


'''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(_UpperCAmelCase    )
class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	int:
         super().__init__(**SCREAMING_SNAKE_CASE_      )

         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     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         return super().__call__(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      Dict														=						{}
         if "candidate_labels" in kwargs:
              __lowerCamelCase  :      List[str]														=						kwargs['candidate_labels']
         if "hypothesis_template" in kwargs:
              __lowerCamelCase  :      Tuple														=						kwargs['hypothesis_template']

         return preprocess_params, {}, {}







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="This is a photo of {}."      )							->	Tuple:
         __lowerCamelCase  :      Union[str, Any]														=						load_image(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						self.image_processor(images=[image]     ,	return_tensors=self.framework      )
         __lowerCamelCase  :      Optional[Any]														=						candidate_labels
         __lowerCamelCase  :      int														=						[hypothesis_template.format(SCREAMING_SNAKE_CASE_      ) for x in candidate_labels]
         __lowerCamelCase  :      Union[str, Any]														=						self.tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors=self.framework     ,	padding=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[str]														=						[text_inputs]
         return inputs







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         __lowerCamelCase  :      Dict														=						model_inputs.pop('candidate_labels'      )
         __lowerCamelCase  :      int														=						model_inputs.pop('text_inputs'      )
         if isinstance(text_inputs[0]     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      List[Any]														=						text_inputs[0]
         else:
              # Batching case.
              __lowerCamelCase  :      Optional[Any]														=						text_inputs[0][0]

         __lowerCamelCase  :      Optional[int]														=						self.model(**SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						{
             'candidate_labels': candidate_labels,
             'logits': outputs.logits_per_image,
         }
         return model_outputs







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         __lowerCamelCase  :      str														=						model_outputs.pop('candidate_labels'      )
         __lowerCamelCase  :      Union[str, Any]														=						model_outputs['logits'][0]
         if self.framework == "pt":
              __lowerCamelCase  :      List[Any]														=						logits.softmax(dim=-1      ).squeeze(-1      )
              __lowerCamelCase  :      Dict														=						probs.tolist()
              if not isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                   __lowerCamelCase  :      List[Any]														=						[scores]
         elif self.framework == "tf":
              __lowerCamelCase  :      Union[str, Any]														=						stable_softmax(SCREAMING_SNAKE_CASE_     ,	axis=-1      )
              __lowerCamelCase  :      Optional[Any]														=						probs.numpy().tolist()
         else:
              raise ValueError(f'Unsupported framework: {self.framework}'      )

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










'''simple docstring'''



from __future__ import annotations

import os
from collections.abc import Mapping

A__						:   Optional[Any]				=    tuple[int, int]





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         __lowerCamelCase  :      set[int]														=						vertices
         __lowerCamelCase  :      dict[EdgeT, int]														=						{
             (min(SCREAMING_SNAKE_CASE_      ), max(SCREAMING_SNAKE_CASE_      )): weight for edge, weight in edges.items()
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         self.vertices.add(edge[0]      )
         self.vertices.add(edge[1]      )
         __lowerCamelCase  :      Union[str, Any]														=						weight







    def  lowercase_							(				self      )							->	Graph:
         __lowerCamelCase  :      Graph														=						Graph({min(self.vertices      )}     ,	{}      )
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int

         while len(subgraph.vertices      ) < len(self.vertices      ):
              __lowerCamelCase  :      Any														=						max(self.edges.values()      ) + 1
              for edge, weight in self.edges.items():
                   if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                        if weight < min_weight:
                             __lowerCamelCase  :      Optional[int]														=						edge
                             __lowerCamelCase  :      List[str]														=						weight

              subgraph.add_edge(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         return subgraph







def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "p107_network.txt"				)	->						int:
     __lowerCamelCase  :      str														=						os.path.abspath(os.path.dirname(UpperCAmelCase_				)				)
     __lowerCamelCase  :      str														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      dict[EdgeT, int]														=						{}
     __lowerCamelCase  :      list[str]
     __lowerCamelCase  :      int
     __lowerCamelCase  :      int

     with open(UpperCAmelCase_				) as f:
          __lowerCamelCase  :      Any														=						f.read().strip().split('\n'				)

     __lowerCamelCase  :      Any														=						[line.split(','				) for line in data]

     for edgea in range(1 ,       len(UpperCAmelCase_				)				):
          for edgea in range(UpperCAmelCase_				):
               if adjaceny_matrix[edgea][edgea] != "-":
                    __lowerCamelCase  :      int														=						int(adjaceny_matrix[edgea][edgea]				)

     __lowerCamelCase  :      Graph														=						Graph(set(range(len(UpperCAmelCase_				)				)				) ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Graph														=						graph.prims_algorithm()

     __lowerCamelCase  :      int														=						sum(graph.edges.values()				)
     __lowerCamelCase  :      int														=						sum(subgraph.edges.values()				)

     return initial_total - optimal_total


if __name__ == "__main__":
      print(f'''{solution() = }''')






