





"""simple docstring"""






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

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

def       __A (     )						->						None:
    __a	:  List[Any]         =					get_dataset(__lowerCAmelCase				,    __lowerCAmelCase)
    print('''Processing...''')
    __a	:  List[Any]         =					update_image_and_anno(__lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase)

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

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


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






"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					['''image_processor''', '''tokenizer''']
					__lowerCAmelCase					=					'''CLIPImageProcessor'''
					__lowerCAmelCase					=					('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')







					def __init__(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):
									__a	:  Optional[Any]         =					None
									if "feature_extractor" in kwargs:
													warnings.warn(
													    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
													    ''' instead.'''		,     _UpperCAmelCase		,     )
													__a	:  Any         =					kwargs.pop('''feature_extractor'''       )

									__a	:  Optional[Any]         =					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
													raise ValueError('''You need to specify an `image_processor`.'''       )
									if tokenizer is None:
													raise ValueError('''You need to specify a `tokenizer`.'''       )

									super().__init__(_UpperCAmelCase		,     _UpperCAmelCase       )







					def __call__(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):

									if text is None and images is None:
													raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

									if text is not None:
													__a	:  Any         =					self.tokenizer(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if images is not None:
													__a	:  List[str]         =					self.image_processor(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if text is not None and images is not None:
													__a	:  Optional[int]         =					image_features.pixel_values
													return encoding
									elif text is not None:
													return encoding
									else:
													return BatchEncoding(data=dict(**_UpperCAmelCase       )		,     tensor_type=_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return self.tokenizer.batch_decode(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return self.tokenizer.decode(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.tokenizer.model_input_names
									__a	:  Dict         =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )