code: 
  
  
  
  
  import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile, rmtree
  from typing import Generator
  
  import datasets.config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.filelock import logger as fl_logger
  from datasets.utils.info_utils import VerificationMode
  from datasets.utils.logging import ERROR, get_logger
  
  
  lowercase__			=	get_logger(__name__)
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     return TestCommand(
         args.dataset	,     args.name	,     args.cache_dir	,     args.data_dir	,     args.all_configs	,     args.save_info or args.save_infos	,     args.ignore_verifications	,     args.force_redownload	,     args.clear_cache	,     )
  
  
  
  
  
  
  
  class 						lowercase     (   UpperCAmelCase_):
        __lowercase 		=  False  # to tell pytest it's not a test class
  
  
  
  
  
  
        @staticmethod
        def    A__(a__					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= parser.add_parser('''test''' ,  help='''Test dataset implementation.'''					)
           test_parser.add_argument('''--name''' ,  type=a__ ,  default=a__ ,  help='''Dataset processing name'''					)
           test_parser.add_argument(
               '''--cache_dir''' ,  type=a__ ,  default=a__ ,  help='''Cache directory where the datasets are stored.''' ,  )
           test_parser.add_argument(
               '''--data_dir''' ,  type=a__ ,  default=a__ ,  help='''Can be used to specify a manual directory to get the files from.''' ,  )
           test_parser.add_argument('''--all_configs''' ,  action='''store_true''' ,  help='''Test all dataset configurations'''					)
           test_parser.add_argument(
               '''--save_info''' ,  action='''store_true''' ,  help='''Save the dataset infos in the dataset card (README.md)'''					)
           test_parser.add_argument(
               '''--ignore_verifications''' ,  action='''store_true''' ,  help='''Run the test without checksums and splits checks.''' ,  )
           test_parser.add_argument('''--force_redownload''' ,  action='''store_true''' ,  help='''Force dataset redownload'''					)
           test_parser.add_argument(
               '''--clear_cache''' ,  action='''store_true''' ,  help='''Remove downloaded files and cached datasets after each config test''' ,  )
           # aliases
           test_parser.add_argument('''--save_infos''' ,  action='''store_true''' ,  help='''alias to save_info'''					)
           test_parser.add_argument('''dataset''' ,  type=a__ ,  help='''Name of the dataset to download'''					)
           test_parser.set_defaults(func=_test_command_factory					)
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__ ,  a__ ,  a__ ,  a__ ,  a__ ,  a__ ,  a__ ,  a__ ,  )       ->   Tuple:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= dataset
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= name
           SCREAMING_SNAKE_CASE__			:				Tuple       						= cache_dir
           SCREAMING_SNAKE_CASE__			:				str       						= data_dir
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= all_configs
           SCREAMING_SNAKE_CASE__			:				int       						= save_infos
           SCREAMING_SNAKE_CASE__			:				int       						= ignore_verifications
           SCREAMING_SNAKE_CASE__			:				str       						= force_redownload
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= clear_cache
           if clear_cache and not cache_dir:
              print(
                  '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
                  '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
                  '''Please provide a --cache_dir that will be used to test the dataset script.'''					)
              exit(1					)
           if save_infos:
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= True
  
  
  
  
  
  
        def    A__(self					)       ->   Optional[int]:
           fl_logger().setLevel(a__					)
           if self._name is not None and self._all_configs:
              print('''Both parameters `config` and `all_configs` can\'t be used at once.'''					)
              exit(1					)
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= self._dataset, self._name
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= dataset_module_factory(a__					)
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= import_main_class(module.module_path					)
           SCREAMING_SNAKE_CASE__			:				str       						= len(builder_cls.BUILDER_CONFIGS					) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1
  
           def get_builders() -> Generator[DatasetBuilder, None, None]:
              if self._all_configs and builder_cls.BUILDER_CONFIGS:
                 for i, config in enumerate(builder_cls.BUILDER_CONFIGS					):
                    if "config_name" in module.builder_kwargs:
                       yield builder_cls(
                           cache_dir=self._cache_dir ,  data_dir=self._data_dir ,  **module.builder_kwargs ,  )
                    else:
                       yield builder_cls(
                           config_name=config.name ,  cache_dir=self._cache_dir ,  data_dir=self._data_dir ,  **module.builder_kwargs ,  )
              else:
                 if "config_name" in module.builder_kwargs:
                    yield builder_cls(cache_dir=self._cache_dir ,  data_dir=self._data_dir ,  **module.builder_kwargs					)
                 else:
                    yield builder_cls(
                        config_name=a__ ,  cache_dir=self._cache_dir ,  data_dir=self._data_dir ,  **module.builder_kwargs ,  )
  
           for j, builder in enumerate(get_builders()					):
              print(f"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""					)
              SCREAMING_SNAKE_CASE__			:				int       						= os.path.exists(
                  os.path.join(builder.get_imported_module_dir() ,  datasets.config.DATASETDICT_INFOS_FILENAME					)					)  # record checksums only if we need to update a (deprecated) dataset_infos.json
              builder.download_and_prepare(
                  download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                  if not self._force_redownload
                  else DownloadMode.FORCE_REDOWNLOAD ,  verification_mode=VerificationMode.NO_CHECKS
                  if self._ignore_verifications
                  else VerificationMode.ALL_CHECKS ,  try_from_hf_gcs=a__ ,  )
              builder.as_dataset()
              if self._save_infos:
                 builder._save_infos()
  
              # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
              # The dataset_infos are saved in the YAML part of the README.md
  
              # Let's move it to the original directory of the dataset script, to allow the user to
              # upload them on S3 at the same time afterwards.
              if self._save_infos:
                 SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= os.path.join(builder_cls.get_imported_module_dir() ,  '''README.md'''					)
                 SCREAMING_SNAKE_CASE__			:				List[str]       						= Path(a__					).name + ".py"
                 SCREAMING_SNAKE_CASE__			:				Dict       						= os.path.join(a__ ,  a__					)
                 if os.path.isfile(a__					):
                    SCREAMING_SNAKE_CASE__			:				str       						= os.path.dirname(a__					)
                 elif os.path.isfile(a__					):
                    SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= path
                 elif os.path.isdir(a__					):  # for local directories containing only data files
                    SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= path
                 else:  # in case of a remote dataset
                    SCREAMING_SNAKE_CASE__			:				Dict       						= None
                    print(f"""Dataset card saved at {dataset_readme_path}"""					)
  
                 # Move dataset_info back to the user
                 if dataset_dir is not None:
                    SCREAMING_SNAKE_CASE__			:				List[Any]       						= os.path.join(a__ ,  '''README.md'''					)
                    copyfile(a__ ,  a__					)
                    print(f"""Dataset card saved at {user_dataset_readme_path}"""					)
  
                 # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
              if self._clear_cache:
                 if os.path.isdir(builder._cache_dir					):
                    logger.warning(f"""Clearing cache at {builder._cache_dir}"""					)
                    rmtree(builder._cache_dir					)
                 SCREAMING_SNAKE_CASE__			:				Tuple       						= os.path.join(self._cache_dir ,  datasets.config.DOWNLOADED_DATASETS_DIR					)
                 if os.path.isdir(a__					):
                    logger.warning(f"""Clearing cache at {download_dir}"""					)
                    rmtree(a__					)
  
           print('''Test successful.'''					)
  
  
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 '''}],
               ] ,  )
  
  