





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        __magic_name__	:





      """simple docstring"""



      @staticmethod
      def     SCREAMING_SNAKE_CASE		(				*snake_case							:List[Any]      ,   **snake_case							:Union[str, Any]	):





         '''simple docstring'''




         pass


@is_pipeline_test
@require_vision
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     @require_torch
     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      pipeline(
            model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,   )
        A_       :					List[str]        =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
        A_       :					Optional[int]        =      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"}],
            ]      ,   )

        A_       :					int        =      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							:int	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      pipeline(
            model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,   framework="tf"	)
        A_       :					Dict        =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
        A_       :					Dict        =      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"}]      ,   )

        A_       :					List[Any]        =      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[str]	):





        '''simple docstring'''




        A_       :					Dict        =      pipeline(
            task="zero-shot-image-classification"      ,   model="openai/clip-vit-base-patch32"      ,   )
        # This is an image of 2 cats with remotes and no planes
        A_       :					Union[str, Any]        =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
        A_       :					str        =      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"},
            ]      ,   )

        A_       :					Any        =      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							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					List[str]        =      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
        A_       :					Dict        =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
        A_       :					Union[str, Any]        =      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"},
            ]      ,   )

        A_       :					Optional[Any]        =      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      ,   )







from __future__ import annotations

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int						)			->    list[list[int]]:
   A_       :					list[list[int]]        =      []
   A_       :					list[int]        =      []
   A_       :					Dict        =      0
   A_       :					str        =      sum(_lowerCAmelCase						)
   create_state_space_tree(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
   return result

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			list[list[int]]					,					_lowerCAmelCase			:			int					,					)			->    None:
   if sum(_lowerCAmelCase						) > max_sum or (remaining_nums_sum + sum(_lowerCAmelCase						)) < max_sum:
      return
   if sum(_lowerCAmelCase						) == max_sum:
      result.append(_lowerCAmelCase						)
      return
   for index in range(_lowerCAmelCase					,					len(_lowerCAmelCase						)						):
      create_state_space_tree(
          _lowerCAmelCase					,					_lowerCAmelCase					,					index + 1					,					[*path, nums[index]]					,					_lowerCAmelCase					,					remaining_nums_sum - nums[index]					,					)
_lowerCAmelCase				:   List[Any]						       =						[3, 34, 4, 12, 5, 2]
_lowerCAmelCase				:   Dict						       =						9
_lowerCAmelCase				:   Dict						       =						generate_sum_of_subsets_soln(nums, max_sum)





print(*result)
