

'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam


class        a__  (							datasets.BeamBasedBuilder  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return datasets.DatasetInfo(
          features=datasets.Features({"content": datasets.Value("string"  )}  )      ,    supervised_keys=_UpperCamelCase      ,    )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,    gen_kwargs={"examples": get_test_dummy_examples()}  )]






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      import apache_beam as beam

      return pipeline | "Load Examples" >> beam.Create(_UpperCamelCase  )




class        a__  (							datasets.BeamBasedBuilder  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return datasets.DatasetInfo(
          features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"  )}  )}  )      ,    supervised_keys=_UpperCamelCase      ,    )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      return [
          datasets.SplitGenerator(name=datasets.Split.TRAIN      ,    gen_kwargs={"examples": get_test_nested_examples()}  )
      ]






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      import apache_beam as beam

      return pipeline | "Load Examples" >> beam.Create(_UpperCamelCase  )







def  _A		(     )      ->     int:
   return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"]					)]







def  _A		(     )      ->     Optional[int]:
   return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"]					)]


class        a__  (							lowerCamelCase_  ):


   @require_beam
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       len(get_test_dummy_examples()  )
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
         _lowercase      :				Optional[Any]      					=       DummyBeamDataset(cache_dir=_UpperCamelCase      ,    beam_runner="DirectRunner"  )
         builder.download_and_prepare()
         self.assertTrue(
             os.path.exists(
                 os.path.join(_UpperCamelCase      ,    builder.name      ,    "default"      ,    "0.0.0"      ,    f'''{builder.name}-train.arrow'''  )  )  )
         self.assertDictEqual(builder.info.features      ,    datasets.Features({"content": datasets.Value("string"  )}  )  )
         _lowercase      :				Optional[Any]      					=       builder.as_dataset()
         self.assertEqual(dset["train"].num_rows      ,    _UpperCamelCase  )
         self.assertEqual(dset["train"].info.splits["train"].num_examples      ,    _UpperCamelCase  )
         self.assertDictEqual(dset["train"][0]      ,    get_test_dummy_examples()[0][1]  )
         self.assertDictEqual(
             dset["train"][expected_num_examples - 1]      ,    get_test_dummy_examples()[expected_num_examples - 1][1]  )
         self.assertTrue(
             os.path.exists(os.path.join(_UpperCamelCase      ,    builder.name      ,    "default"      ,    "0.0.0"      ,    "dataset_info.json"  )  )  )
         del dset


   @require_beam
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      import apache_beam as beam

      _lowercase      :				int      					=       beam.io.parquetio.WriteToParquet

      _lowercase      :				Optional[int]      					=       len(get_test_dummy_examples()  )
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
         _lowercase      :				List[Any]      					=       DummyBeamDataset(cache_dir=_UpperCamelCase      ,    beam_runner="DirectRunner"  )
         with patch("apache_beam.io.parquetio.WriteToParquet"  ) as write_parquet_mock:
            _lowercase      :				Any      					=       partial(_UpperCamelCase      ,    num_shards=2  )
            builder.download_and_prepare()
         self.assertTrue(
             os.path.exists(
                 os.path.join(
                     _UpperCamelCase      ,    builder.name      ,    "default"      ,    "0.0.0"      ,    f'''{builder.name}-train-00000-of-00002.arrow'''  )  )  )
         self.assertTrue(
             os.path.exists(
                 os.path.join(
                     _UpperCamelCase      ,    builder.name      ,    "default"      ,    "0.0.0"      ,    f'''{builder.name}-train-00000-of-00002.arrow'''  )  )  )
         self.assertDictEqual(builder.info.features      ,    datasets.Features({"content": datasets.Value("string"  )}  )  )
         _lowercase      :				Tuple      					=       builder.as_dataset()
         self.assertEqual(dset["train"].num_rows      ,    _UpperCamelCase  )
         self.assertEqual(dset["train"].info.splits["train"].num_examples      ,    _UpperCamelCase  )
         # Order is not preserved when sharding, so we just check that all the elements are there
         self.assertListEqual(sorted(dset["train"]["content"]  )      ,    sorted(["foo", "bar", "foobar"]  )  )
         self.assertTrue(
             os.path.exists(os.path.join(_UpperCamelCase      ,    builder.name      ,    "default"      ,    "0.0.0"      ,    "dataset_info.json"  )  )  )
         del dset


   @require_beam
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      with tempfile.TemporaryDirectory() as tmp_cache_dir:
         _lowercase      :				Tuple      					=       DummyBeamDataset(cache_dir=_UpperCamelCase  )
         self.assertRaises(datasets.builder.MissingBeamOptions      ,    builder.download_and_prepare  )






   @require_beam
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       len(get_test_nested_examples()  )
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
         _lowercase      :				Dict      					=       NestedBeamDataset(cache_dir=_UpperCamelCase      ,    beam_runner="DirectRunner"  )
         builder.download_and_prepare()
         self.assertTrue(
             os.path.exists(
                 os.path.join(_UpperCamelCase      ,    builder.name      ,    "default"      ,    "0.0.0"      ,    f'''{builder.name}-train.arrow'''  )  )  )
         self.assertDictEqual(
             builder.info.features      ,    datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"  )}  )}  )  )
         _lowercase      :				Optional[Any]      					=       builder.as_dataset()
         self.assertEqual(dset["train"].num_rows      ,    _UpperCamelCase  )
         self.assertEqual(dset["train"].info.splits["train"].num_examples      ,    _UpperCamelCase  )
         self.assertDictEqual(dset["train"][0]      ,    get_test_nested_examples()[0][1]  )
         self.assertDictEqual(
             dset["train"][expected_num_examples - 1]      ,    get_test_nested_examples()[expected_num_examples - 1][1]  )
         self.assertTrue(
             os.path.exists(os.path.join(_UpperCamelCase      ,    builder.name      ,    "default"      ,    "0.0.0"      ,    "dataset_info.json"  )  )  )
         del dset









'''simple docstring'''
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,
)






