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_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						int			  =    []
							for part_id in partition_order:
														_SCREAMING_SNAKE_CASE		:						str			  =    df.where(F"""SPARK_PARTITION_ID() = {part_id}"""  ).collect()
														for row_idx, row in enumerate(__SCREAMING_SNAKE_CASE  ):
																					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_()->    Dict:
							_SCREAMING_SNAKE_CASE		:						Any			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    spark.range(100  ).repartition(1  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    Spark(__SCREAMING_SNAKE_CASE  )
							# 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_()->    int:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    spark.range(10  ).repartition(2  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    [1, 0]
							_SCREAMING_SNAKE_CASE		:						str			  =    _generate_iterable_examples(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )  # Reverse the partitions.
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							for i, (row_id, row_dict) in enumerate(generate_fn()  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_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_()->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    spark.range(10  ).repartition(1  )
							_SCREAMING_SNAKE_CASE		:						str			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  )
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE  ):
														assert row_id == F"""0_{i}"""
														assert row_dict == {"id": i}



@require_not_windows
@require_dill_gt_0_3_2
def      lowerCamelCase_()->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_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:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    lambda __SCREAMING_SNAKE_CASE  : x.reverse()
														_SCREAMING_SNAKE_CASE		:						str			  =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE     ,	[2, 1, 0]  )

														_SCREAMING_SNAKE_CASE		:						str			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  ).shuffle_data_sources(__SCREAMING_SNAKE_CASE  )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Dict			  =    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_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    spark.range(20  ).repartition(4  )

							# Partitions 0 and 2
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  ).shard_data_sources(worker_id=0     ,	num_workers=2  )
							assert shard_it_a.n_shards == 2
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE     ,	[0, 2]  )
							for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						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
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  ).shard_data_sources(worker_id=1     ,	num_workers=2  )
							assert shard_it_a.n_shards == 2
							_SCREAMING_SNAKE_CASE		:						Dict			  =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE     ,	[1, 3]  )
							for i, (row_id, row_dict) in enumerate(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, 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      lowerCamelCase_()->    int:
							_SCREAMING_SNAKE_CASE		:						Any			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Any			  =    spark.range(100  ).repartition(1  )
							_SCREAMING_SNAKE_CASE		:						int			  =    Spark(__SCREAMING_SNAKE_CASE  )
							# 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



"""simple docstring"""





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							int      ,    _A							:							List[Any]      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											if dst_width < 0 or dst_height < 0:
																		raise ValueError("""Destination width/height should be > 0""")

											_SCREAMING_SNAKE_CASE		:						str			  =    img
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    img.shape[1]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    img.shape[0]
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_width
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_height

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.src_w / self.dst_w
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.src_h / self.dst_h

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    np.ones((self.dst_h, self.dst_w, 3)      ,    np.uinta) * 2_5_5
											)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											for i in range(self.dst_h):
																		for j in range(self.dst_w):
																									_SCREAMING_SNAKE_CASE		:						Any			  =    self.img[self.get_y(_A)][self.get_x(_A)]





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_x * x)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_y * y)


if __name__ == "__main__":
			lowerCAmelCase_ ,					lowerCAmelCase_     =	800, 600
			lowerCAmelCase_     =	imread('''image_data/lena.jpg''', 1)
			lowerCAmelCase_     =	NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
			)
			waitKey(0)
			destroyAllWindows()


