


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			['''image_processor''', '''tokenizer''']
					A__      =			'''BlipImageProcessor'''
					A__      =			('''BertTokenizer''', '''BertTokenizerFast''')

					def __init__(self   :						Tuple				, _UpperCAmelCase   :						Any				, _UpperCAmelCase   :						str		)						->       int:




							"""simple docstring"""


							lowercase__					      =		False
							super().__init__(_UpperCAmelCase				, _UpperCAmelCase		)
							lowercase__					      =		self.image_processor

					def __call__(self   :						List[str]				, _UpperCAmelCase   :						ImageInput = None				, _UpperCAmelCase   :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Union[bool, str, PaddingStrategy] = False				, _UpperCAmelCase   :						Union[bool, str, TruncationStrategy] = None				, _UpperCAmelCase   :						Optional[int] = None				, _UpperCAmelCase   :						int = 0				, _UpperCAmelCase   :						Optional[int] = None				, _UpperCAmelCase   :						Optional[bool] = None				, _UpperCAmelCase   :						bool = False				, _UpperCAmelCase   :						bool = False				, _UpperCAmelCase   :						bool = False				, _UpperCAmelCase   :						bool = False				, _UpperCAmelCase   :						bool = False				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Optional[Union[str, TensorType]] = None				, **_UpperCAmelCase   :						List[str]				, )						->       BatchEncoding:




							"""simple docstring"""


							if images is None and text is None:
									raise ValueError("""You have to specify either images or text."""		)

							# Get only text
							if images is None:
									lowercase__					      =		self.tokenizer
									lowercase__					      =		self.tokenizer(
									    text=_UpperCAmelCase				, add_special_tokens=_UpperCAmelCase				, padding=_UpperCAmelCase				, truncation=_UpperCAmelCase				, max_length=_UpperCAmelCase				, stride=_UpperCAmelCase				, pad_to_multiple_of=_UpperCAmelCase				, return_attention_mask=_UpperCAmelCase				, return_overflowing_tokens=_UpperCAmelCase				, return_special_tokens_mask=_UpperCAmelCase				, return_offsets_mapping=_UpperCAmelCase				, return_token_type_ids=_UpperCAmelCase				, return_length=_UpperCAmelCase				, verbose=_UpperCAmelCase				, return_tensors=_UpperCAmelCase				, **_UpperCAmelCase				, )
									return text_encoding

							# add pixel_values
							lowercase__					      =		self.image_processor(_UpperCAmelCase				, return_tensors=_UpperCAmelCase		)

							if text is not None:
									lowercase__					      =		self.tokenizer(
									    text=_UpperCAmelCase				, add_special_tokens=_UpperCAmelCase				, padding=_UpperCAmelCase				, truncation=_UpperCAmelCase				, max_length=_UpperCAmelCase				, stride=_UpperCAmelCase				, pad_to_multiple_of=_UpperCAmelCase				, return_attention_mask=_UpperCAmelCase				, return_overflowing_tokens=_UpperCAmelCase				, return_special_tokens_mask=_UpperCAmelCase				, return_offsets_mapping=_UpperCAmelCase				, return_token_type_ids=_UpperCAmelCase				, return_length=_UpperCAmelCase				, verbose=_UpperCAmelCase				, return_tensors=_UpperCAmelCase				, **_UpperCAmelCase				, )
							else:
									lowercase__					      =		None

							if text_encoding is not None:
									encoding_image_processor.update(_UpperCAmelCase		)

							return encoding_image_processor

					def   lowerCamelCase__					(self   :						Optional[int]				, *_UpperCAmelCase   :						Union[str, Any]				, **_UpperCAmelCase   :						Dict		)						->       List[Any]:




							"""simple docstring"""


							return self.tokenizer.batch_decode(*_UpperCAmelCase				, **_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						int				, *_UpperCAmelCase   :						List[Any]				, **_UpperCAmelCase   :						Optional[Any]		)						->       str:




							"""simple docstring"""


							return self.tokenizer.decode(*_UpperCAmelCase				, **_UpperCAmelCase		)


					@property
					def   lowerCamelCase__					(self   :						int		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		self.tokenizer.model_input_names
							lowercase__					      =		self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)









import pytest


A	:		Optional[Any]    =       '__dummy_dataset1__'

A	:		Tuple    =       '\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 			UpperCamelCase (     )       ->  Any:
		"""simple docstring"""




		return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def 			UpperCamelCase (     )       ->  Union[str, Any]:
		"""simple docstring"""




		return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def 			UpperCamelCase (     __magic_name__					:							List[str]   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Any					)       ->  str:
		"""simple docstring"""




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

