code: 
  
  
  
  
  import unittest
  
  import pytest
  
  from transformers import (
      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      MBartaaTokenizerFast,
      MBartConfig,
      MBartForConditionalGeneration,
      TranslationPipeline,
      pipeline,
  )
  from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow
  
  from .test_pipelines_common import ANY
  
  
  
  
  
  
  @is_pipeline_test
  class 						lowercase     (   unittest.TestCase):
        __lowercase 		=  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
        __lowercase 		=  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ ,  a__					)       ->   Optional[Any]:
           if isinstance(model.config ,  __SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Any       						= list(tokenizer.lang_code_to_id.keys()					)[:2]
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= TranslationPipeline(model=__SCREAMING_SNAKE_CASE ,  tokenizer=__SCREAMING_SNAKE_CASE ,  src_lang=__SCREAMING_SNAKE_CASE ,  tgt_lang=__SCREAMING_SNAKE_CASE					)
           else:
              SCREAMING_SNAKE_CASE__			:				int       						= TranslationPipeline(model=__SCREAMING_SNAKE_CASE ,  tokenizer=__SCREAMING_SNAKE_CASE					)
           return translator, ["Some string", "Some other text"]
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__					)       ->   Optional[Any]:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= translator('''Some string'''					)
           self.assertEqual(__SCREAMING_SNAKE_CASE ,  [{'''translation_text''': ANY(__SCREAMING_SNAKE_CASE					)}]					)
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= translator(['''Some string''']					)
           self.assertEqual(__SCREAMING_SNAKE_CASE ,  [{'''translation_text''': ANY(__SCREAMING_SNAKE_CASE					)}]					)
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= translator(['''Some string''', '''other string''']					)
           self.assertEqual(__SCREAMING_SNAKE_CASE ,  [{'''translation_text''': ANY(__SCREAMING_SNAKE_CASE					)}, {'''translation_text''': ANY(__SCREAMING_SNAKE_CASE					)}]					)
  
  
  
  
  
  
        @require_torch
        def    A__(self					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				Dict       						= pipeline('''translation_en_to_ro''' ,  model='''patrickvonplaten/t5-tiny-random''' ,  framework='''pt'''					)
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= translator('''This is a test string''' ,  max_length=2_0					)
           self.assertEqual(
               __SCREAMING_SNAKE_CASE ,  [
                   {
                       '''translation_text''': (
                           '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                           ''' Beide Beide'''
                       )
                   }
               ] ,  )
  
  
  
  
  
  
        @require_tf
        def    A__(self					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				int       						= pipeline('''translation_en_to_ro''' ,  model='''patrickvonplaten/t5-tiny-random''' ,  framework='''tf'''					)
           SCREAMING_SNAKE_CASE__			:				Dict       						= translator('''This is a test string''' ,  max_length=2_0					)
           self.assertEqual(
               __SCREAMING_SNAKE_CASE ,  [
                   {
                       '''translation_text''': (
                           '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                           ''' Beide Beide'''
                       )
                   }
               ] ,  )
  
  
  
  
  
  
        @require_torch
        def    A__(self					)       ->   List[str]:
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= pipeline('''translation_en_to_de''' ,  model='''patrickvonplaten/t5-tiny-random''' ,  framework='''pt'''					)
           SCREAMING_SNAKE_CASE__			:				List[str]       						= translator('''This is a test string''' ,  max_length=2_0					)
           self.assertEqual(
               __SCREAMING_SNAKE_CASE ,  [
                   {
                       '''translation_text''': (
                           '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                           ''' urine urine urine urine urine urine urine'''
                       )
                   }
               ] ,  )
  
  
  
  
  
  
        @require_tf
        def    A__(self					)       ->   Dict:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pipeline('''translation_en_to_de''' ,  model='''patrickvonplaten/t5-tiny-random''' ,  framework='''tf'''					)
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= translator('''This is a test string''' ,  max_length=2_0					)
           self.assertEqual(
               __SCREAMING_SNAKE_CASE ,  [
                   {
                       '''translation_text''': (
                           '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                           ''' urine urine urine urine urine urine urine'''
                       )
                   }
               ] ,  )
  
  
  
  
  
  
  class 						lowercase     (   unittest.TestCase):
  
  
  
  
  
  
        @require_torch
        @slow
        def    A__(self					)       ->   List[Any]:
           # We don't provide a default for this pair
           with self.assertRaises(__SCREAMING_SNAKE_CASE					):
              pipeline(task='''translation_cn_to_ar'''					)
  
           # but we do for this one
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= pipeline(task='''translation_en_to_de'''					)
           self.assertEqual(translator._preprocess_params['''src_lang'''] ,  '''en'''					)
           self.assertEqual(translator._preprocess_params['''tgt_lang'''] ,  '''de'''					)
  
  
  
  
  
  
        @require_torch
        @slow
        def    A__(self					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				Dict       						= MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''					)
           SCREAMING_SNAKE_CASE__			:				Dict       						= MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''					)
  
