


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							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						Tuple		)						->       Tuple:




							"""simple docstring"""


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

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




							"""simple docstring"""


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


					def   lowerCamelCase__					(self   :						Dict				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Dict		)						->       int:




							"""simple docstring"""


							import apache_beam as beam

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






class 							A   (	datasets.BeamBasedBuilder							):
					'''simple docstring'''






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




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Dict		)						->       str:




							"""simple docstring"""


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


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




							"""simple docstring"""


							import apache_beam as beam

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






def 			UpperCamelCase (     )       ->  str:
		"""simple docstring"""




		return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]					)]






def 			UpperCamelCase (     )       ->  Dict:
		"""simple docstring"""




		return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]					)]







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






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




							"""simple docstring"""


							lowercase__					      =		len(get_test_dummy_examples()		)
							with tempfile.TemporaryDirectory() as tmp_cache_dir:
									lowercase__					      =		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__					      =		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   :						str		)						->       Dict:




							"""simple docstring"""


							import apache_beam as beam

							lowercase__					      =		beam.io.parquetio.WriteToParquet

							lowercase__					      =		len(get_test_dummy_examples()		)
							with tempfile.TemporaryDirectory() as tmp_cache_dir:
									lowercase__					      =		DummyBeamDataset(cache_dir=_UpperCAmelCase				, beam_runner="""DirectRunner"""		)
									with patch("""apache_beam.io.parquetio.WriteToParquet"""		) as write_parquet_mock:
											lowercase__					      =		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__					      =		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   :						Dict		)						->       int:




							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmp_cache_dir:
									lowercase__					      =		DummyBeamDataset(cache_dir=_UpperCAmelCase		)
									self.assertRaises(datasets.builder.MissingBeamOptions				, builder.download_and_prepare		)


					@require_beam
					def   lowerCamelCase__					(self   :						List[Any]		)						->       str:




							"""simple docstring"""


							lowercase__					      =		len(get_test_nested_examples()		)
							with tempfile.TemporaryDirectory() as tmp_cache_dir:
									lowercase__					      =		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__					      =		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





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__					)

