




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_									=	logging.get_logger(__name__)





@add_end_docstrings(__lowerCamelCase)
class     lowerCamelCase__(					__lowerCamelCase):
      def __init__(				self: List[str]    ,       *UpperCamelCase_: Any    ,       **UpperCamelCase_: Union[str, Any]   ):
             super().__init__(*UpperCamelCase_    ,       **UpperCamelCase_   )
             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 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Tuple=None   ):
             __lowerCamelCase   =						{}
             if top_k is not None:
                    __lowerCamelCase   =						top_k
             return {}, {}, postprocess_params
      def __call__(				self: Tuple    ,       UpperCamelCase_: Union[str, List[str], "Image.Image", List["Image.Image"]]    ,       **UpperCamelCase_: Optional[int]   ):
             return super().__call__(UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Union[str, Any]   ):
             __lowerCamelCase   =						load_image(UpperCamelCase_   )
             __lowerCamelCase   =						self.image_processor(images=UpperCamelCase_    ,       return_tensors=self.framework   )
             return model_inputs
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Any   ):
             __lowerCamelCase   =						self.model(**UpperCamelCase_   )
             return model_outputs




      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Union[str, Any]=5   ):
             if top_k > self.model.config.num_labels:
                    __lowerCamelCase   =						self.model.config.num_labels

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

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









'''simple docstring'''







from __future__ import annotations

_A	:			Any         ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}

class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				dict[str, list[str]]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	str         =       graph
														# mapping node to its parent in resulting breadth first tree
														lowerCamelCase__					:	dict[str, str | None]         =       {}
														lowerCamelCase__					:	Any         =       source_vertex




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	List[str]         =       {self.source_vertex}
														lowerCamelCase__					:	List[str]         =       None
														lowerCamelCase__					:	Tuple         =       [self.source_vertex]  # first in first out queue

														while queue:
																					lowerCamelCase__					:	Tuple         =       queue.pop(0     )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(UpperCamelCase__     )
																																			lowerCamelCase__					:	List[str]         =       vertex
																																			queue.append(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str     ):
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														lowerCamelCase__					:	Tuple         =       self.parent.get(UpperCamelCase__     )
														if target_vertex_parent is None:
																					lowerCamelCase__					:	int         =       (
																					    F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
																					)
																					raise ValueError(UpperCamelCase__     )

														return self.shortest_path(UpperCamelCase__     ) + F'''->{target_vertex}'''


if __name__ == "__main__":
			_A	:			int         =Graph(graph, '''G''')
			g.breath_first_search()
			print(g.shortest_path('''D'''))
			print(g.shortest_path('''G'''))
			print(g.shortest_path('''Foo'''))

