
"""simple docstring"""

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

import cva
UpperCamelCase     :    Optional[Any]       =			""
UpperCamelCase     :    int       =			""
UpperCamelCase     :    Optional[Any]       =			""
UpperCamelCase     :    Any       =			1  # (0 is vertical, 1 is horizontal)






def 							A						(    )				->     None:
 __UpperCamelCase  ,     __UpperCamelCase      =	get_dataset(snake_case    , snake_case		)
 print('Processing...'		)
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	update_image_and_anno(snake_case    , snake_case    , snake_case		)

 for index, image in enumerate(snake_case		):
  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __UpperCamelCase      =	random_chars(3_2		)
  __UpperCamelCase      =	paths[index].split(os.sep		)[-1].rsplit('.'    , 1		)[0]
  __UpperCamelCase      =	f'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
  cva.imwrite(f'/{file_root}.jpg'    , snake_case    , [cva.IMWRITE_JPEG_QUALITY, 8_5]		)
  print(f'Success {index+1}/{len(snake_case		)} with {file_name}'		)
  __UpperCamelCase      =	[]
  for anno in new_annos[index]:
   __UpperCamelCase      =	f'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
   annos_list.append(snake_case		)
  with open(f'/{file_root}.txt'    , 'w'		) as outfile:
   outfile.write('\n'.join(line for line in annos_list		)		)






def 							A						(    snake_case				:str    , snake_case				:str		)				->     tuple[list, list]:
 __UpperCamelCase      =	[]
 __UpperCamelCase      =	[]
 for label_file in glob.glob(os.path.join(snake_case    , '*.txt'		)		):
  __UpperCamelCase      =	label_file.split(os.sep		)[-1].rsplit('.'    , 1		)[0]
  with open(snake_case		) as in_file:
   __UpperCamelCase      =	in_file.readlines()
  __UpperCamelCase      =	os.path.join(snake_case    , f'{label_name}.jpg'		)

  __UpperCamelCase      =	[]
  for obj_list in obj_lists:
   __UpperCamelCase      =	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(snake_case		)
  labels.append(snake_case		)
 return img_paths, labels






def 							A						(    snake_case				:list    , snake_case				:list    , snake_case				:int = 1		)				->     tuple[list, list, list]:
 __UpperCamelCase      =	[]
 __UpperCamelCase      =	[]
 __UpperCamelCase      =	[]
 for idx in range(len(snake_case		)		):
  __UpperCamelCase      =	[]
  __UpperCamelCase      =	img_list[idx]
  path_list.append(snake_case		)
  __UpperCamelCase      =	anno_list[idx]
  __UpperCamelCase      =	cva.imread(snake_case		)
  if flip_type == 1:
   __UpperCamelCase      =	cva.flip(snake_case    , snake_case		)
   for bbox in img_annos:
    __UpperCamelCase      =	1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]		)
  elif flip_type == 0:
   __UpperCamelCase      =	cva.flip(snake_case    , snake_case		)
   for bbox in img_annos:
    __UpperCamelCase      =	1 - bbox[2]
    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]		)
  new_annos_lists.append(snake_case		)
  new_imgs_list.append(snake_case		)
 return new_imgs_list, new_annos_lists, path_list






def 							A						(    snake_case				:int = 3_2		)				->     str:
 assert number_char > 1, "The number of character should greater than 1"
 __UpperCamelCase      =	ascii_lowercase + digits
 return "".join(random.choice(snake_case		) for _ in range(snake_case		)		)


if __name__ == "__main__":
      main()
      print("DONE ✅")





"""simple docstring"""

