

from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

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

_a:    Union[str, Any] 							=			logging.get_logger(__name__)

@add_end_docstrings(lowercase						)
class 						__UpperCamelCase    (					lowercase						):




	def __init__(		self	:						Union[str, Any]		,	*lowerCAmelCase	:						Union[str, Any]		,	**lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		super().__init__(*lowerCAmelCase		,	**lowerCAmelCase							)
		self.check_model_type(lowerCAmelCase							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						List[Any]=None		,	lowerCAmelCase	:						Optional[Any]=None		,	lowerCAmelCase	:						Dict=None		,	**lowerCAmelCase	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			{}, {}
		if padding is not None:
			UpperCAmelCase_       				=			padding
		if truncation is not None:
			UpperCAmelCase_       				=			truncation
		if top_k is not None:
			UpperCAmelCase_       				=			top_k
		return preprocess_params, {}, postprocess_params




	def __call__(		self	:						int		,	lowerCAmelCase	:						Union["Image.Image", str]		,	lowerCAmelCase	:						str = None		,	**lowerCAmelCase	:						Dict							):


		'''simple docstring'''







		if isinstance(lowerCAmelCase		,	(Image.Image, str)							) and isinstance(lowerCAmelCase		,	lowerCAmelCase							):
			UpperCAmelCase_       				=			{"image": image, "question": question}
		else:
			UpperCAmelCase_       				=			image
		UpperCAmelCase_       				=			super().__call__(lowerCAmelCase		,	**lowerCAmelCase							)
		return results




	def       __A					(		self	:						str		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[str]=False		,	lowerCAmelCase	:						int=False							):


		'''simple docstring'''







		UpperCAmelCase_       				=			load_image(inputs["image"]							)
		UpperCAmelCase_       				=			self.tokenizer(
		    inputs["question"]		,	return_tensors=self.framework		,	padding=lowerCAmelCase		,	truncation=lowerCAmelCase							)
		UpperCAmelCase_       				=			self.image_processor(images=lowerCAmelCase		,	return_tensors=self.framework							)
		model_inputs.update(lowerCAmelCase							)
		return model_inputs




	def       __A					(		self	:						int		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model(**lowerCAmelCase							)
		return model_outputs




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Union[str, Any]=5							):


		'''simple docstring'''







		if top_k > self.model.config.num_labels:
			UpperCAmelCase_       				=			self.model.config.num_labels

		if self.framework == "pt":
			UpperCAmelCase_       				=			model_outputs.logits.sigmoid()[0]
			UpperCAmelCase_				,   UpperCAmelCase_       				=			probs.topk(lowerCAmelCase							)
		else:
			raise ValueError(F"Unsupported framework: {self.framework}"							)

		UpperCAmelCase_       				=			scores.tolist()
		UpperCAmelCase_       				=			ids.tolist()
		return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase		,	lowerCAmelCase							)]


from __future__ import annotations



def       __lowerCAmelCase      (						A       ,     A    ):
	print(F"Vertex\tShortest Distance from vertex {src}"    )
	for i, d in enumerate(A    ):
		print(F"{i}\t\t{d}"    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	for j in range(A    ):
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])
		if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
			return True
	return False



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[float("inf"    )] * vertex_count
	UpperCAmelCase_       				=			0.0

	for _ in range(vertex_count - 1    ):
		for j in range(A    ):
			UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])

			if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
				UpperCAmelCase_       				=			distance[u] + w

	UpperCAmelCase_       				=			check_negative_cycle(A       ,     A       ,     A    )
	if negative_cycle_exists:
		raise Exception("Negative cycle found"    )

	return distance


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a:    List[str] 							=			int(input("""Enter number of vertices: """).strip())
				_a:    str 							=			int(input("""Enter number of edges: """).strip())

				_a:    list[dict[str, int]] 							=			[{} for _ in range(E)]

				for i in range(E):
								print("""Edge """, i + 1)
								_a							,			_a							,			_a:    Any 							=			(
								    int(x)
								    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
								)
								_a:    str 							=			{"""src""": src, """dst""": dest, """weight""": weight}

				_a:    List[Any] 							=			int(input("""\nEnter shortest path source:""").strip())
				_a:    Optional[int] 							=			bellman_ford(graph, V, E, source)
				print_distance(shortest_distance, 0)