



import pytest


__snake_case     :List[Any]			       ='__dummy_dataset1__'

__snake_case     :Optional[int]			       ='\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'






@pytest.fixture
def  lowerCamelCase_	(      )       ->							Optional[int]:

							'''simple docstring'''






							return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def  lowerCamelCase_	(      )       ->							Optional[int]:

							'''simple docstring'''






							return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def  lowerCamelCase_	(      lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	int				)       ->							int:

							'''simple docstring'''






							A      						=    dataset_loading_script_name
							A      						=    tmp_path / 'datasets' / script_name
							script_dir.mkdir(parents=lowerCAmelCase__				)
							A      						=    script_dir / F'''{script_name}.py'''
							with open(lowerCAmelCase__			,	'w'				) as f:
														f.write(lowerCAmelCase__				)
							return str(lowerCAmelCase__				)





'''simple docstring'''


import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
				_snake_case : Tuple  				=						{
				    'linear': PIL.Image.Resampling.BILINEAR,
				    'bilinear': PIL.Image.Resampling.BILINEAR,
				    'bicubic': PIL.Image.Resampling.BICUBIC,
				    'lanczos': PIL.Image.Resampling.LANCZOS,
				    'nearest': PIL.Image.Resampling.NEAREST,
				}
else:
				_snake_case : Any  				=						{
				    'linear': PIL.Image.LINEAR,
				    'bilinear': PIL.Image.BILINEAR,
				    'bicubic': PIL.Image.BICUBIC,
				    'lanczos': PIL.Image.LANCZOS,
				    'nearest': PIL.Image.NEAREST,
				}
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							(images / 2 + 0.5).clamp(0					,		1 )
							_a      	=							images.cpu().permute(0					,		2					,		3					,		1 ).float().numpy()
							_a      	=							numpy_to_pil(UpperCamelCase )
							return images

def 						snake_case_       (UpperCamelCase			:    str ):





							'''simple docstring'''


							if images.ndim == 3:
														_a      	=							images[None, ...]
							_a      	=							(images * 255).round().astype('''uint8''' )
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														_a      	=							[Image.fromarray(image.squeeze()					,		mode='''L''' ) for image in images]
							else:
														_a      	=							[Image.fromarray(UpperCamelCase ) for image in images]

							return pil_images

