code: import unittest
  
  import requests
  
  from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
  from transformers.pipelines import pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      is_torch_available,
      require_tf,
      require_torch,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
  else:
   a     :			Optional[int]					    =						False
  
  
  if is_vision_available():
   from PIL import Image
  else:
  
   class 				_lowercase						:
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
       @staticmethod
       def     _a						(						*lowerCamelCase__					,	**lowerCamelCase__   ):
          pass
  
  @is_pipeline_test
  @require_vision
  class 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						MODEL_FOR_VISION_2_SEQ_MAPPING
      SCREAMING_SNAKE_CASE:							List[Any]        =						TF_MODEL_FOR_VISION_2_SEQ_MAPPING
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Any         =  pipeline("image-to-text"					,	model=lowerCamelCase__					,	tokenizer=lowerCamelCase__					,	image_processor=lowerCamelCase__   )
         lowerCAmelCase_:       Tuple         =  [
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   ),
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]
         return pipe, examples
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Optional[int]         =  pipe(lowerCamelCase__   )
         self.assertEqual(
             lowerCamelCase__					,	[
                 [{"generated_text": ANY(lowerCamelCase__   )}],
                 [{"generated_text": ANY(lowerCamelCase__   )}],
             ]					,	)
  
  
  
  
      @require_tf
      def     _a						(						self   ):
         lowerCAmelCase_:       str         =  pipeline("image-to-text"					,	model="hf-internal-testing/tiny-random-vit-gpt2"					,	framework="tf"   )
         lowerCAmelCase_:       Union[str, Any]         =  "./tests/fixtures/tests_samples/COCO/000000039769.png"
  
         lowerCAmelCase_:       Dict         =  pipe(lowerCamelCase__   )
         self.assertEqual(
             lowerCamelCase__					,	[
                 {
                     "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                 },
             ]					,	)
  
         lowerCAmelCase_:       Dict         =  pipe([image, image]   )
         self.assertEqual(
             lowerCamelCase__					,	[
                 [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     }
                 ],
                 [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     }
                 ],
             ]					,	)
  
         lowerCAmelCase_:       List[Any]         =  pipe(lowerCamelCase__					,	max_new_tokens=1   )
         self.assertEqual(
             lowerCamelCase__					,	[{"generated_text": "growth"}]					,	)
  
  
  
  
      @require_torch
      def     _a						(						self   ):
         lowerCAmelCase_:       Dict         =  pipeline("image-to-text"					,	model="hf-internal-testing/tiny-random-vit-gpt2"   )
         lowerCAmelCase_:       List[Any]         =  "./tests/fixtures/tests_samples/COCO/000000039769.png"
  
         lowerCAmelCase_:       int         =  pipe(lowerCamelCase__   )
         self.assertEqual(
             lowerCamelCase__					,	[
                 {
                     "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                 },
             ]					,	)
  
         lowerCAmelCase_:       Union[str, Any]         =  pipe([image, image]   )
         self.assertEqual(
             lowerCamelCase__					,	[
                 [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     }
                 ],
                 [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     }
                 ],
             ]					,	)
  
  
  
  
      @require_torch
      def     _a						(						self   ):
         lowerCAmelCase_:       int         =  pipeline("image-to-text"					,	model="hf-internal-testing/tiny-random-BlipForConditionalGeneration"   )
         lowerCAmelCase_:       Optional[Any]         =  "./tests/fixtures/tests_samples/COCO/000000039769.png"
         lowerCAmelCase_:       Dict         =  "a photo of"
  
         lowerCAmelCase_:       List[Any]         =  pipe(lowerCamelCase__					,	prompt=lowerCamelCase__   )
         self.assertTrue(outputs[0]["generated_text"].startswith(lowerCamelCase__   )   )
  
  
  
  
      @slow
      @require_torch
      def     _a						(						self   ):
         lowerCAmelCase_:       int         =  pipeline("image-to-text"					,	model="ydshieh/vit-gpt2-coco-en"   )
         lowerCAmelCase_:       List[str]         =  "./tests/fixtures/tests_samples/COCO/000000039769.png"
  
         lowerCAmelCase_:       List[Any]         =  pipe(lowerCamelCase__   )
         self.assertEqual(lowerCamelCase__					,	[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]   )
  
         lowerCAmelCase_:       Optional[Any]         =  pipe([image, image]   )
         self.assertEqual(
             lowerCamelCase__					,	[
                 [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                 [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
             ]					,	)
  
  
  
  
      @slow
      @require_torch
      def     _a						(						self   ):
         lowerCAmelCase_:       Dict         =  pipeline("image-to-text"					,	model="Salesforce/blip-image-captioning-base"   )
         lowerCAmelCase_:       Dict         =  "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
         lowerCAmelCase_:       Any         =  Image.open(requests.get(lowerCamelCase__					,	stream=lowerCamelCase__   ).raw   )
  
         lowerCAmelCase_:       List[Any]         =  pipe(lowerCamelCase__   )
         self.assertEqual(lowerCamelCase__					,	[{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}]   )
  
