
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 __snake_case		:
      @staticmethod
      def       _SCREAMING_SNAKE_CASE       (  *snake_case		,**snake_case      ):



             '''simple docstring'''







             pass

@is_pipeline_test
@require_torch
@require_vision
class __snake_case		(      unittest.TestCase     ):
    _a		:						str=				MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      pipeline("""visual-question-answering"""		,model="""hf-internal-testing/tiny-vilt-random-vqa"""      )
           lowercase							:     Any       	=      [
               {
                   """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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     str       	=      vqa_pipeline(snake_case		,top_k=1      )
           self.assertEqual(
               snake_case		,[
                   [{"""score""": ANY(snake_case      ), """answer""": ANY(snake_case      )}],
                   [{"""score""": ANY(snake_case      ), """answer""": ANY(snake_case      )}],
               ]		,)
    @require_torch
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      pipeline("""visual-question-answering"""		,model="""hf-internal-testing/tiny-vilt-random-vqa"""      )
           lowercase							:     Union[str, Any]       	=      """./tests/fixtures/tests_samples/COCO/000000039769.png"""
           lowercase							:     int       	=      """How many cats are there?"""

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

           lowercase							:     List[str]       	=      vqa_pipeline({"""image""": image, """question""": question}		,top_k=2      )
           self.assertEqual(
               snake_case		,[{"""score""": ANY(snake_case      ), """answer""": ANY(snake_case      )}, {"""score""": ANY(snake_case      ), """answer""": ANY(snake_case      )}]      )
    @slow
    @require_torch
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      pipeline("""visual-question-answering"""		,model="""dandelin/vilt-b32-finetuned-vqa"""      )
           lowercase							:     Tuple       	=      """./tests/fixtures/tests_samples/COCO/000000039769.png"""
           lowercase							:     Optional[int]       	=      """How many cats are there?"""

           lowercase							:     Any       	=      vqa_pipeline(image=snake_case		,question=snake_case		,top_k=2      )
           self.assertEqual(
               nested_simplify(snake_case		,decimals=4      )		,[{"""score""": 0.8_799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]      )

           lowercase							:     str       	=      vqa_pipeline({"""image""": image, """question""": question}		,top_k=2      )
           self.assertEqual(
               nested_simplify(snake_case		,decimals=4      )		,[{"""score""": 0.8_799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]      )

           lowercase							:     Any       	=      vqa_pipeline(
               [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]		,top_k=2      )
           self.assertEqual(
               nested_simplify(snake_case		,decimals=4      )		,[[{"""score""": 0.8_799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]] * 2		,)
    @require_tf
    @unittest.skip("""Visual question answering not implemented in TF"""      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass






'''simple docstring'''







from __future__ import annotations





def 						snake_case_							(_a	:							Dict    ,   _a	:							str    ,   _a	:							Optional[Any]    ,   _a	:							List[str]     ):  # noqa: E741
    while r - l > 1:
        UpperCAmelCase			       =		(l + r) // 2
        if v[m] >= key:
            UpperCAmelCase			       =		m
        else:
            UpperCAmelCase			       =		m  # noqa: E741
    return r





def 						snake_case_							(_a	:							list[int]     ):
    if len(_a     ) == 0:
        return 0

    UpperCAmelCase			       =		[0] * len(_a     )
    UpperCAmelCase			       =		1

    UpperCAmelCase			       =		v[0]

    for i in range(1    ,   len(_a     )     ):
        if v[i] < tail[0]:
            UpperCAmelCase			       =		v[i]
        elif v[i] > tail[length - 1]:
            UpperCAmelCase			       =		v[i]
            length += 1
        else:
            UpperCAmelCase			       =		v[i]

    return length


if __name__ == "__main__":
   import doctest

   doctest.testmod()




