




from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Dict					,      __lowerCamelCase				:     Callable					,      __lowerCamelCase				:     Optional[Features] = None					,      __lowerCamelCase				:     str = None					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     Optional[dict] = None					,      __lowerCamelCase				:     Optional[int] = None					,      **__lowerCamelCase				:     int					,      )					->     Optional[Any]:
							super().__init__(
							    features=__lowerCamelCase					,      cache_dir=__lowerCamelCase					,      keep_in_memory=__lowerCamelCase					,      streaming=__lowerCamelCase					,      num_proc=__lowerCamelCase					,      **__lowerCamelCase					,      )
							a    =			Generator(
							    cache_dir=__lowerCamelCase					,      features=__lowerCamelCase					,      generator=__lowerCamelCase					,      gen_kwargs=__lowerCamelCase					,      **__lowerCamelCase					,      )





				def 							__UpperCAmelCase							(      self				:     str							)					->     str:
							# Build iterable dataset
							if self.streaming:
										a    =			self.builder.as_streaming_dataset(split="train"							)
							# Build regular (map-style) dataset
							else:
										a    =			None
										a    =			None
										a    =			None
										a    =			None

										self.builder.download_and_prepare(
										    download_config=__lowerCamelCase					,      download_mode=__lowerCamelCase					,      verification_mode=__lowerCamelCase					,      base_path=__lowerCamelCase					,      num_proc=self.num_proc					,      )
										a    =			self.builder.as_dataset(
										    split="train"					,      verification_mode=__lowerCamelCase					,      in_memory=self.keep_in_memory							)
							return dataset












from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






