

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

import cva
a					:Optional[int]    = ""
a					:Dict    = ""
a					:Optional[int]    = ""
a					:Tuple    = 1  # (0 is vertical, 1 is horizontal)




def  _lowercase					(    )       ->				None:
      SCREAMING_SNAKE_CASE__   :      int      		=	get_dataset(__lowerCAmelCase  ,					__lowerCAmelCase							)
      print("""Processing..."""							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	update_image_and_anno(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)

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




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				tuple[list, list]:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[]
      SCREAMING_SNAKE_CASE__   :      int      		=	[]
      for label_file in glob.glob(os.path.join(__lowerCAmelCase  ,					"""*.txt"""							)							):
            SCREAMING_SNAKE_CASE__   :      Any      		=	label_file.split(os.sep							)[-1].rsplit("""."""  ,					1							)[0]
            with open(__lowerCAmelCase							) as in_file:
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	in_file.readlines()
            SCREAMING_SNAKE_CASE__   :      str      		=	os.path.join(__lowerCAmelCase  ,					F'''{label_name}.jpg'''							)

            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	[]
            for obj_list in obj_lists:
                  SCREAMING_SNAKE_CASE__   :      Any      		=	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(__lowerCAmelCase							)
            labels.append(__lowerCAmelCase							)
      return img_paths, labels




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase = 1							)       ->				tuple[list, list, list]:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	[]
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	[]
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	[]
      for idx in range(len(__lowerCAmelCase							)							):
            SCREAMING_SNAKE_CASE__   :      str      		=	[]
            SCREAMING_SNAKE_CASE__   :      int      		=	img_list[idx]
            path_list.append(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      Any      		=	anno_list[idx]
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	cva.imread(__lowerCAmelCase							)
            if flip_type == 1:
                  SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	cva.flip(__lowerCAmelCase  ,					__lowerCAmelCase							)
                  for bbox in img_annos:
                        SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	1 - bbox[1]
                        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]							)
            elif flip_type == 0:
                  SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	cva.flip(__lowerCAmelCase  ,					__lowerCAmelCase							)
                  for bbox in img_annos:
                        SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	1 - bbox[2]
                        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]							)
            new_annos_lists.append(__lowerCAmelCase							)
            new_imgs_list.append(__lowerCAmelCase							)
      return new_imgs_list, new_annos_lists, path_list




def  _lowercase					(    __lowerCAmelCase = 32							)       ->				str:
      assert number_char > 1, "The number of character should greater than 1"
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	ascii_lowercase + digits
      return "".join(random.choice(__lowerCAmelCase							) for _ in range(__lowerCAmelCase							)							)


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







"""simple docstring"""


import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
  import sqlitea

  import sqlalchemy

class      __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a = None	,    _a = None	,    _a = False	,    **_a	,    )  ->      Union[str, Any]:


           """simple docstring"""


           super().__init__(features=_a	,    cache_dir=_a	,    keep_in_memory=_a	,    **_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	Sql(
               cache_dir=_a	,    features=_a	,    sql=_a	,    con=_a	,    **_a	,    )





     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	None
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           SCREAMING_SNAKE_CASE__   :      Dict      		=	None
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	None

           self.builder.download_and_prepare(
               download_config=_a	,    download_mode=_a	,    verification_mode=_a	,    base_path=_a	,    )

           # Build dataset for splits
           SCREAMING_SNAKE_CASE__   :      str      		=	self.builder.as_dataset(
               split="""train"""	,    verification_mode=_a	,    in_memory=self.keep_in_memory  )
           return dataset





class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a	,    _a = None	,    _a = None	,    **_a	,    )  ->      Any:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      int      		=	dataset
           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	con
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           SCREAMING_SNAKE_CASE__   :      int      		=	num_proc
           SCREAMING_SNAKE_CASE__   :      int      		=	to_sql_kwargs

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.to_sql_kwargs.pop("""sql"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.to_sql_kwargs.pop("""con"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.to_sql_kwargs.pop("""index"""	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self._write(index=_a	,    **self.to_sql_kwargs  )
           return written

     def 					_a			(						self	,    _a  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	args
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
           SCREAMING_SNAKE_CASE__   :      Any      		=	query_table(
               table=self.dataset.data	,    key=slice(_a	,    offset + self.batch_size  )	,    indices=self.dataset._indices	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	batch.to_pandas()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	df.to_sql(self.name	,    self.con	,    index=_a	,    **_a  )
           return num_rows or len(_a  )





     def 					_a			(						self	,    _a	,    **_a  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	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 SQL from Arrow format"""	,    ):
                       written += self._batch_sql((offset, index, to_sql_kwargs)  )
           else:
                 SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      str      		=	len(self.dataset  ), self.batch_size
                 with multiprocessing.Pool(self.num_proc  ) as pool:
                       for num_rows in logging.tqdm(
                           pool.imap(
                               self._batch_sql	,    [(offset, index, to_sql_kwargs) for offset in range(0	,    _a	,    _a  )]	,    )	,    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 SQL from Arrow format"""	,    ):
                             written += num_rows

           return written




