


from queue import PriorityQueue
from typing import Any

import numpy as np




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     ):





				for nxt, d in graph[v]:
								if nxt in visited_forward:
												continue
								__magic_name__	:	int								=cst_fwd.get(UpperCamelCase__	,     np.inf   )
								__magic_name__	:	str								=cst_fwd[v] + d
								if new_cost_f < old_cost_f:
												queue.put((new_cost_f, nxt)   )
												__magic_name__	:	List[str]								=new_cost_f
												__magic_name__	:	Tuple								=v
								if nxt in visited_backward:
												if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																__magic_name__	:	Optional[Any]								=cst_fwd[v] + d + cst_bwd[nxt]
				return shortest_distance




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Dict								=-1

				__magic_name__	:	Union[str, Any]								=set()
				__magic_name__	:	Tuple								=set()
				__magic_name__	:	Optional[Any]								={source: 0}
				__magic_name__	:	Tuple								={destination: 0}
				__magic_name__	:	int								={source: None}
				__magic_name__	:	List[str]								={destination: None}
				__magic_name__	:	int								=PriorityQueue()
				__magic_name__	:	List[str]								=PriorityQueue()

				__magic_name__	:	Union[str, Any]								=np.inf

				queue_forward.put((0, source)   )
				queue_backward.put((0, destination)   )

				if source == destination:
								return 0

				while not queue_forward.empty() and not queue_backward.empty():
								__magic_name__				, __magic_name__	:	Dict								=queue_forward.get()
								visited_forward.add(UpperCamelCase__   )

								__magic_name__				, __magic_name__	:	List[str]								=queue_backward.get()
								visited_backward.add(UpperCamelCase__   )

								__magic_name__	:	Tuple								=pass_and_relaxation(
								    UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     )

								__magic_name__	:	List[str]								=pass_and_relaxation(
								    UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     )

								if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
												break

				if shortest_distance != np.inf:
								__magic_name__	:	List[str]								=shortest_distance
				return shortest_path_distance


UpperCAmelCase_		:				Union[str, Any]									=     {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
UpperCAmelCase_		:				Dict									=     {
    "B": [["E", 1]],
    "C": [["B", 1]],
    "D": [["C", 1]],
    "F": [["D", 1], ["G", 1]],
    "E": [[None, np.inf]],
    "G": [["E", 2]],
}

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

UpperCAmelCase_		:				str									=     logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__			)
class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Any    , *__snake_case   :List[Any]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										super().__init__(*__snake_case    , **__snake_case )
										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     A__						(		self   :int    , __snake_case   :Optional[Any]=None ):
										'''simple docstring'''

										__magic_name__	:	Any								={}
										if top_k is not None:
														__magic_name__	:	str								=top_k
										return {}, {}, postprocess_params

						def __call__(		self   :Tuple    , __snake_case   :Union[str, List[str], "Image.Image", List["Image.Image"]]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :str    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=load_image(__snake_case )
										__magic_name__	:	Any								=self.image_processor(images=__snake_case    , return_tensors=self.framework )
										return model_inputs

						def     A__						(		self   :int    , __snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model(**__snake_case )
										return model_outputs


						def     A__						(		self   :List[str]    , __snake_case   :int    , __snake_case   :Union[str, Any]=5 ):
										'''simple docstring'''

										if top_k > self.model.config.num_labels:
														__magic_name__	:	Dict								=self.model.config.num_labels

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

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




