge(lowercase__    ,   lowercase__						)

						return subgraph





def 						snake_case__       (					_lowerCamelCase = "p107_network.txt"				)			->int:






		"""simple docstring"""


		__lowercase				:		str												=				os.path.abspath(os.path.dirname(_lowerCamelCase				)				)
		__lowercase				:		str												=				os.path.join(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		dict[EdgeT, int]												=				{}
		__lowercase				:		list[str]
		__lowercase				:		int
		__lowercase				:		int

		with open(_lowerCamelCase				) as f:
				__lowercase				:		List[str]												=				f.read().strip().split("\n"				)

		__lowercase				:		Any												=				[line.split(","				) for line in data]

		for edgea in range(1,  len(_lowerCamelCase				)				):
				for edgea in range(_lowerCamelCase				):
						if adjaceny_matrix[edgea][edgea] != "-":
								__lowercase				:		Dict												=				int(adjaceny_matrix[edgea][edgea]				)

		__lowercase				:		Graph												=				Graph(set(range(len(_lowerCamelCase				)				)				),  _lowerCamelCase				)

		__lowercase				:		Graph												=				graph.prims_algorithm()

		__lowercase				:		int												=				sum(graph.edges.values()				)
		__lowercase				:		int												=				sum(subgraph.edges.values()				)

		return initial_total - optimal_total


if __name__ == "__main__":
						print(F"""{solution() = }""")
