






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       snake_case_		(     lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Optional[int]      ):
							__lowercase      :							Optional[int]				=     []
							for part_id in partition_order:
														__lowercase      :							List[Any]				=     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       snake_case_		(     ):
							__lowercase      :							Optional[int]				=     pyspark.sql.SparkSession.builder.master("""local[*]"""      ).appName("""pyspark"""      ).getOrCreate()
							__lowercase      :							str				=     spark.range(100      ).repartition(1      )
							__lowercase      :							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=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       snake_case_		(     ):
							__lowercase      :							List[Any]				=     pyspark.sql.SparkSession.builder.master("""local[*]"""      ).appName("""pyspark"""      ).getOrCreate()
							__lowercase      :							List[str]				=     spark.range(10      ).repartition(2      )
							__lowercase      :							List[Any]				=     [1, 0]
							__lowercase      :							Optional[int]				=     _generate_iterable_examples(lowerCAmelCase_       ,       lowerCAmelCase_      )  # Reverse the partitions.
							__lowercase      :							Optional[int]				=     _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase_       ,       lowerCAmelCase_      )

							for i, (row_id, row_dict) in enumerate(generate_fn()      ):
														__lowercase			,			__lowercase      :							List[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       snake_case_		(     ):
							__lowercase      :							List[Any]				=     pyspark.sql.SparkSession.builder.master("""local[*]"""      ).appName("""pyspark"""      ).getOrCreate()
							__lowercase      :							Optional[Any]				=     spark.range(10      ).repartition(1      )
							__lowercase      :							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       snake_case_		(     ):
							__lowercase      :							Optional[Any]				=     pyspark.sql.SparkSession.builder.master("""local[*]"""      ).appName("""pyspark"""      ).getOrCreate()
							__lowercase      :							Optional[Any]				=     spark.range(30      ).repartition(3      )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch("""numpy.random.Generator"""      ) as generator_mock:
														__lowercase      :							List[Any]				=     lambda lowerCAmelCase_      : x.reverse()
														__lowercase      :							Dict				=     _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase_       ,       [2, 1, 0]      )

														__lowercase      :							str				=     SparkExamplesIterable(lowerCAmelCase_      ).shuffle_data_sources(lowerCAmelCase_      )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(lowerCAmelCase_      ):
																					__lowercase			,			__lowercase      :							List[str]				=     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       snake_case_		(     ):
							__lowercase      :							Dict				=     pyspark.sql.SparkSession.builder.master("""local[*]"""      ).appName("""pyspark"""      ).getOrCreate()
							__lowercase      :							Dict				=     spark.range(20      ).repartition(4      )

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

							# Partitions 1 and 3
							__lowercase      :							List[str]				=     SparkExamplesIterable(lowerCAmelCase_      ).shard_data_sources(worker_id=1       ,       num_workers=2      )
							assert shard_it_a.n_shards == 2
							__lowercase      :							List[str]				=     _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase_       ,       [1, 3]      )
							for i, (row_id, row_dict) in enumerate(lowerCAmelCase_      ):
														__lowercase			,			__lowercase      :							Dict				=     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       snake_case_		(     ):
							__lowercase      :							Tuple				=     pyspark.sql.SparkSession.builder.master("""local[*]"""      ).appName("""pyspark"""      ).getOrCreate()
							__lowercase      :							Dict				=     spark.range(100      ).repartition(1      )
							__lowercase      :							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() == 100







from argparse import ArgumentParser

from . import BaseTransformersCLICommand
def       snake_case_		(     lowerCAmelCase_       :						Dict      ):
							return DownloadCommand(args.model       ,       args.cache_dir       ,       args.force       ,       args.trust_remote_code      )



class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					@staticmethod
					def       lowerCAmelCase				(  __a				:	ArgumentParser							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     parser.add_parser("""download"""							)
												download_parser.add_argument(
												    """--cache-dir"""		,     type=__a		,     default=__a		,     help="""Path to location to store the models"""							)
												download_parser.add_argument(
												    """--force"""		,     action="""store_true"""		,     help="""Force the model to be download even if already in cache-dir"""							)
												download_parser.add_argument(
												    """--trust-remote-code"""		,     action="""store_true"""		,     help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""		,     )
												download_parser.add_argument("""model"""		,     type=__a		,     help="""Name of the model to download"""							)
												download_parser.set_defaults(func=__a							)



					def __init__(  self				:	Dict		,     __a				:	str		,     __a				:	str		,     __a				:	bool		,     __a				:	bool							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     model
												__lowercase      :							List[Any]				=     cache
												__lowercase      :							Any				=     force
												__lowercase      :							Optional[int]				=     trust_remote_code



					def       lowerCAmelCase				(  self				:	str							)			->  List[str]:


												"""simple docstring"""

												from ..models.auto import AutoModel, AutoTokenizer

												AutoModel.from_pretrained(
												    self._model		,     cache_dir=self._cache		,     force_download=self._force		,     trust_remote_code=self._trust_remote_code							)
												AutoTokenizer.from_pretrained(
												    self._model		,     cache_dir=self._cache		,     force_download=self._force		,     trust_remote_code=self._trust_remote_code							)