code: 
  
  
  
  
  from collections import OrderedDict
  from dataclasses import fields
  from typing import Any, Tuple
  
  import numpy as np
  
  from .import_utils import is_torch_available
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     if is_torch_available():
        import torch
  
        if isinstance(snake_case	,     torch.Tensor   ):
           return True
  
     return isinstance(snake_case	,     np.ndarray   )
  
  
  
  
  
  
  
  class 						lowercase     (   UpperCAmelCase_):
  
  
  
  
  
  
        def    A__(self					)       ->   List[Any]:
           SCREAMING_SNAKE_CASE__			:				int       						= fields(self					)
  
           # Safety and consistency checks
           if not len(a__					):
              raise ValueError(f"""{self.__class__.__name__} has no fields."""					)
  
           SCREAMING_SNAKE_CASE__			:				Dict       						= getattr(self ,  class_fields[0].name					)
           SCREAMING_SNAKE_CASE__			:				Dict       						= all(getattr(self ,  field.name					) is None for field in class_fields[1:]					)
  
           if other_fields_are_none and isinstance(a__ ,  a__					):
              for key, value in first_field.items():
                 SCREAMING_SNAKE_CASE__			:				Tuple       						= value
           else:
              for field in class_fields:
                 SCREAMING_SNAKE_CASE__			:				Optional[int]       						= getattr(self ,  field.name					)
                 if v is not None:
                    SCREAMING_SNAKE_CASE__			:				Tuple       						= v
  
  
  
  
  
  
        def __delitem__(self ,  *a__ ,  **a__					)       ->   Tuple:
           raise Exception(f"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""					)
  
  
  
  
  
  
        def    A__(self ,  *a__ ,  **a__					)       ->   List[Any]:
           raise Exception(f"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""					)
  
  
  
  
  
  
        def    A__(self ,  *a__ ,  **a__					)       ->   Union[str, Any]:
           raise Exception(f"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""					)
  
  
  
  
  
  
        def    A__(self ,  *a__ ,  **a__					)       ->   List[Any]:
           raise Exception(f"""You cannot use ``update`` on a {self.__class__.__name__} instance."""					)
  
  
  
  
  
  
        def __getitem__(self ,  a__					)       ->   List[Any]:
           if isinstance(a__ ,  a__					):
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= dict(self.items()					)
              return inner_dict[k]
           else:
              return self.to_tuple()[k]
  
  
  
  
  
  
        def __setattr__(self ,  a__ ,  a__					)       ->   List[str]:
           if name in self.keys() and value is not None:
              # Don't call self.__setitem__ to avoid recursion errors
              super().__setitem__(a__ ,  a__					)
           super().__setattr__(a__ ,  a__					)
  
  
  
  
  
  
        def __setitem__(self ,  a__ ,  a__					)       ->   Any:
           # Will raise a KeyException if needed
           super().__setitem__(a__ ,  a__					)
           # Don't call self.__setattr__ to avoid recursion errors
           super().__setattr__(a__ ,  a__					)
  
  
  
  
  
  
        def    A__(self					)       ->   Tuple[Any]:
           return tuple(self[k] for k in self.keys()					)
  
  
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 '''}],
               ] ,  )
  
  