

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		:      Dict):
								return datasets.DatasetInfo(
								    features=datasets.Features({'content': datasets.Value('string')})       ,supervised_keys=SCREAMING_SNAKE_CASE__       ,)
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[str]):
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,gen_kwargs={'examples': get_test_dummy_examples()})]


							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								import apache_beam as beam

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






class 	A_		(	datasets.BeamBasedBuilder    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								return datasets.DatasetInfo(
								    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string')})})       ,supervised_keys=SCREAMING_SNAKE_CASE__       ,)
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								return [
								    datasets.SplitGenerator(name=datasets.Split.TRAIN       ,gen_kwargs={'examples': get_test_nested_examples()})
								]


							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple):
								import apache_beam as beam

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






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






def        SCREAMING_SNAKE_CASE__	(  )  ->					Union[str, Any]:
	return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']      )]






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							@require_beam
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[Any] 	=			len(get_test_dummy_examples())
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
									__lowerCamelCase    :    Any 	=			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')}))
									__lowerCamelCase    :    int 	=			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    lowerCAmelCase					(		self		:      Union[str, Any]):
								import apache_beam as beam

								__lowerCamelCase    :    str 	=			beam.io.parquetio.WriteToParquet

								__lowerCamelCase    :    Union[str, Any] 	=			len(get_test_dummy_examples())
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
									__lowerCamelCase    :    Optional[int] 	=			DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE__       ,beam_runner='DirectRunner')
									with patch('apache_beam.io.parquetio.WriteToParquet') as write_parquet_mock:
										__lowerCamelCase    :    str 	=			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')}))
									__lowerCamelCase    :    Dict 	=			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    lowerCAmelCase					(		self		:      List[str]):
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
									__lowerCamelCase    :    List[str] 	=			DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE__)
									self.assertRaises(datasets.builder.MissingBeamOptions       ,builder.download_and_prepare)


							@require_beam
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    str 	=			len(get_test_nested_examples())
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
									__lowerCamelCase    :    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')})}))
									__lowerCamelCase    :    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







from typing import TYPE_CHECKING

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


a		={
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Mask2FormerImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """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

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure)