           SCREAMING_SNAKE_CASE__			:				Any       						= pipeline(task='''translation''' ,  model=__SCREAMING_SNAKE_CASE ,  tokenizer=__SCREAMING_SNAKE_CASE					)
           # Missing src_lang, tgt_lang
           with self.assertRaises(__SCREAMING_SNAKE_CASE					):
              translator('''This is a test'''					)
  
           SCREAMING_SNAKE_CASE__			:				Tuple       						= translator('''This is a test''' ,  src_lang='''en_XX''' ,  tgt_lang='''ar_AR'''					)
           self.assertEqual(__SCREAMING_SNAKE_CASE ,  [{'''translation_text''': '''هذا إختبار'''}]					)
  
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= translator('''This is a test''' ,  src_lang='''en_XX''' ,  tgt_lang='''hi_IN'''					)
           self.assertEqual(__SCREAMING_SNAKE_CASE ,  [{'''translation_text''': '''यह एक परीक्षण है'''}]					)
  
           # src_lang, tgt_lang can be defined at pipeline call time
           SCREAMING_SNAKE_CASE__			:				List[str]       						= pipeline(task='''translation''' ,  model=__SCREAMING_SNAKE_CASE ,  tokenizer=__SCREAMING_SNAKE_CASE ,  src_lang='''en_XX''' ,  tgt_lang='''ar_AR'''					)
           SCREAMING_SNAKE_CASE__			:				Any       						= translator('''This is a test'''					)
           self.assertEqual(__SCREAMING_SNAKE_CASE ,  [{'''translation_text''': '''هذا إختبار'''}]					)
  
  
  
  
  
  
        @require_torch
        def    A__(self					)       ->   Dict:
           SCREAMING_SNAKE_CASE__			:				int       						= '''patrickvonplaten/t5-tiny-random'''
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= pipeline(task='''translation_cn_to_ar''' ,  model=__SCREAMING_SNAKE_CASE					)
           self.assertEqual(translator._preprocess_params['''src_lang'''] ,  '''cn'''					)
           self.assertEqual(translator._preprocess_params['''tgt_lang'''] ,  '''ar'''					)
  
  
  
  
  
  
        @require_torch
        def    A__(self					)       ->   Optional[int]:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= '''patrickvonplaten/t5-tiny-random'''
           with pytest.warns(__SCREAMING_SNAKE_CASE ,  match=R'''.*translation_en_to_de.*'''					):
              SCREAMING_SNAKE_CASE__			:				Any       						= pipeline(task='''translation''' ,  model=__SCREAMING_SNAKE_CASE					)
           self.assertEqual(translator.task ,  '''translation_en_to_de'''					)
           self.assertEqual(translator._preprocess_params['''src_lang'''] ,  '''en'''					)
           self.assertEqual(translator._preprocess_params['''tgt_lang'''] ,  '''de'''					)
  
  
  
  
  
  
        @require_torch
        def    A__(self					)       ->   str:
           with self.assertRaises(__SCREAMING_SNAKE_CASE					):
              pipeline(task='''translation'''					)
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  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:
         lowercase__			=	False
  
  
  if is_vision_available():
         from PIL import Image
  else:
  
  
  
  
  
  
         class 						lowercase     :
  
  
  
  
  
  
               @staticmethod
               def    A__(*a__ ,  **a__					)       ->   Optional[Any]:
                  pass
  
  
  
  
  
  
  @is_pipeline_test
  @require_vision
  class 						lowercase     (   unittest.TestCase):
        __lowercase 		=  MODEL_FOR_VISION_2_SEQ_MAPPING
        __lowercase 		=  TF_MODEL_FOR_VISION_2_SEQ_MAPPING
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ ,  a__					)       ->   Optional[Any]:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= pipeline('''image-to-text''' ,  model=a__ ,  tokenizer=a__ ,  image_processor=a__					)
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= [
               Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					),
               '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
           ]
           return pipe, examples
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= pipe(a__					)
           self.assertEqual(
               a__ ,  [
                   [{'''generated_text''': ANY(a__					)}],
                   [{'''generated_text''': ANY(a__					)}],
               ] ,  )
  
  
  
  
  
  
        @require_tf
        def    A__(self					)       ->   List[str]:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= pipeline('''image-to-text''' ,  model='''hf-internal-testing/tiny-random-vit-gpt2''' ,  framework='''tf'''					)
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
  
           SCREAMING_SNAKE_CASE__			:				str       						= pipe(a__					)
           self.assertEqual(
               a__ ,  [
                   {
                       '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                   },
               ] ,  )
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pipe([image, image]					)
           self.assertEqual(
               a__ ,  [
                   [
                       {
                           '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                       }
                   ],
                   [
                       {
                           '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                       }
                   ],
               ] ,  )
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe(a__ ,  max_new_tokens=1					)
           self.assertEqual(
               a__ ,  [{'''generated_text''': '''growth'''}] ,  )
  
  
  
  
  