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(
          __UpperCAmelCase     ,					split=__UpperCAmelCase     ,					features=__UpperCAmelCase     ,					cache_dir=__UpperCAmelCase     ,					keep_in_memory=__UpperCAmelCase     ,					streaming=__UpperCAmelCase     ,					num_proc=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)
      __UpperCamelCase      =	field
      __UpperCamelCase      =	path_or_paths if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else {self.split: path_or_paths}
      __UpperCamelCase      =	Json(
          cache_dir=__UpperCAmelCase     ,					data_files=__UpperCAmelCase     ,					features=__UpperCAmelCase     ,					field=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if self.streaming:
       __UpperCamelCase      =	self.builder.as_streaming_dataset(split=self.split				)
      # Build regular (map-style) dataset
      else:
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None

       self.builder.download_and_prepare(
           download_config=__UpperCAmelCase     ,					download_mode=__UpperCAmelCase     ,					verification_mode=__UpperCAmelCase     ,					base_path=__UpperCAmelCase     ,					num_proc=self.num_proc     ,					)
       __UpperCamelCase      =	self.builder.as_dataset(
           split=self.split     ,					verification_mode=__UpperCAmelCase     ,					in_memory=self.keep_in_memory				)
      return dataset


class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      if num_proc is not None and num_proc <= 0:
       raise ValueError(F'num_proc {num_proc} must be an integer > 0.'				)

      __UpperCamelCase      =	dataset
      __UpperCamelCase      =	path_or_buf
      __UpperCamelCase      =	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
      __UpperCamelCase      =	num_proc
      __UpperCamelCase      =	'utf-8'
      __UpperCamelCase      =	to_json_kwargs





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.to_json_kwargs.pop('path_or_buf'     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('orient'     ,					'records'				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('lines'     ,					True if orient == 'records' else False				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('index'     ,					False if orient in ['split', 'table'] else True				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('compression'     ,					__UpperCAmelCase				)

      if compression not in [None, "infer", "gzip", "bz2", "xz"]:
       raise NotImplementedError(F'`datasets` currently does not support {compression} compression'				)

      if isinstance(self.path_or_buf     ,					(str, bytes, os.PathLike)				):
       with fsspec.open(self.path_or_buf     ,					'wb'     ,					compression=__UpperCAmelCase				) as buffer:
        __UpperCamelCase      =	self._write(file_obj=__UpperCAmelCase     ,					orient=__UpperCAmelCase     ,					lines=__UpperCAmelCase     ,					index=__UpperCAmelCase     ,					**self.to_json_kwargs				)
      else:
       if compression:
        raise NotImplementedError(
            F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
            ' was passed. Please provide a local path instead.'				)
       __UpperCamelCase      =	self._write(
           file_obj=self.path_or_buf     ,					orient=__UpperCAmelCase     ,					lines=__UpperCAmelCase     ,					index=__UpperCAmelCase     ,					**self.to_json_kwargs				)
      return written





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	args

      __UpperCamelCase      =	query_table(
          table=self.dataset.data     ,					key=slice(__UpperCAmelCase     ,					offset + self.batch_size				)     ,					indices=self.dataset._indices     ,					)
      __UpperCamelCase      =	batch.to_pandas().to_json(
          path_or_buf=__UpperCAmelCase     ,					orient=__UpperCAmelCase     ,					lines=__UpperCAmelCase     ,					index=__UpperCAmelCase     ,					**__UpperCAmelCase				)
      if not json_str.endswith('\n'				):
       json_str += "\n"
      return json_str.encode(self.encoding				)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	0

      if self.num_proc is None or self.num_proc == 1:
       for offset in logging.tqdm(
           range(0     ,					len(self.dataset				)     ,					self.batch_size				)     ,					unit='ba'     ,					disable=not logging.is_progress_bar_enabled()     ,					desc='Creating json from Arrow format'     ,					):
        __UpperCamelCase      =	self._batch_json((offset, orient, lines, index, to_json_kwargs)				)
        written += file_obj.write(__UpperCAmelCase				)
      else:
       __UpperCamelCase  ,     __UpperCamelCase      =	len(self.dataset				), self.batch_size
       with multiprocessing.Pool(self.num_proc				) as pool:
        for json_str in logging.tqdm(
            pool.imap(
                self._batch_json     ,					[(offset, orient, lines, index, to_json_kwargs) for offset in range(0     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)]     ,					)     ,					total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,					unit='ba'     ,					disable=not logging.is_progress_bar_enabled()     ,					desc='Creating json from Arrow format'     ,					):
         written += file_obj.write(__UpperCAmelCase				)

      return written



