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 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        return datasets.DatasetInfo(
            features=datasets.Features({'''content''': datasets.Value('''string''')}) , supervised_keys=__lowercase , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Optional[int]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_dummy_examples()})]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	List[str]:
        import apache_beam as beam

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


class 	lowerCamelCase_     (   datasets.BeamBasedBuilder ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Dict:
        return datasets.DatasetInfo(
            features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''')})}) , supervised_keys=__lowercase , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Dict:
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_nested_examples()})
        ]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Optional[int]:
        import apache_beam as beam

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



def lowerCamelCase  (   ):
    '''simple docstring'''







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



def lowerCamelCase  (   ):
    '''simple docstring'''







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


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






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







    @require_beam
    def 			UpperCamelCase__   (	self)       ->	Any:
        import apache_beam as beam

        __UpperCamelCase     :int			  =		beam.io.parquetio.WriteToParquet

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







    @require_beam
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
            __UpperCamelCase     :Optional[int]			  =		DummyBeamDataset(cache_dir=__lowercase)
            self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare)







    @require_beam
    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Dict			  =		len(get_test_nested_examples())
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
            __UpperCamelCase     :Tuple			  =		NestedBeamDataset(cache_dir=__lowercase , beam_runner='''DirectRunner''')
            builder.download_and_prepare()
            self.assertTrue(
                os.path.exists(
                    os.path.join(__lowercase , 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''')})}))
            __UpperCamelCase     :Union[str, Any]			  =		builder.as_dataset()
            self.assertEqual(dset['''train'''].num_rows , __lowercase)
            self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __lowercase)
            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(__lowercase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''')))
            del dset









'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


__snake_case    						=logging.get_logger(__name__)

class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Dict    ,      *UpperCAmelCase__       :      Dict    ,      **UpperCAmelCase__       :      List[str]    )				->  None:
        warnings.warn(
            'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use FlavaImageProcessor instead.'    ,      UpperCAmelCase__    ,      )
        super().__init__(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





