
"""simple docstring"""



import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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 		a_						:







     @staticmethod
     def      _snake_case					(			*__UpperCamelCase			:       Union[str, Any]     ,					**__UpperCamelCase			:       int     )		->str:






         '''simple docstring'''






         pass




@is_pipeline_test
@require_torch
@require_vision
class 		a_						( unittest.TestCase	):
    a      :				int     =				MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       List[str]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   pipeline("""visual-question-answering"""     ,					model="""hf-internal-testing/tiny-vilt-random-vqa"""     )
        _UpperCAmelCase          =   [
            {
                """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     ),
                """question""": """How many cats are there?""",
            },
            {
                """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
                """question""": """How many cats are there?""",
            },
        ]
        return vqa_pipeline, examples







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[int]     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   vqa_pipeline(__UpperCamelCase     ,					top_k=1     )
        self.assertEqual(
            __UpperCamelCase     ,					[
                [{"""score""": ANY(__UpperCamelCase     ), """answer""": ANY(__UpperCamelCase     )}],
                [{"""score""": ANY(__UpperCamelCase     ), """answer""": ANY(__UpperCamelCase     )}],
            ]     ,					)







    @require_torch
    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   pipeline("""visual-question-answering"""     ,					model="""hf-internal-testing/tiny-vilt-random-vqa"""     )
        _UpperCAmelCase          =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
        _UpperCAmelCase          =   """How many cats are there?"""

        _UpperCAmelCase          =   vqa_pipeline(image=__UpperCamelCase     ,					question="""How many cats are there?"""     ,					top_k=2     )
        self.assertEqual(
            __UpperCamelCase     ,					[{"""score""": ANY(__UpperCamelCase     ), """answer""": ANY(__UpperCamelCase     )}, {"""score""": ANY(__UpperCamelCase     ), """answer""": ANY(__UpperCamelCase     )}]     )

        _UpperCAmelCase          =   vqa_pipeline({"""image""": image, """question""": question}     ,					top_k=2     )
        self.assertEqual(
            __UpperCamelCase     ,					[{"""score""": ANY(__UpperCamelCase     ), """answer""": ANY(__UpperCamelCase     )}, {"""score""": ANY(__UpperCamelCase     ), """answer""": ANY(__UpperCamelCase     )}]     )







    @slow
    @require_torch
    def      _snake_case					(			self			:       Dict     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   pipeline("""visual-question-answering"""     ,					model="""dandelin/vilt-b32-finetuned-vqa"""     )
        _UpperCAmelCase          =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
        _UpperCAmelCase          =   """How many cats are there?"""

        _UpperCAmelCase          =   vqa_pipeline(image=__UpperCamelCase     ,					question=__UpperCamelCase     ,					top_k=2     )
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,					decimals=4     )     ,					[{"""score""": 0.8_7_9_9, """answer""": """2"""}, {"""score""": 0.2_9_6, """answer""": """1"""}]     )

        _UpperCAmelCase          =   vqa_pipeline({"""image""": image, """question""": question}     ,					top_k=2     )
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,					decimals=4     )     ,					[{"""score""": 0.8_7_9_9, """answer""": """2"""}, {"""score""": 0.2_9_6, """answer""": """1"""}]     )

        _UpperCAmelCase          =   vqa_pipeline(
            [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]     ,					top_k=2     )
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,					decimals=4     )     ,					[[{"""score""": 0.8_7_9_9, """answer""": """2"""}, {"""score""": 0.2_9_6, """answer""": """1"""}]] * 2     ,					)







    @require_tf
    @unittest.skip("""Visual question answering not implemented in TF"""     )
    def      _snake_case					(			self			:       Optional[int]     )		->Tuple:






        '''simple docstring'''






        pass

"""simple docstring"""



from __future__ import annotations


def  _UpperCamelCase	(						_A      )    ->  None:





    """simple docstring"""

    create_state_space_tree(_A					,      []					,      0					,      [0 for i in range(len(_A      )      )]      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      )    ->  None:





    """simple docstring"""

    if index == len(_A      ):
        print(_A      )
        return

    for i in range(len(_A      )      ):
        if not index_used[i]:
            current_sequence.append(sequence[i]      )
            _UpperCAmelCase          =   True
            create_state_space_tree(_A					,      _A					,      index + 1					,      _A      )
            current_sequence.pop()
            _UpperCAmelCase          =   False
a					:	list[int | str]							=		[3, 1, 2, 4]
generate_all_permutations(sequence)

a					:	list[int | str]							=		["A", "B", "C"]







generate_all_permutations(sequence_a)