




"""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      UpperCAmelCase_  :




          @staticmethod
          def 							__magic_name__							(   *snake_case_						:	List[Any]     ,			**snake_case_						:	Union[str, Any]				)				->							str:




           '''simple docstring'''

           pass




@is_pipeline_test
@require_torch
@require_vision
class      UpperCAmelCase_  (						unittest.TestCase							):
    lowercase__						 =				MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	Tuple     ,			snake_case_						:	Tuple     ,			snake_case_						:	int				)				->							int:




     '''simple docstring'''

     A__	=    pipeline("visual-question-answering"     ,			model="hf-internal-testing/tiny-vilt-random-vqa"				)
     A__	=    [
         {
             "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 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[Any]				)				->							Dict:




     '''simple docstring'''

     A__	=    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 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

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

     A__	=    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_				)}]				)

     A__	=    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 							__magic_name__							(   self						:	str				)				->							Optional[int]:




     '''simple docstring'''

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

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

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

     A__	=    vqa_pipeline(
         [{"image": image, "question": question}, {"image": image, "question": question}]     ,			top_k=2				)
     self.assertEqual(
         nested_simplify(snake_case_     ,			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 							__magic_name__							(   self						:	str				)				->							List[Any]:




     '''simple docstring'''

     pass











"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand







def   _SCREAMING_SNAKE_CASE       (							)						->		List[Any]:
 A__	=    ArgumentParser("Diffusers CLI tool"      ,			usage="diffusers-cli <command> [<args>]"						)
 A__	=    parser.add_subparsers(help="diffusers-cli command helpers"						)

 # Register commands
 EnvironmentCommand.register_subcommand(lowercase_						)

 # Let's go
 A__	=    parser.parse_args()

 if not hasattr(lowercase_      ,			"func"						):
  parser.print_help()
  exit(1						)

 # Run
 A__	=    args.func(lowercase_						)
 service.run()


if __name__ == "__main__":
      main()