  
  
  
      @slow
      @require_torch
      def     _a						(						self   ):
         lowerCAmelCase_:       int         =  pipeline("image-to-text"					,	model="microsoft/git-base-coco"   )
         lowerCAmelCase_:       Union[str, Any]         =  "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
         lowerCAmelCase_:       Tuple         =  Image.open(requests.get(lowerCamelCase__					,	stream=lowerCamelCase__   ).raw   )
  
         lowerCAmelCase_:       int         =  pipe(lowerCamelCase__   )
         self.assertEqual(lowerCamelCase__					,	[{"generated_text": "a cartoon of a purple character."}]   )
  
  
  
  
      @slow
      @require_torch
      def     _a						(						self   ):
         lowerCAmelCase_:       int         =  pipeline("image-to-text"					,	model="Salesforce/blip-image-captioning-base"   )
         lowerCAmelCase_:       Tuple         =  "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
         lowerCAmelCase_:       str         =  Image.open(requests.get(lowerCamelCase__					,	stream=lowerCamelCase__   ).raw   )
  
         lowerCAmelCase_:       List[str]         =  "a photography of"
  
         lowerCAmelCase_:       Any         =  pipe(lowerCamelCase__					,	prompt=lowerCamelCase__   )
         self.assertEqual(lowerCamelCase__					,	[{"generated_text": "a photography of a volcano"}]   )
  
         with self.assertRaises(lowerCamelCase__   ):
            lowerCAmelCase_:       List[str]         =  pipe([image, image]					,	prompt=[prompt, prompt]   )
  
  
  
  
      @slow
      @require_torch
      def     _a						(						self   ):
         lowerCAmelCase_:       int         =  pipeline("image-to-text"					,	model="microsoft/git-base-coco"   )
         lowerCAmelCase_:       str         =  "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
         lowerCAmelCase_:       Dict         =  Image.open(requests.get(lowerCamelCase__					,	stream=lowerCamelCase__   ).raw   )
  
         lowerCAmelCase_:       Optional[int]         =  "a photo of a"
  
         lowerCAmelCase_:       Dict         =  pipe(lowerCamelCase__					,	prompt=lowerCamelCase__   )
         self.assertEqual(lowerCamelCase__					,	[{"generated_text": "a photo of a tent with a tent and a tent in the background."}]   )
  
         with self.assertRaises(lowerCamelCase__   ):
            lowerCAmelCase_:       Union[str, Any]         =  pipe([image, image]					,	prompt=[prompt, prompt]   )
  
  
  
  
      @unittest.skipIf(
          not is_torch_greater_or_equal_than_1_11					,	reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   )
      @slow
      @require_torch
      def     _a						(						self   ):
         lowerCAmelCase_:       str         =  pipeline("image-to-text"					,	model="google/pix2struct-ai2d-base"   )
         lowerCAmelCase_:       Optional[Any]         =  "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
         lowerCAmelCase_:       Dict         =  Image.open(requests.get(lowerCamelCase__					,	stream=lowerCamelCase__   ).raw   )
  
         lowerCAmelCase_:       Union[str, Any]         =  "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
  
         lowerCAmelCase_:       Union[str, Any]         =  pipe(lowerCamelCase__					,	prompt=lowerCamelCase__   )
         self.assertEqual(lowerCamelCase__					,	[{"generated_text": "ash cloud"}]   )
  
         with self.assertRaises(lowerCamelCase__   ):
            lowerCAmelCase_:       List[str]         =  pipe([image, image]					,	prompt=[prompt, prompt]   )
  
  
  
  
      @slow
      @require_tf
      def     _a						(						self   ):
         lowerCAmelCase_:       Optional[Any]         =  pipeline("image-to-text"					,	model="ydshieh/vit-gpt2-coco-en"					,	framework="tf"   )
         lowerCAmelCase_:       Dict         =  "./tests/fixtures/tests_samples/COCO/000000039769.png"
  
         lowerCAmelCase_:       Any         =  pipe(lowerCamelCase__   )
         self.assertEqual(lowerCamelCase__					,	[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]   )
  
         lowerCAmelCase_:       Tuple         =  pipe([image, image]   )
         self.assertEqual(
             lowerCamelCase__					,	[
                 [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                 [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
             ]					,	)
style_context: import itertools
  def 		snake_case__  (		lowercase				):
     return (
         int("".join(combination[0:2]				)				) * int("".join(combination[2:5]				)				)
         == int("".join(combination[5:9]				)				)
     ) or (
         int("".join(combination[0]				)				) * int("".join(combination[1:5]				)				)
         == int("".join(combination[5:9]				)				)
     )
  def 		snake_case__  (		):
     return sum(
         {
             int("".join(pandigital[5:9]				)				)
             for pandigital in itertools.permutations("123456789"				)
             if is_combination_valid(lowercase				)
         }				)
  
  
  if __name__ == "__main__":
   print(solution())