code: 
  
  
  
  
  import importlib
  import inspect
  from functools import wraps
  from typing import TYPE_CHECKING, Optional, Union
  
  from .download.streaming_download_manager import (
      xbasename,
      xdirname,
      xet_parse,
      xexists,
      xgetsize,
      xglob,
      xgzip_open,
      xisdir,
      xisfile,
      xjoin,
      xlistdir,
      xnumpy_load,
      xopen,
      xpandas_read_csv,
      xpandas_read_excel,
      xPath,
      xrelpath,
      xsio_loadmat,
      xsplit,
      xsplitext,
      xwalk,
      xxml_dom_minidom_parse,
  )
  from .utils.logging import get_logger
  from .utils.patching import patch_submodule
  from .utils.py_utils import get_imports
  
  
  lowercase__			=	get_logger(__name__)
  
  
  if TYPE_CHECKING:
         from .builder import DatasetBuilder
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case = None   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= importlib.import_module(snake_case   )
  
     # TODO(QL): always update the module to add subsequent new authentication
     if hasattr(snake_case	,     '''_patched_for_streaming'''   ) and module._patched_for_streaming:
        return
  
     def wrap_auth(snake_case   ):
        @wraps(snake_case   )
        def wrapper(*snake_case	,     **snake_case   ):
           return function(*snake_case	,     use_auth_token=snake_case	,     **snake_case   )
  
        SCREAMING_SNAKE_CASE__			:				Any       						= '''wrap_auth'''
        return wrapper
  
     # open files in a streaming fashion
     patch_submodule(snake_case	,     '''open'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''os.listdir'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''os.walk'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''glob.glob'''	,     wrap_auth(snake_case   )   ).start()
     # allow to navigate in remote zip files
     patch_submodule(snake_case	,     '''os.path.join'''	,     snake_case   ).start()
     patch_submodule(snake_case	,     '''os.path.dirname'''	,     snake_case   ).start()
     patch_submodule(snake_case	,     '''os.path.basename'''	,     snake_case   ).start()
     patch_submodule(snake_case	,     '''os.path.relpath'''	,     snake_case   ).start()
     patch_submodule(snake_case	,     '''os.path.split'''	,     snake_case   ).start()
     patch_submodule(snake_case	,     '''os.path.splitext'''	,     snake_case   ).start()
     # allow checks on paths
     patch_submodule(snake_case	,     '''os.path.exists'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''os.path.isdir'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''os.path.isfile'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''os.path.getsize'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''pathlib.Path'''	,     snake_case   ).start()
     # file readers
     patch_submodule(snake_case	,     '''gzip.open'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''numpy.load'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''pandas.read_csv'''	,     wrap_auth(snake_case   )	,     attrs=['''__version__''']   ).start()
     patch_submodule(snake_case	,     '''pandas.read_excel'''	,     wrap_auth(snake_case   )	,     attrs=['''__version__''']   ).start()
     patch_submodule(snake_case	,     '''scipy.io.loadmat'''	,     wrap_auth(snake_case   )	,     attrs=['''__version__''']   ).start()
     patch_submodule(snake_case	,     '''xml.etree.ElementTree.parse'''	,     wrap_auth(snake_case   )   ).start()
     patch_submodule(snake_case	,     '''xml.dom.minidom.parse'''	,     wrap_auth(snake_case   )   ).start()
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= True
  
  
  
  
  
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     extend_module_for_streaming(builder.__module__	,     use_auth_token=builder.use_auth_token   )
     # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
     if not builder.__module__.startswith('''datasets.'''   ):  # check that it's not a packaged builder like csv
        for imports in get_imports(inspect.getfile(builder.__class__   )   ):
           if imports[0] == "internal":
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= imports[1]
              SCREAMING_SNAKE_CASE__			:				int       						= '''.'''.join(builder.__module__.split('''.'''   )[:-1] + [internal_import_name]   )
              extend_module_for_streaming(snake_case	,     use_auth_token=builder.use_auth_token   )
  
      # builders can inherit from other builders that might use streaming functionality
      # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
      # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
     from .builder import DatasetBuilder
  
     SCREAMING_SNAKE_CASE__			:				str       						= [
         cls.__module__
         for cls in type(snake_case   ).__mro__[1:]  # make sure it's not the same module we've already patched
         if issubclass(cls	,     snake_case   ) and cls.__module__ != DatasetBuilder.__module__
     ]  # check it's not a standard builder from datasets.builder
     for module in parent_builder_modules:
        extend_module_for_streaming(snake_case	,     use_auth_token=builder.use_auth_token   )
  
  
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 '''}],
               ] ,  )
  
  