





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_		(    __A				:    Union[str, Any]						,					__A				:    List[str]       )			->	Optional[int]:

       """simple docstring"""

       a_      :	Dict							=						[]
       for part_id in partition_order:
              a_      :	Optional[int]							=						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  SCREAMING_SNAKE_CASE_		(    )			->	str:

       """simple docstring"""

       a_      :	Any							=						pyspark.sql.SparkSession.builder.master('local[*]'       ).appName('pyspark'       ).getOrCreate()
       a_      :	Optional[int]							=						spark.range(1_00       ).repartition(1       )
       a_      :	List[str]							=						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=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  SCREAMING_SNAKE_CASE_		(    )			->	Tuple:

       """simple docstring"""

       a_      :	Optional[Any]							=						pyspark.sql.SparkSession.builder.master('local[*]'       ).appName('pyspark'       ).getOrCreate()
       a_      :	Tuple							=						spark.range(10       ).repartition(2       )
       a_      :	List[Any]							=						[1, 0]
       a_      :	Tuple							=						_generate_iterable_examples(__A						,					__A       )  # Reverse the partitions.
       a_      :	Union[str, Any]							=						_get_expected_row_ids_and_row_dicts_for_partition_order(__A						,					__A       )

       for i, (row_id, row_dict) in enumerate(generate_fn()       ):
              a_      :	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  SCREAMING_SNAKE_CASE_		(    )			->	str:

       """simple docstring"""

       a_      :	Dict							=						pyspark.sql.SparkSession.builder.master('local[*]'       ).appName('pyspark'       ).getOrCreate()
       a_      :	Dict							=						spark.range(10       ).repartition(1       )
       a_      :	List[Any]							=						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  SCREAMING_SNAKE_CASE_		(    )			->	Optional[int]:

       """simple docstring"""

       a_      :	Any							=						pyspark.sql.SparkSession.builder.master('local[*]'       ).appName('pyspark'       ).getOrCreate()
       a_      :	Tuple							=						spark.range(30       ).repartition(3       )
       # Mock the generator so that shuffle reverses the partition indices.
       with patch('numpy.random.Generator'       ) as generator_mock:
              a_      :	int							=						lambda __A       : x.reverse()
              a_      :	Optional[Any]							=						_get_expected_row_ids_and_row_dicts_for_partition_order(__A						,					[2, 1, 0]       )

              a_      :	Tuple							=						SparkExamplesIterable(__A       ).shuffle_data_sources(__A       )
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(__A       ):
                     a_      :	Optional[int]							=						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_		(    )			->	Tuple:

       """simple docstring"""

       a_      :	Union[str, Any]							=						pyspark.sql.SparkSession.builder.master('local[*]'       ).appName('pyspark'       ).getOrCreate()
       a_      :	Optional[Any]							=						spark.range(20       ).repartition(4       )

       # Partitions 0 and 2
       a_      :	Optional[Any]							=						SparkExamplesIterable(__A       ).shard_data_sources(worker_id=0						,					num_workers=2       )
       assert shard_it_a.n_shards == 2
       a_      :	int							=						_get_expected_row_ids_and_row_dicts_for_partition_order(__A						,					[0, 2]       )
       for i, (row_id, row_dict) in enumerate(__A       ):
              a_      :	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
       a_      :	str							=						SparkExamplesIterable(__A       ).shard_data_sources(worker_id=1						,					num_workers=2       )
       assert shard_it_a.n_shards == 2
       a_      :	Tuple							=						_get_expected_row_ids_and_row_dicts_for_partition_order(__A						,					[1, 3]       )
       for i, (row_id, row_dict) in enumerate(__A       ):
              a_      :	List[str]							=						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:

       """simple docstring"""

       a_      :	List[str]							=						pyspark.sql.SparkSession.builder.master('local[*]'       ).appName('pyspark'       ).getOrCreate()
       a_      :	str							=						spark.range(1_00       ).repartition(1       )
       a_      :	Tuple							=						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_00






from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCAmelCase_   :				Dict						      = [
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed
      elif pkg == "accelerate":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_accelerate_available

         # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
         # Transformers with PyTorch
         if not is_accelerate_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(F'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int=None       )			->	int:

       """simple docstring"""

       require_version(deps[pkg]						,					__A       )


