



"""simple docstring"""

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

    for i, (row_id, row_dict) in enumerate(generate_fn()    ):
        snake_case_,	snake_case_ :     Tuple				=	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 __lowercase			(							):
    snake_case_ :     List[str]				=	pyspark.sql.SparkSession.builder.master('''local[*]'''    ).appName('''pyspark'''    ).getOrCreate()
    snake_case_ :     List[Any]				=	spark.range(10    ).repartition(1    )
    snake_case_ :     List[str]				=	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 __lowercase			(							):
    snake_case_ :     List[str]				=	pyspark.sql.SparkSession.builder.master('''local[*]'''    ).appName('''pyspark'''    ).getOrCreate()
    snake_case_ :     str				=	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_ :     str				=	lambda _a    : x.reverse()
        snake_case_ :     Tuple				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_a		,			[2, 1, 0]    )

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

    # Partitions 0 and 2
    snake_case_ :     Tuple				=	SparkExamplesIterable(_a    ).shard_data_sources(worker_id=0		,			num_workers=2    )
    assert shard_it_a.n_shards == 2
    snake_case_ :     List[str]				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_a		,			[0, 2]    )
    for i, (row_id, row_dict) in enumerate(_a    ):
        snake_case_,	snake_case_ :     Tuple				=	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_ :     str				=	SparkExamplesIterable(_a    ).shard_data_sources(worker_id=1		,			num_workers=2    )
    assert shard_it_a.n_shards == 2
    snake_case_ :     Tuple				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_a		,			[1, 3]    )
    for i, (row_id, row_dict) in enumerate(_a    ):
        snake_case_,	snake_case_ :     Optional[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 __lowercase			(							):
    snake_case_ :     int				=	pyspark.sql.SparkSession.builder.master('''local[*]'''    ).appName('''pyspark'''    ).getOrCreate()
    snake_case_ :     str				=	spark.range(100    ).repartition(1    )
    snake_case_ :     Dict				=	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() == 100










"""simple docstring"""

import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt




class        _UpperCAmelCase       :

 def __init__(     self		:       List[Any]				):
     snake_case_ :     List[str]				=	''''''
     snake_case_ :     Tuple				=	''''''
     snake_case_ :     int				=	[]
     snake_case_ :     Optional[int]				=	0
     snake_case_ :     Optional[Any]				=	256
     snake_case_ :     Tuple				=	0
     snake_case_ :     Tuple				=	0
     snake_case_ :     Optional[Any]				=	0
     snake_case_ :     Any				=	0

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[Any]				):
     snake_case_ :     List[Any]				=	cva.imread(lowercase_  ,     0				)
     snake_case_ :     Tuple				=	copy.deepcopy(self.img				)
     snake_case_,	snake_case_,	snake_case_ :     List[Any]				=	plt.hist(self.img.ravel()  ,     256  ,     [0, 256]  ,     label='''x'''				)
     snake_case_ :     str				=	np.sum(lowercase_				)
     for i in range(len(lowercase_				)				):
         snake_case_ :     Optional[Any]				=	x[i] / self.k
         self.sk += prk
         snake_case_ :     Any				=	(self.L - 1) * self.sk
         if self.rem != 0:
             snake_case_ :     Dict				=	int(last % last				)
         snake_case_ :     Union[str, Any]				=	int(last + 1 if self.rem >= 0.5 else last				)
         self.last_list.append(lowercase_				)
         snake_case_ :     int				=	int(np.ma.count(self.img				) / self.img[1].size				)
         snake_case_ :     Tuple				=	self.img[1].size
     for i in range(self.number_of_cols				):
         for j in range(self.number_of_rows				):
             snake_case_ :     Union[str, Any]				=	self.img[j][i]
             if num != self.last_list[num]:
                 snake_case_ :     List[str]				=	self.last_list[num]
     cva.imwrite('''output_data/output.jpg'''  ,     self.img				)

 def 						_snake_case					(     self		:       Tuple				):
     plt.hist(self.img.ravel()  ,     256  ,     [0, 256]				)






 def 						_snake_case					(     self		:       int				):
     cva.imshow('''Output-Image'''  ,     self.img				)
     cva.imshow('''Input-Image'''  ,     self.original_image				)
     cva.waitKey(5000				)
     cva.destroyAllWindows()


if __name__ == "__main__":
     lowercase__    :  Any							    =	os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
     lowercase__    :  Any							    =	ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()





