"""simple docstring"""





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_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						Any			  =    []
							for part_id in partition_order:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    df.where(F"""SPARK_PARTITION_ID() = {part_id}"""  ).collect()
														for row_idx, row in enumerate(__SCREAMING_SNAKE_CASE  ):
																					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_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Any			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    spark.range(100  ).repartition(1  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    Spark(__SCREAMING_SNAKE_CASE  )
							# 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=16  )
							# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
							assert spark_builder.df.rdd.getNumPartitions() == 50



@require_not_windows
@require_dill_gt_0_3_2
def      lowerCamelCase_()->    List[str]:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    spark.range(10  ).repartition(2  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    [1, 0]
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    _generate_iterable_examples(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )  # Reverse the partitions.
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							for i, (row_id, row_dict) in enumerate(generate_fn()  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    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]:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    spark.range(10  ).repartition(1  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  )
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE  ):
														assert row_id == F"""0_{i}"""
														assert row_dict == {"id": i}



@require_not_windows
@require_dill_gt_0_3_2
def      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						int			  =    spark.range(30  ).repartition(3  )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch("""numpy.random.Generator"""  ) as generator_mock:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    lambda __SCREAMING_SNAKE_CASE  : x.reverse()
														_SCREAMING_SNAKE_CASE		:						int			  =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE     ,	[2, 1, 0]  )

														_SCREAMING_SNAKE_CASE		:						Tuple			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  ).shuffle_data_sources(__SCREAMING_SNAKE_CASE  )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Any			  =    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[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Any			  =    spark.range(20  ).repartition(4  )

							# Partitions 0 and 2
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  ).shard_data_sources(worker_id=0     ,	num_workers=2  )
							assert shard_it_a.n_shards == 2
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE     ,	[0, 2]  )
							for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

							# Partitions 1 and 3
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  ).shard_data_sources(worker_id=1     ,	num_workers=2  )
							assert shard_it_a.n_shards == 2
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE     ,	[1, 3]  )
							for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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_()->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Any			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    spark.range(100  ).repartition(1  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    Spark(__SCREAMING_SNAKE_CASE  )
							# 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() == 100



"""simple docstring"""





from typing import TYPE_CHECKING

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


lowerCAmelCase_     =	{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''UniSpeechForCTC''',
			    '''UniSpeechForPreTraining''',
			    '''UniSpeechForSequenceClassification''',
			    '''UniSpeechModel''',
			    '''UniSpeechPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_unispeech import (
						    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
						    UniSpeechForCTC,
						    UniSpeechForPreTraining,
						    UniSpeechForSequenceClassification,
						    UniSpeechModel,
						    UniSpeechPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


