
import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowercase__		:Dict   =    {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					if got_ver is None or want_ver is None:
										raise ValueError(
										    f'Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'
										    f' reinstalling {pkg}.'		)
					if not ops[op](version.parse(lowerCAmelCase__		) ,			version.parse(lowerCAmelCase__		)		):
										raise ImportError(
										    f'{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'		)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ = None		):
					'''simple docstring'''
					lowercase							=						f'\n{hint}' if hint is not None else ''''''

					# non-versioned check
					if re.match(R'''^[\w_\-\d]+$''' ,			lowerCAmelCase__		):
										lowercase	,						lowercase	,						lowercase							=						requirement, None, None
					else:
										lowercase							=						re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)''' ,			lowerCAmelCase__		)
										if not match:
															raise ValueError(
															    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
															    f' got {requirement}'		)
										lowercase	,						lowercase							=						match[0]
										lowercase							=						want_full.split(''','''		)  # there could be multiple requirements
										lowercase							=						{}
										for w in want_range:
															lowercase							=						re.findall(R'''^([\s!=<>]{1,2})(.+)''' ,			lowerCAmelCase__		)
															if not match:
																				raise ValueError(
																				    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
																				    f' but got {requirement}'		)
															lowercase	,						lowercase							=						match[0]
															lowercase							=						want_ver
															if op not in ops:
																				raise ValueError(f'{requirement}: need one of {list(ops.keys()		)}, but got {op}'		)

    # special case
					if pkg == "python":
										lowercase							=						'''.'''.join([str(lowerCAmelCase__		) for x in sys.version_info[:3]]		)
										for op, want_ver in wanted.items():
															_compare_versions(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)
										return

					# check if any version is installed
					try:
										lowercase							=						importlib.metadata.version(lowerCAmelCase__		)
					except importlib.metadata.PackageNotFoundError:
										raise importlib.metadata.PackageNotFoundError(
										    f'The \'{requirement}\' distribution was not found and is required by this application. {hint}'		)

					# check that the right version is installed if version number or a range was provided
					if want_ver is not None:
										for op, want_ver in wanted.items():
															_compare_versions(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						'''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
					return require_version(lowerCAmelCase__ ,			lowerCAmelCase__		)









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       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Tuple:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	[]
				for part_id in partition_order:
								snake_case__	:      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 			_lowerCAmelCase       (					)   ->					Tuple:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Optional[Any]     		=	spark.range(100      ).repartition(1      )
				snake_case__	:      Optional[int]     		=	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 			_lowerCAmelCase       (					)   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(10      ).repartition(2      )
				snake_case__	:      Any     		=	[1, 0]
				snake_case__	:      Tuple     		=	_generate_iterable_examples(__lowerCAmelCase  ,  __lowerCAmelCase      )  # Reverse the partitions.
				snake_case__	:      Any     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  __lowerCAmelCase      )

				for i, (row_id, row_dict) in enumerate(generate_fn()      ):
								snake_case__		, 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       (					)   ->					Any:


				"""simple docstring"""

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


				"""simple docstring"""

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

								snake_case__	:      List[str]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shuffle_data_sources(__lowerCAmelCase      )
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
												snake_case__		, snake_case__	:      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 			_lowerCAmelCase       (					)   ->					List[Any]:


				"""simple docstring"""

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

				# Partitions 0 and 2
				snake_case__	:      List[Any]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shard_data_sources(worker_id=0  ,  num_workers=2      )
				assert shard_it_a.n_shards == 2
				snake_case__	:      int     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  [0, 2]      )
				for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
								snake_case__		, snake_case__	:      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
				snake_case__	:      List[str]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shard_data_sources(worker_id=1  ,  num_workers=2      )
				assert shard_it_a.n_shards == 2
				snake_case__	:      List[Any]     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  [1, 3]      )
				for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
								snake_case__		, 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       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      int     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(100      ).repartition(1      )
				snake_case__	:      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

