


from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		List[str]	)  ->		List[str]:


       '''simple docstring'''





       _UpperCAmelCase					=      []
       for part_id in partition_order:
              _UpperCAmelCase					=      df.where(F'''SPARK_PARTITION_ID() = {part_id}'''	).collect()
              for row_idx, row in enumerate(a__	):
                     expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict())	)
       return expected_row_ids_and_row_dicts

@require_not_windows
@require_dill_gt_0_3_2
def 	lowerCAmelCase__			(      )  ->		Any:


       '''simple docstring'''





       _UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
       _UpperCAmelCase					=      spark.range(1_0_0	).repartition(1	)
       _UpperCAmelCase					=      Spark(a__	)
       # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
       # that each partition can hold 2 rows.
       spark_builder._repartition_df_if_needed(max_shard_size=1_6	)
       # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
       assert spark_builder.df.rdd.getNumPartitions() == 5_0

@require_not_windows
@require_dill_gt_0_3_2
def 	lowerCAmelCase__			(      )  ->		Tuple:


       '''simple docstring'''





       _UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
       _UpperCAmelCase					=      spark.range(1_0	).repartition(2	)
       _UpperCAmelCase					=      [1, 0]
       _UpperCAmelCase					=      _generate_iterable_examples(a__      ,  a__	)  # Reverse the partitions.
       _UpperCAmelCase					=      _get_expected_row_ids_and_row_dicts_for_partition_order(a__      ,  a__	)

       for i, (row_id, row_dict) in enumerate(generate_fn()	):
              _UpperCAmelCase		,	_UpperCAmelCase					=      expected_row_ids_and_row_dicts[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict

@require_not_windows
@require_dill_gt_0_3_2
def 	lowerCAmelCase__			(      )  ->		Tuple:


       '''simple docstring'''





       _UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
       _UpperCAmelCase					=      spark.range(1_0	).repartition(1	)
       _UpperCAmelCase					=      SparkExamplesIterable(a__	)
       assert it.n_shards == 1
       for i, (row_id, row_dict) in enumerate(a__	):
              assert row_id == F'''0_{i}'''
              assert row_dict == {"id": i}

@require_not_windows
@require_dill_gt_0_3_2
def 	lowerCAmelCase__			(      )  ->		Dict:


       '''simple docstring'''





       _UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
       _UpperCAmelCase					=      spark.range(3_0	).repartition(3	)
       # Mock the generator so that shuffle reverses the partition indices.
       with patch('numpy.random.Generator'	) as generator_mock:
              _UpperCAmelCase					=      lambda a__	: x.reverse()
              _UpperCAmelCase					=      _get_expected_row_ids_and_row_dicts_for_partition_order(a__      ,  [2, 1, 0]	)

              _UpperCAmelCase					=      SparkExamplesIterable(a__	).shuffle_data_sources(a__	)
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(a__	):
                     _UpperCAmelCase		,	_UpperCAmelCase					=      expected_row_ids_and_row_dicts[i]
                     assert row_id == expected_row_id
                     assert row_dict == expected_row_dict

@require_not_windows
@require_dill_gt_0_3_2
def 	lowerCAmelCase__			(      )  ->		Optional[int]:


       '''simple docstring'''





       _UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
       _UpperCAmelCase					=      spark.range(2_0	).repartition(4	)

       # Partitions 0 and 2
       _UpperCAmelCase					=      SparkExamplesIterable(a__	).shard_data_sources(worker_id=0      ,  num_workers=2	)
       assert shard_it_a.n_shards == 2
       _UpperCAmelCase					=      _get_expected_row_ids_and_row_dicts_for_partition_order(a__      ,  [0, 2]	)
       for i, (row_id, row_dict) in enumerate(a__	):
              _UpperCAmelCase		,	_UpperCAmelCase					=      expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict

       # Partitions 1 and 3
       _UpperCAmelCase					=      SparkExamplesIterable(a__	).shard_data_sources(worker_id=1      ,  num_workers=2	)
       assert shard_it_a.n_shards == 2
       _UpperCAmelCase					=      _get_expected_row_ids_and_row_dicts_for_partition_order(a__      ,  [1, 3]	)
       for i, (row_id, row_dict) in enumerate(a__	):
              _UpperCAmelCase		,	_UpperCAmelCase					=      expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def 	lowerCAmelCase__			(      )  ->		int:


       '''simple docstring'''





       _UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
       _UpperCAmelCase					=      spark.range(1_0_0	).repartition(1	)
       _UpperCAmelCase					=      Spark(a__	)
       # Choose a small max_shard_size for maximum partitioning.
       spark_builder._repartition_df_if_needed(max_shard_size=1	)
       # The new number of partitions should not be greater than the number of rows.
       assert spark_builder.df.rdd.getNumPartitions() == 1_0_0




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__  :Tuple             =							{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							['''BeitFeatureExtractor''']
		lowerCAmelCase__  :Optional[Any]             =							['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :int             =							[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Tuple             =							[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__  :Dict             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
