


import heapq as hq
import math
from collections.abc import Iterator

class     a		:




							"""simple docstring"""


							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Any    )     ->   List[Any]:
														__snake_case		:      Dict										=       str(id_    )
														__snake_case		:      List[Any]										=       None
														__snake_case		:      Optional[Any]										=       None
														__snake_case		:      Dict										=       []
														__snake_case		:      List[str]										=       {}  # {vertex:distance}

							def __lt__(							self	:  str   ,     lowerCamelCase	:  Any    )     ->   Union[str, Any]:
														return self.key < other.key

							def __repr__(							self	:  Optional[int]    )     ->   Any:
														return self.id

							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Union[str, Any]    )     ->   Any:
														self.neighbors.append(lowerCamelCase    )






							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  List[Any]    )     ->   Union[str, Any]:
														__snake_case		:      List[str]										=       weight







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# add the neighbors:
							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]							,     __lowerCamelCase     )
							graph[b - 1].add_edge(graph[a - 1]							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[Any]										=       []
							for u in graph:
														__snake_case		:      List[str]										=       math.inf
														__snake_case		:      Optional[Any]										=       None
							__snake_case		:      Union[str, Any]										=       0
							__snake_case		:      str										=       graph[:]
							while q:
														__snake_case		:      Optional[int]										=       min(__lowerCamelCase     )
														q.remove(__lowerCamelCase     )
														for v in u.neighbors:
																					if (v in q) and (u.edges[v.id] < v.key):
																												__snake_case		:      Union[str, Any]										=       u
																												__snake_case		:      Any										=       u.edges[v.id]
							for i in range(1							,     len(__lowerCamelCase     )     ):
														a.append((int(graph[i].id     ) + 1, int(graph[i].pi.id     ) + 1)     )
							return a







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							for u in graph:
														__snake_case		:      Union[str, Any]										=       math.inf
														__snake_case		:      Tuple										=       None
							__snake_case		:      List[Any]										=       0

							__snake_case		:      Tuple										=       list(__lowerCamelCase     )
							hq.heapify(__lowerCamelCase     )

							while h:
														__snake_case		:      int										=       hq.heappop(__lowerCamelCase     )
														for v in u.neighbors:
																					if (v in h) and (u.edges[v.id] < v.key):
																												__snake_case		:      Optional[int]										=       u
																												__snake_case		:      Optional[Any]										=       u.edges[v.id]
																												hq.heapify(__lowerCamelCase     )

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







def     lowerCAmelCase_					( ):
							pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

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

_snake_case					:		Optional[int]      =    logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase				)
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  Tuple    )     ->   int:
														super().__init__(*lowerCamelCase   ,     **lowerCamelCase    )
														requires_backends(self   ,     "vision"    )
														self.check_model_type(
														    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
														    if self.framework == "tf"
														    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING    )

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[Any]=None    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       {}
														if top_k is not None:
																					__snake_case		:      List[Any]										=       top_k
														return {}, {}, postprocess_params

							def __call__(							self	:  List[Any]   ,     lowerCamelCase	:  Union[str, List[str], "Image.Image", List["Image.Image"]]   ,     **lowerCamelCase	:  Dict    )     ->   Optional[int]:
														return super().__call__(lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  List[Any]    )     ->   int:
														__snake_case		:      Any										=       load_image(lowerCamelCase    )
														__snake_case		:      str										=       self.image_processor(images=lowerCamelCase   ,     return_tensors=self.framework    )
														return model_inputs

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  List[str]    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.model(**lowerCamelCase    )
														return model_outputs






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]=5    )     ->   List[str]:
														if top_k > self.model.config.num_labels:
																					__snake_case		:      int										=       self.model.config.num_labels

														if self.framework == "pt":
																					__snake_case		:      Optional[Any]										=       model_outputs.logits.softmax(-1    )[0]
																					__snake_case			,      __snake_case		:      List[str]										=       probs.topk(lowerCamelCase    )
														elif self.framework == "tf":
																					__snake_case		:      Tuple										=       stable_softmax(model_outputs.logits   ,     axis=-1    )[0]
																					__snake_case		:      Optional[Any]										=       tf.math.top_k(lowerCamelCase   ,     k=lowerCamelCase    )
																					__snake_case			,      __snake_case		:      List[Any]										=       topk.values.numpy(), topk.indices.numpy()
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'    )

														__snake_case		:      Any										=       scores.tolist()
														__snake_case		:      Optional[int]										=       ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase   ,     lowerCamelCase    )]

