import glob
import os
import random
from string import ascii_lowercase, digits

import cva
UpperCAmelCase_			    =							''''''
UpperCAmelCase_			    =							''''''
UpperCAmelCase_			    =							''''''
UpperCAmelCase_			    =							1  # (0 is vertical, 1 is horizontal)




def       lowerCAmelCase_     (		)    ->				None:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     int							= get_dataset(lowercase  ,					lowercase		)
 print('''Processing...'''		)
 _UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase:     Dict							= update_image_and_anno(lowercase  ,					lowercase  ,					lowercase		)

 for index, image in enumerate(lowercase		):
  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  _UpperCamelCase:     Dict							= random_chars(32		)
  _UpperCamelCase:     Optional[int]							= paths[index].split(os.sep		)[-1].rsplit('''.'''  ,					1		)[0]
  _UpperCamelCase:     Any							= F"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
  cva.imwrite(F"""/{file_root}.jpg"""  ,					lowercase  ,					[cva.IMWRITE_JPEG_QUALITY, 85]		)
  print(F"""Success {index+1}/{len(lowercase		)} with {file_name}"""		)
  _UpperCamelCase:     Union[str, Any]							= []
  for anno in new_annos[index]:
   _UpperCamelCase:     Tuple							= F"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
   annos_list.append(lowercase		)
  with open(F"""/{file_root}.txt"""  ,					'''w'''		) as outfile:
   outfile.write('''\n'''.join(line for line in annos_list		)		)




def       lowerCAmelCase_     (		lowercase:							str  ,					lowercase:							str		)    ->				tuple[list, list]:







 '''simple docstring'''
 _UpperCamelCase:     Any							= []
 _UpperCamelCase:     List[Any]							= []
 for label_file in glob.glob(os.path.join(lowercase  ,					'''*.txt'''		)		):
  _UpperCamelCase:     Dict							= label_file.split(os.sep		)[-1].rsplit('''.'''  ,					1		)[0]
  with open(lowercase		) as in_file:
   _UpperCamelCase:     Optional[Any]							= in_file.readlines()
  _UpperCamelCase:     Tuple							= os.path.join(lowercase  ,					F"""{label_name}.jpg"""		)

  _UpperCamelCase:     Dict							= []
  for obj_list in obj_lists:
   _UpperCamelCase:     Tuple							= obj_list.rstrip('''\n'''		).split(''' '''		)
   boxes.append(
       [
           int(obj[0]		),
           float(obj[1]		),
           float(obj[2]		),
           float(obj[3]		),
           float(obj[4]		),
       ]		)
  if not boxes:
   continue
  img_paths.append(lowercase		)
  labels.append(lowercase		)
 return img_paths, labels




def       lowerCAmelCase_     (		lowercase:							list  ,					lowercase:							list  ,					lowercase:							int = 1		)    ->				tuple[list, list, list]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= []
 _UpperCamelCase:     Tuple							= []
 _UpperCamelCase:     int							= []
 for idx in range(len(lowercase		)		):
  _UpperCamelCase:     str							= []
  _UpperCamelCase:     List[str]							= img_list[idx]
  path_list.append(lowercase		)
  _UpperCamelCase:     Union[str, Any]							= anno_list[idx]
  _UpperCamelCase:     Optional[int]							= cva.imread(lowercase		)
  if flip_type == 1:
   _UpperCamelCase:     Union[str, Any]							= cva.flip(lowercase  ,					lowercase		)
   for bbox in img_annos:
    _UpperCamelCase:     List[Any]							= 1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]		)
  elif flip_type == 0:
   _UpperCamelCase:     List[str]							= cva.flip(lowercase  ,					lowercase		)
   for bbox in img_annos:
    _UpperCamelCase:     List[Any]							= 1 - bbox[2]
    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]		)
  new_annos_lists.append(lowercase		)
  new_imgs_list.append(lowercase		)
 return new_imgs_list, new_annos_lists, path_list




def       lowerCAmelCase_     (		lowercase:							int = 32		)    ->				str:







 '''simple docstring'''
 assert number_char > 1, "The number of character should greater than 1"
 _UpperCamelCase:     List[Any]							= ascii_lowercase + digits
 return "".join(random.choice(lowercase		) for _ in range(lowercase		)		)


if __name__ == "__main__":
  main()
  print('''DONE ✅''')
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_     (		lowercase:							Optional[Any]  ,					lowercase:							Tuple		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Union[str, Any]							= []
 for part_id in partition_order:
  _UpperCamelCase:     int							= df.where(F"""SPARK_PARTITION_ID() = {part_id}"""		).collect()
  for row_idx, row in enumerate(lowercase		):
   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_     (		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     int							= spark.range(100		).repartition(1		)
 _UpperCamelCase:     int							= Spark(lowercase		)
 # 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_     (		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     Optional[Any]							= spark.range(10		).repartition(2		)
 _UpperCamelCase:     int							= [1, 0]
 _UpperCamelCase:     Any							= _generate_iterable_examples(lowercase  ,					lowercase		)  # Reverse the partitions.
 _UpperCamelCase:     List[str]							= _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase  ,					lowercase		)

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







 '''simple docstring'''
 _UpperCamelCase:     Optional[Any]							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     Any							= spark.range(10		).repartition(1		)
 _UpperCamelCase:     int							= SparkExamplesIterable(lowercase		)
 assert it.n_shards == 1
 for i, (row_id, row_dict) in enumerate(lowercase		):
  assert row_id == F"""0_{i}"""
  assert row_dict == {"id": i}




@require_not_windows
@require_dill_gt_0_3_2
def       lowerCAmelCase_     (		)    ->				Union[str, Any]:







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

  _UpperCamelCase:     Union[str, Any]							= SparkExamplesIterable(lowercase		).shuffle_data_sources(lowercase		)
  assert shuffled_it.n_shards == 3
  for i, (row_id, row_dict) in enumerate(lowercase		):
   _UpperCamelCase	,							_UpperCamelCase:     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       lowerCAmelCase_     (		)    ->				Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:     str							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     Tuple							= spark.range(20		).repartition(4		)

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







 '''simple docstring'''
 _UpperCamelCase:     Any							= pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
 _UpperCamelCase:     Union[str, Any]							= spark.range(100		).repartition(1		)
 _UpperCamelCase:     Optional[Any]							= Spark(lowercase		)
 # 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