
"""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 			lowerCAmelCase_		(datasets.BeamBasedBuilder						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											import apache_beam as beam

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






class 			lowerCAmelCase_		(datasets.BeamBasedBuilder						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	str:



											"""simple docstring"""




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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											import apache_beam as beam

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






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

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



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				@require_beam
				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




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



				@require_beam
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											import apache_beam as beam

											SCREAMING_SNAKE_CASE__		: Dict     =     beam.io.parquetio.WriteToParquet

											SCREAMING_SNAKE_CASE__		: Any     =     len(get_test_dummy_examples()					)
											with tempfile.TemporaryDirectory() as tmp_cache_dir:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE__     , beam_runner="""DirectRunner"""					)
																		with patch("""apache_beam.io.parquetio.WriteToParquet"""					) as write_parquet_mock:
																									SCREAMING_SNAKE_CASE__		: Dict     =     partial(SCREAMING_SNAKE_CASE__     , num_shards=2					)
																									builder.download_and_prepare()
																		self.assertTrue(
																		    os.path.exists(
																		        os.path.join(
																		            SCREAMING_SNAKE_CASE__     , builder.name     , """default"""     , """0.0.0"""     , F'''{builder.name}-train-00000-of-00002.arrow'''					)					)					)
																		self.assertTrue(
																		    os.path.exists(
																		        os.path.join(
																		            SCREAMING_SNAKE_CASE__     , 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"""					)}					)					)
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     builder.as_dataset()
																		self.assertEqual(dset["""train"""].num_rows     , SCREAMING_SNAKE_CASE__					)
																		self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples     , SCREAMING_SNAKE_CASE__					)
																		# 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(SCREAMING_SNAKE_CASE__     , builder.name     , """default"""     , """0.0.0"""     , """dataset_info.json"""					)					)					)
																		del dset



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



											"""simple docstring"""




											with tempfile.TemporaryDirectory() as tmp_cache_dir:
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE__					)
																		self.assertRaises(datasets.builder.MissingBeamOptions     , builder.download_and_prepare					)



				@require_beam
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     len(get_test_nested_examples()					)
											with tempfile.TemporaryDirectory() as tmp_cache_dir:
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     NestedBeamDataset(cache_dir=SCREAMING_SNAKE_CASE__     , beam_runner="""DirectRunner"""					)
																		builder.download_and_prepare()
																		self.assertTrue(
																		    os.path.exists(
																		        os.path.join(SCREAMING_SNAKE_CASE__     , 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"""					)}					)}					)					)
																		SCREAMING_SNAKE_CASE__		: Any     =     builder.as_dataset()
																		self.assertEqual(dset["""train"""].num_rows     , SCREAMING_SNAKE_CASE__					)
																		self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples     , SCREAMING_SNAKE_CASE__					)
																		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(SCREAMING_SNAKE_CASE__     , builder.name     , """default"""     , """0.0.0"""     , """dataset_info.json"""					)					)					)
																		del dset




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__   :					Tuple												=				{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					str												=				['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					Any												=				[
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__   :					Union[str, Any]												=				_LazyModule(__name__, globals()['__file__'], _import_structure)




