



'''simple docstring'''

from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst







def 						__UpperCamelCase				(							) ->			List[str]:
	_lowercase					:       Optional[int]       =		9, 14  # noqa: F841
	_lowercase					:       int       =		[
	    [0, 1, 4],
	    [0, 7, 8],
	    [1, 2, 8],
	    [7, 8, 7],
	    [7, 6, 1],
	    [2, 8, 2],
	    [8, 6, 6],
	    [2, 3, 7],
	    [2, 5, 4],
	    [6, 5, 2],
	    [3, 5, 14],
	    [3, 4, 9],
	    [5, 4, 10],
	    [1, 7, 11],
	]

	_lowercase					:       int       =		defaultdict(_lowercase					)
	for nodea, nodea, cost in edges:
		adjancency[nodea].append([nodea, cost]					)
		adjancency[nodea].append([nodea, cost]					)

	_lowercase					:       Optional[Any]       =		mst(_lowercase					)

	_lowercase					:       List[str]       =		[
	    [7, 6, 1],
	    [2, 8, 2],
	    [6, 5, 2],
	    [0, 1, 4],
	    [2, 5, 4],
	    [2, 3, 7],
	    [0, 7, 8],
	    [3, 4, 9],
	]

	for answer in expected:
		_lowercase					:       Any       =		tuple(answer[:2]					)
		_lowercase					:       Union[str, Any]       =		tuple(edge[::-1]					)
		assert edge in result or reverse in result





'''simple docstring'''

from collections import UserDict
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_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

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



@add_end_docstrings(A     )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  **UpperCamelCase_				:	List[str]					)  ->       int:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						requires_backends(self     ,  'vision'					)
						self.check_model_type(
						    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						    if self.framework == 'tf'
						    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING					)




					def __call__(  self				:	int     ,  UpperCamelCase_				:	Union[str, List[str], "Image", List["Image"]]     ,  **UpperCamelCase_				:	Tuple					)  ->       List[Any]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  **UpperCamelCase_				:	str					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		{}
						if "candidate_labels" in kwargs:
							_lowercase					:       Union[str, Any]       =		kwargs['candidate_labels']
						if "hypothesis_template" in kwargs:
							_lowercase					:       int       =		kwargs['hypothesis_template']

						return preprocess_params, {}, {}




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[Any]=None     ,  UpperCamelCase_				:	str="This is a photo of {}."					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		load_image(UpperCamelCase_					)
						_lowercase					:       List[str]       =		self.image_processor(images=[image]     ,  return_tensors=self.framework					)
						_lowercase					:       Optional[Any]       =		candidate_labels
						_lowercase					:       List[Any]       =		[hypothesis_template.format(UpperCamelCase_					) for x in candidate_labels]
						_lowercase					:       Union[str, Any]       =		self.tokenizer(UpperCamelCase_     ,  return_tensors=self.framework     ,  padding=UpperCamelCase_					)
						_lowercase					:       Any       =		[text_inputs]
						return inputs




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		model_inputs.pop('candidate_labels'					)
						_lowercase					:       List[str]       =		model_inputs.pop('text_inputs'					)
						if isinstance(text_inputs[0]     ,  UpperCamelCase_					):
							_lowercase					:       Optional[int]       =		text_inputs[0]
						else:
							# Batching case.
							_lowercase					:       List[str]       =		text_inputs[0][0]

						_lowercase					:       Optional[Any]       =		self.model(**UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		{
						    'candidate_labels': candidate_labels,
						    'logits': outputs.logits_per_image,
						}
						return model_outputs




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		model_outputs.pop('candidate_labels'					)
						_lowercase					:       Optional[int]       =		model_outputs['logits'][0]
						if self.framework == "pt":
							_lowercase					:       List[Any]       =		logits.softmax(dim=-1					).squeeze(-1					)
							_lowercase					:       Tuple       =		probs.tolist()
							if not isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       List[Any]       =		[scores]
						elif self.framework == "tf":
							_lowercase					:       Optional[int]       =		stable_softmax(UpperCamelCase_     ,  axis=-1					)
							_lowercase					:       List[Any]       =		probs.numpy().tolist()
						else:
							raise ValueError(F'''Unsupported framework: {self.framework}'''					)

						_lowercase					:       List[Any]       =		[
						    {'score': score, 'label': candidate_label}
						    for score, candidate_label in sorted(zip(UpperCamelCase_     ,  UpperCamelCase_					)     ,  key=lambda UpperCamelCase_					: -x[0]					)
						]
						return result