  
        @require_torch
        def    A__(self					)       ->   Optional[Any]:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pipeline('''image-to-text''' ,  model='''hf-internal-testing/tiny-random-vit-gpt2'''					)
           SCREAMING_SNAKE_CASE__			:				Any       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pipe(a__					)
           self.assertEqual(
               a__ ,  [
                   {
                       '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                   },
               ] ,  )
  
           SCREAMING_SNAKE_CASE__			:				Any       						= pipe([image, image]					)
           self.assertEqual(
               a__ ,  [
                   [
                       {
                           '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                       }
                   ],
                   [
                       {
                           '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                       }
                   ],
               ] ,  )
  
  
  
  
  
  
        @require_torch
        def    A__(self					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipeline('''image-to-text''' ,  model='''hf-internal-testing/tiny-random-BlipForConditionalGeneration'''					)
           SCREAMING_SNAKE_CASE__			:				int       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
           SCREAMING_SNAKE_CASE__			:				Tuple       						= '''a photo of'''
  
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= pipe(a__ ,  prompt=a__					)
           self.assertTrue(outputs[0]['''generated_text'''].startswith(a__					)					)
  
  
  
  
  
  
        @slow
        @require_torch
        def    A__(self					)       ->   List[Any]:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= pipeline('''image-to-text''' ,  model='''ydshieh/vit-gpt2-coco-en'''					)
           SCREAMING_SNAKE_CASE__			:				List[str]       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
  
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= pipe(a__					)
           self.assertEqual(a__ ,  [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}]					)
  
           SCREAMING_SNAKE_CASE__			:				Tuple       						= pipe([image, image]					)
           self.assertEqual(
               a__ ,  [
                   [{'''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					)       ->   Tuple:
           SCREAMING_SNAKE_CASE__			:				Dict       						= pipeline('''image-to-text''' ,  model='''Salesforce/blip-image-captioning-base'''					)
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= '''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pipe(a__					)
           self.assertEqual(a__ ,  [{'''generated_text''': '''a pink pokemon pokemon with a blue shirt and a blue shirt'''}]					)
  
  
  
  
  
  
        @slow
        @require_torch
        def    A__(self					)       ->   int:
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pipeline('''image-to-text''' ,  model='''microsoft/git-base-coco'''					)
           SCREAMING_SNAKE_CASE__			:				str       						= '''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
           SCREAMING_SNAKE_CASE__			:				List[str]       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)
  
           SCREAMING_SNAKE_CASE__			:				str       						= pipe(a__					)
           self.assertEqual(a__ ,  [{'''generated_text''': '''a cartoon of a purple character.'''}]					)
  
  
  
  
  
  
        @slow
        @require_torch
        def    A__(self					)       ->   Dict:
           SCREAMING_SNAKE_CASE__			:				str       						= pipeline('''image-to-text''' ,  model='''Salesforce/blip-image-captioning-base'''					)
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= '''a photography of'''
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe(a__ ,  prompt=a__					)
           self.assertEqual(a__ ,  [{'''generated_text''': '''a photography of a volcano'''}]					)
  
           with self.assertRaises(a__					):
              SCREAMING_SNAKE_CASE__			:				Any       						= pipe([image, image] ,  prompt=[prompt, prompt]					)
  
  
  
  
  
  
        @slow
        @require_torch
        def    A__(self					)       ->   Dict:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipeline('''image-to-text''' ,  model='''microsoft/git-base-coco'''					)
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
           SCREAMING_SNAKE_CASE__			:				Dict       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= '''a photo of a'''
  
           SCREAMING_SNAKE_CASE__			:				str       						= pipe(a__ ,  prompt=a__					)
           self.assertEqual(a__ ,  [{'''generated_text''': '''a photo of a tent with a tent and a tent in the background.'''}]					)
  
           with self.assertRaises(a__					):
              SCREAMING_SNAKE_CASE__			:				Dict       						= 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					)       ->   Union[str, Any]:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= pipeline('''image-to-text''' ,  model='''google/pix2struct-ai2d-base'''					)
           SCREAMING_SNAKE_CASE__			:				int       						= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
           SCREAMING_SNAKE_CASE__			:				Any       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)
  
           SCREAMING_SNAKE_CASE__			:				int       						= '''What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'''
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe(a__ ,  prompt=a__					)
           self.assertEqual(a__ ,  [{'''generated_text''': '''ash cloud'''}]					)
  
           with self.assertRaises(a__					):
              SCREAMING_SNAKE_CASE__			:				Any       						= pipe([image, image] ,  prompt=[prompt, prompt]					)
  
  
  
  
  
  
        @slow
        @require_tf
        def    A__(self					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pipeline('''image-to-text''' ,  model='''ydshieh/vit-gpt2-coco-en''' ,  framework='''tf'''					)
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
  
           SCREAMING_SNAKE_CASE__			:				Dict       						= pipe(a__					)
           self.assertEqual(a__ ,  [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}]					)
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe([image, image]					)
           self.assertEqual(
               a__ ,  [
                   [{'''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 '''}],
               ] ,  )
  
  