






"""simple docstring"""





import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:
  class    __lowerCamelCase    :

     @staticmethod
     def 						SCREAMING_SNAKE_CASE__ (					*snake_case_ ,				**snake_case_      )		->       Optional[Any]:
         pass
@is_pipeline_test
@require_vision
class    __lowerCamelCase    (  unittest.TestCase  ):

   @require_torch
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    pipeline(
           model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' ,				)
       UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
       UpperCamelCase__         =    image_classifier(snake_case_ ,				candidate_labels=['a', 'b', 'c']      )

       # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
       # python and torch versions.
       self.assertIn(
           nested_simplify(snake_case_      ) ,				[
               [{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'b'}, {'score': 0.333, 'label': 'c'}],
               [{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'c'}, {'score': 0.333, 'label': 'b'}],
           ] ,				)

       UpperCamelCase__         =    image_classifier([image] * 5 ,				candidate_labels=['A', 'B', 'C'] ,				batch_size=2      )
       self.assertEqual(
           nested_simplify(snake_case_      ) ,				[
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
           ] ,				)

   @require_tf
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    pipeline(
           model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' ,				framework='tf'      )
       UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
       UpperCamelCase__         =    image_classifier(snake_case_ ,				candidate_labels=['a', 'b', 'c']      )

       self.assertEqual(
           nested_simplify(snake_case_      ) ,				[{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'b'}, {'score': 0.333, 'label': 'c'}] ,				)

       UpperCamelCase__         =    image_classifier([image] * 5 ,				candidate_labels=['A', 'B', 'C'] ,				batch_size=2      )
       self.assertEqual(
           nested_simplify(snake_case_      ) ,				[
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
               [
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
                   {'score': 0.333, 'label': ANY(snake_case_      )},
               ],
           ] ,				)

   @slow
   @require_torch
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    pipeline(
           task='zero-shot-image-classification' ,				model='openai/clip-vit-base-patch32' ,				)
       # This is an image of 2 cats with remotes and no planes
       UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
       UpperCamelCase__         =    image_classifier(snake_case_ ,				candidate_labels=['cat', 'plane', 'remote']      )

       self.assertEqual(
           nested_simplify(snake_case_      ) ,				[
               {'score': 0.511, 'label': 'remote'},
               {'score': 0.485, 'label': 'cat'},
               {'score': 0.004, 'label': 'plane'},
           ] ,				)

       UpperCamelCase__         =    image_classifier([image] * 5 ,				candidate_labels=['cat', 'plane', 'remote'] ,				batch_size=2      )
       self.assertEqual(
           nested_simplify(snake_case_      ) ,				[
               [
                   {'score': 0.511, 'label': 'remote'},
                   {'score': 0.485, 'label': 'cat'},
                   {'score': 0.004, 'label': 'plane'},
               ],
           ]
           * 5 ,				)







   @slow
   @require_tf
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    pipeline(
           task='zero-shot-image-classification' ,				model='openai/clip-vit-base-patch32' ,				framework='tf'      )
       # This is an image of 2 cats with remotes and no planes
       UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
       UpperCamelCase__         =    image_classifier(snake_case_ ,				candidate_labels=['cat', 'plane', 'remote']      )
       self.assertEqual(
           nested_simplify(snake_case_      ) ,				[
               {'score': 0.511, 'label': 'remote'},
               {'score': 0.485, 'label': 'cat'},
               {'score': 0.004, 'label': 'plane'},
           ] ,				)

       UpperCamelCase__         =    image_classifier([image] * 5 ,				candidate_labels=['cat', 'plane', 'remote'] ,				batch_size=2      )
       self.assertEqual(
           nested_simplify(snake_case_      ) ,				[
               [
                   {'score': 0.511, 'label': 'remote'},
                   {'score': 0.485, 'label': 'cat'},
                   {'score': 0.004, 'label': 'plane'},
               ],
           ]
           * 5 ,				)












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

A__					: Any=	TypeVar("""T""")
class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # map from node name to the node object
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       DisjointSetTreeNode[T]:
       # find the set x belongs to (with path-compression)
       UpperCamelCase__         =    self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__         =    self.find_set(elem_ref.parent.data      )
       return elem_ref.parent

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # merge 2 disjoint sets
       self.link(self.find_set(snake_case_      ) ,				self.find_set(snake_case_      )      )







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)      )
                   edges.append((start, end, self.connections[start][end])      )
       edges.sort(key=lambda snake_case_      : x[2]      )

       # creating the disjoint set
       UpperCamelCase__         =    DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(snake_case_      )

       # MST generation
       UpperCamelCase__         =    0
       UpperCamelCase__         =    0
       UpperCamelCase__         =    GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections      ) - 1:
           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    edges[index]
           index += 1
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(snake_case_ ,				snake_case_ ,				snake_case_      )
               disjoint_set.union(snake_case_ ,				snake_case_      )
       return graph




