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      lowerCamelCase    :
        """simple docstring"""







        @staticmethod
        def       __A   (							*__magic_name__       :				str     ,   **__magic_name__       :				Union[str, Any]      )     ->		Dict:
              pass


@is_pipeline_test
@require_torch
@require_vision
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				Dict     ,   __magic_name__       :				int     ,   __magic_name__       :				Union[str, Any]      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     pipeline("visual-question-answering"     ,   model="hf-internal-testing/tiny-vilt-random-vqa"      )
        SCREAMING_SNAKE_CASE_      =     [
            {
                "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       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				str      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     vqa_pipeline(__magic_name__     ,   top_k=1      )
        self.assertEqual(
            __magic_name__     ,   [
                [{"score": ANY(__magic_name__      ), "answer": ANY(__magic_name__      )}],
                [{"score": ANY(__magic_name__      ), "answer": ANY(__magic_name__      )}],
            ]     ,   )







  @require_torch
  def       __A   (							self       :				Union[str, Any]      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     pipeline("visual-question-answering"     ,   model="hf-internal-testing/tiny-vilt-random-vqa"      )
        SCREAMING_SNAKE_CASE_      =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
        SCREAMING_SNAKE_CASE_      =     "How many cats are there?"

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

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







  @slow
  @require_torch
  def       __A   (							self       :				Dict      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     pipeline("visual-question-answering"     ,   model="dandelin/vilt-b32-finetuned-vqa"      )
        SCREAMING_SNAKE_CASE_      =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
        SCREAMING_SNAKE_CASE_      =     "How many cats are there?"

        SCREAMING_SNAKE_CASE_      =     vqa_pipeline(image=__magic_name__     ,   question=__magic_name__     ,   top_k=2      )
        self.assertEqual(
            nested_simplify(__magic_name__     ,   decimals=4      )     ,   [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]      )

        SCREAMING_SNAKE_CASE_      =     vqa_pipeline({"image": image, "question": question}     ,   top_k=2      )
        self.assertEqual(
            nested_simplify(__magic_name__     ,   decimals=4      )     ,   [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]      )

        SCREAMING_SNAKE_CASE_      =     vqa_pipeline(
            [{"image": image, "question": question}, {"image": image, "question": question}]     ,   top_k=2      )
        self.assertEqual(
            nested_simplify(__magic_name__     ,   decimals=4      )     ,   [[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2     ,   )







  @require_tf
  @unittest.skip("Visual question answering not implemented in TF"      )
  def       __A   (							self       :				Any      )     ->		Any:
        pass



from ...processing_utils import ProcessorMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''feature_extractor''']
  lowerCamelCase__   =  '''TvltImageProcessor'''
  lowerCamelCase__   =  '''TvltFeatureExtractor'''







  def __init__(							self       :				List[str]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Any      )     ->		int:
        super().__init__(image_processor=__magic_name__     ,   feature_extractor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     image_processor
        SCREAMING_SNAKE_CASE_      =     feature_extractor







  def __call__(							self       :				List[str]     ,   __magic_name__       :				Optional[Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				int=None     ,   __magic_name__       :				str=None     ,   __magic_name__       :				Any=False     ,   __magic_name__       :				int=False     ,   *__magic_name__       :				int     ,   **__magic_name__       :				Any     ,   )     ->		List[Any]:

        if images is None and audio is None:
              raise ValueError("You need to specify either an `images` or `audio` input to process."      )

        SCREAMING_SNAKE_CASE_      =     None
        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   mask_pixel=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if images_mixed is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   is_mixed=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if audio is not None:
              SCREAMING_SNAKE_CASE_      =     self.feature_extractor(
                  __magic_name__     ,   *__magic_name__     ,   sampling_rate=__magic_name__     ,   mask_audio=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     {}
        if audio is not None:
              output_dict.update(__magic_name__      )
        if images is not None:
              output_dict.update(__magic_name__      )
        if images_mixed_dict is not None:
              output_dict.update(__magic_name__      )
        return output_dict







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.image_processor.model_input_names
        SCREAMING_SNAKE_CASE_      =     self.feature_extractor.model_input_names
        return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names      )      )


