





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

import cva
_lowerCamelCase			       =      ""
_lowerCamelCase			       =      ""
_lowerCamelCase			       =      ""
_lowerCamelCase			       =      1  # (0 is vertical, 1 is horizontal)




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Union[str, Any]	=		get_dataset(_lowerCamelCase ,    _lowerCamelCase      )
       print('''Processing...'''      )
       _lowerCamelCase							:     List[Any]	=		update_image_and_anno(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase      )

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

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


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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase			       =      {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['LayoutLMv3TokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LayoutLMv3ForQuestionAnswering',
           'LayoutLMv3ForSequenceClassification',
           'LayoutLMv3ForTokenClassification',
           'LayoutLMv3Model',
           'LayoutLMv3PreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFLayoutLMv3ForQuestionAnswering',
           'TFLayoutLMv3ForSequenceClassification',
           'TFLayoutLMv3ForTokenClassification',
           'TFLayoutLMv3Model',
           'TFLayoutLMv3PreTrainedModel',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['LayoutLMv3FeatureExtractor']
       _lowerCamelCase			       =      ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
       from .configuration_layoutlmva import (
           LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LayoutLMvaConfig,
           LayoutLMvaOnnxConfig,
       )
       from .processing_layoutlmva import LayoutLMvaProcessor
       from .tokenization_layoutlmva import LayoutLMvaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlmva import (
                  LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMvaForQuestionAnswering,
                  LayoutLMvaForSequenceClassification,
                  LayoutLMvaForTokenClassification,
                  LayoutLMvaModel,
                  LayoutLMvaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_layoutlmva import (
                  TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLayoutLMvaForQuestionAnswering,
                  TFLayoutLMvaForSequenceClassification,
                  TFLayoutLMvaForTokenClassification,
                  TFLayoutLMvaModel,
                  TFLayoutLMvaPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
              from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

