





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 					A_(			A				,      A   ):
	UpperCAmelCase_								=     []
	for part_id in partition_order:
		UpperCAmelCase_								=     df.where(f"""SPARK_PARTITION_ID() = {part_id}"""   ).collect()
		for row_idx, row in enumerate(__UpperCamelCase   ):
			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 					A_(			):
	UpperCAmelCase_								=     pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
	UpperCAmelCase_								=     spark.range(100   ).repartition(1   )
	UpperCAmelCase_								=     Spark(__UpperCamelCase   )
	# 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 					A_(			):
	UpperCAmelCase_								=     pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
	UpperCAmelCase_								=     spark.range(10   ).repartition(2   )
	UpperCAmelCase_								=     [1, 0]
	UpperCAmelCase_								=     _generate_iterable_examples(__UpperCamelCase				,      __UpperCamelCase   )  # Reverse the partitions.
	UpperCAmelCase_								=     _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase				,      __UpperCamelCase   )

	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 					A_(			):
	UpperCAmelCase_								=     pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
	UpperCAmelCase_								=     spark.range(10   ).repartition(1   )
	UpperCAmelCase_								=     SparkExamplesIterable(__UpperCamelCase   )
	assert it.n_shards == 1
	for i, (row_id, row_dict) in enumerate(__UpperCamelCase   ):
		assert row_id == f"""0_{i}"""
		assert row_dict == {"id": i}




@require_not_windows
@require_dill_gt_0_3_2
def 					A_(			):
	UpperCAmelCase_								=     pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
	UpperCAmelCase_								=     spark.range(30   ).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(__UpperCamelCase				,      [2, 1, 0]   )

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

	# Partitions 0 and 2
	UpperCAmelCase_								=     SparkExamplesIterable(__UpperCamelCase   ).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(__UpperCamelCase				,      [0, 2]   )
	for i, (row_id, row_dict) in enumerate(__UpperCamelCase   ):
		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(__UpperCamelCase   ).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(__UpperCamelCase				,      [1, 3]   )
	for i, (row_id, row_dict) in enumerate(__UpperCamelCase   ):
		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 					A_(			):
	UpperCAmelCase_								=     pyspark.sql.SparkSession.builder.master("""local[*]"""   ).appName("""pyspark"""   ).getOrCreate()
	UpperCAmelCase_								=     spark.range(100   ).repartition(1   )
	UpperCAmelCase_								=     Spark(__UpperCamelCase   )
	# 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









import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def    A_(			A				,      A				,      **A   ):
	UpperCAmelCase_								=     AutoConfig.from_pretrained(A				,      **A   )
	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_config(A   )
	model.save_pretrained(A   )
	AutoTokenizer.from_pretrained(A   ).save_pretrained(A   )
	return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)


