


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

import cva
SCREAMING_SNAKE_CASE :   Dict							      =				""
SCREAMING_SNAKE_CASE :   Any							      =				""
SCREAMING_SNAKE_CASE :   int							      =				""
SCREAMING_SNAKE_CASE :   Dict							      =				1  # (0 is vertical, 1 is horizontal)



def        UpperCamelCase_(   )					->		int:
    _lowercase	,       _lowercase :							Tuple   	=	get_dataset(lowerCamelCase_							,				lowerCamelCase_				)
    print('Processing...'				)
    _lowercase	,       _lowercase	,       _lowercase :							List[str]   	=	update_image_and_anno(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)

    for index, image in enumerate(lowerCamelCase_				):
        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        _lowercase :							str   	=	random_chars(32				)
        _lowercase :							Dict   	=	paths[index].split(os.sep				)[-1].rsplit('.'							,				1				)[0]
        _lowercase :							List[Any]   	=	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}'''				)
        _lowercase :							str   	=	[]
        for anno in new_annos[index]:
            _lowercase :							Optional[int]   	=	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        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							Optional[Any]   	=	[]
    _lowercase :							str   	=	[]
    for label_file in glob.glob(os.path.join(lowerCamelCase_							,				'*.txt'				)				):
        _lowercase :							int   	=	label_file.split(os.sep				)[-1].rsplit('.'							,				1				)[0]
        with open(lowerCamelCase_				) as in_file:
            _lowercase :							Optional[Any]   	=	in_file.readlines()
        _lowercase :							Any   	=	os.path.join(lowerCamelCase_							,				F'''{label_name}.jpg'''				)

        _lowercase :							List[Any]   	=	[]
        for obj_list in obj_lists:
            _lowercase :							str   	=	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        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_ = 1				)					->		int:
    _lowercase :							List[str]   	=	[]
    _lowercase :							List[Any]   	=	[]
    _lowercase :							Optional[int]   	=	[]
    for idx in range(len(lowerCamelCase_				)				):
        _lowercase :							Tuple   	=	[]
        _lowercase :							Union[str, Any]   	=	img_list[idx]
        path_list.append(lowerCamelCase_				)
        _lowercase :							str   	=	anno_list[idx]
        _lowercase :							str   	=	cva.imread(lowerCamelCase_				)
        if flip_type == 1:
            _lowercase :							List[Any]   	=	cva.flip(lowerCamelCase_							,				lowerCamelCase_				)
            for bbox in img_annos:
                _lowercase :							Optional[int]   	=	1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]				)
        elif flip_type == 0:
            _lowercase :							List[Any]   	=	cva.flip(lowerCamelCase_							,				lowerCamelCase_				)
            for bbox in img_annos:
                _lowercase :							Optional[int]   	=	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        UpperCamelCase_(   lowerCamelCase_ = 32				)					->		Optional[Any]:
    assert number_char > 1, "The number of character should greater than 1"
    _lowercase :							Tuple   	=	ascii_lowercase + digits
    return "".join(random.choice(lowerCamelCase_				) for _ in range(lowerCamelCase_				)				)


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






from typing import Dict

from .base import GenericTensor, Pipeline






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     int:




           """simple docstring"""

           if tokenize_kwargs is None:
               _lowercase :							Any   	=	{}

           if truncation is not None:
               if "truncation" in tokenize_kwargs:
                   raise ValueError(
                       'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)')
               _lowercase :							List[Any]   	=	truncation

           _lowercase :							Optional[Any]   	=	tokenize_kwargs

           _lowercase :							Tuple   	=	{}
           if return_tensors is not None:
               _lowercase :							Union[str, Any]   	=	return_tensors

           return preprocess_params, {}, postprocess_params
       def     UpperCamelCase  (      self,   lowerCamelCase,   **lowerCamelCase) ->     Dict[str, GenericTensor]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.framework
           _lowercase :							Dict   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)
           return model_inputs
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							str   	=	self.model(**lowerCamelCase)
           return model_outputs
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=False) ->     Dict:




           """simple docstring"""

           if return_tensors:
               return model_outputs[0]
           if self.framework == "pt":
               return model_outputs[0].tolist()
           elif self.framework == "tf":
               return model_outputs[0].numpy().tolist()




       def __call__(      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return super().__call__(*lowerCamelCase,   **lowerCamelCase)


