

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    List[str] 	=			[]
	for part_id in partition_order:
		__lowerCamelCase    :    str 	=			df.where(F"SPARK_PARTITION_ID() = {part_id}"      ).collect()
		for row_idx, row in enumerate(lowerCamelCase__      ):
			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        SCREAMING_SNAKE_CASE__	(  )  ->					str:
	__lowerCamelCase    :    Any 	=			pyspark.sql.SparkSession.builder.master('local[*]'      ).appName('pyspark'      ).getOrCreate()
	__lowerCamelCase    :    Dict 	=			spark.range(1_0_0      ).repartition(1      )
	__lowerCamelCase    :    str 	=			Spark(lowerCamelCase__      )
	# 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        SCREAMING_SNAKE_CASE__	(  )  ->					Dict:
	__lowerCamelCase    :    List[str] 	=			pyspark.sql.SparkSession.builder.master('local[*]'      ).appName('pyspark'      ).getOrCreate()
	__lowerCamelCase    :    Optional[Any] 	=			spark.range(1_0      ).repartition(2      )
	__lowerCamelCase    :    Optional[int] 	=			[1, 0]
	__lowerCamelCase    :    Dict 	=			_generate_iterable_examples(lowerCamelCase__     ,			lowerCamelCase__      )  # Reverse the partitions.
	__lowerCamelCase    :    Dict 	=			_get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase__     ,			lowerCamelCase__      )

	for i, (row_id, row_dict) in enumerate(generate_fn()      ):
		__lowerCamelCase	,			__lowerCamelCase    :    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        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    str 	=			pyspark.sql.SparkSession.builder.master('local[*]'      ).appName('pyspark'      ).getOrCreate()
	__lowerCamelCase    :    List[str] 	=			spark.range(1_0      ).repartition(1      )
	__lowerCamelCase    :    Dict 	=			SparkExamplesIterable(lowerCamelCase__      )
	assert it.n_shards == 1
	for i, (row_id, row_dict) in enumerate(lowerCamelCase__      ):
		assert row_id == F"0_{i}"
		assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    Union[str, Any] 	=			pyspark.sql.SparkSession.builder.master('local[*]'      ).appName('pyspark'      ).getOrCreate()
	__lowerCamelCase    :    Any 	=			spark.range(3_0      ).repartition(3      )
	# Mock the generator so that shuffle reverses the partition indices.
	with patch('numpy.random.Generator'      ) as generator_mock:
		__lowerCamelCase    :    int 	=			lambda lowerCamelCase__      : x.reverse()
		__lowerCamelCase    :    List[Any] 	=			_get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase__     ,			[2, 1, 0]      )

		__lowerCamelCase    :    Dict 	=			SparkExamplesIterable(lowerCamelCase__      ).shuffle_data_sources(lowerCamelCase__      )
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(lowerCamelCase__      ):
			__lowerCamelCase	,			__lowerCamelCase    :    Optional[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        SCREAMING_SNAKE_CASE__	(  )  ->					Dict:
	__lowerCamelCase    :    Optional[Any] 	=			pyspark.sql.SparkSession.builder.master('local[*]'      ).appName('pyspark'      ).getOrCreate()
	__lowerCamelCase    :    int 	=			spark.range(2_0      ).repartition(4      )

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

	# Partitions 1 and 3
	__lowerCamelCase    :    Any 	=			SparkExamplesIterable(lowerCamelCase__      ).shard_data_sources(worker_id=1     ,			num_workers=2      )
	assert shard_it_a.n_shards == 2
	__lowerCamelCase    :    Union[str, Any] 	=			_get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase__     ,			[1, 3]      )
	for i, (row_id, row_dict) in enumerate(lowerCamelCase__      ):
		__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			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        SCREAMING_SNAKE_CASE__	(  )  ->					Dict:
	__lowerCamelCase    :    List[str] 	=			pyspark.sql.SparkSession.builder.master('local[*]'      ).appName('pyspark'      ).getOrCreate()
	__lowerCamelCase    :    Optional[int] 	=			spark.range(1_0_0      ).repartition(1      )
	__lowerCamelCase    :    Tuple 	=			Spark(lowerCamelCase__      )
	# 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_torch_available


a		={
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

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




