

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

import cva
SCREAMING_SNAKE_CASE		:Optional[int]						=     """"""
SCREAMING_SNAKE_CASE		:List[str]						=     """"""
SCREAMING_SNAKE_CASE		:Optional[int]						=     """"""
SCREAMING_SNAKE_CASE		:Dict						=     1  # (0 is vertical, 1 is horizontal)




def 			lowerCAmelCase(      )->						None:



    """simple docstring"""






    UpperCamelCase_  ,       UpperCamelCase_			  =						get_dataset(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    print("Processing..."			)
    UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						update_image_and_anno(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    for index, image in enumerate(SCREAMING_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"			,						SCREAMING_SNAKE_CASE_			,						[cva.IMWRITE_JPEG_QUALITY, 8_5]			)
        print(f"Success {index+1}/{len(SCREAMING_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(SCREAMING_SNAKE_CASE_			)
        with open(f"/{file_root}.txt"			,						"w"			) as outfile:
            outfile.write("\n".join(line for line in annos_list			)			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						tuple[list, list]:



    """simple docstring"""






    UpperCamelCase_			  =						[]
    UpperCamelCase_			  =						[]
    for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE_			,						"*.txt"			)			):
        UpperCamelCase_			  =						label_file.split(os.sep			)[-1].rsplit("."			,						1			)[0]
        with open(SCREAMING_SNAKE_CASE_			) as in_file:
            UpperCamelCase_			  =						in_file.readlines()
        UpperCamelCase_			  =						os.path.join(SCREAMING_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(SCREAMING_SNAKE_CASE_			)
        labels.append(SCREAMING_SNAKE_CASE_			)
    return img_paths, labels




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_ = 1			)->						tuple[list, list, list]:



    """simple docstring"""






    UpperCamelCase_			  =						[]
    UpperCamelCase_			  =						[]
    UpperCamelCase_			  =						[]
    for idx in range(len(SCREAMING_SNAKE_CASE_			)			):
        UpperCamelCase_			  =						[]
        UpperCamelCase_			  =						img_list[idx]
        path_list.append(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						anno_list[idx]
        UpperCamelCase_			  =						cva.imread(SCREAMING_SNAKE_CASE_			)
        if flip_type == 1:
            UpperCamelCase_			  =						cva.flip(SCREAMING_SNAKE_CASE_			,						SCREAMING_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(SCREAMING_SNAKE_CASE_			,						SCREAMING_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(SCREAMING_SNAKE_CASE_			)
        new_imgs_list.append(SCREAMING_SNAKE_CASE_			)
    return new_imgs_list, new_annos_lists, path_list




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 3_2			)->						str:



    """simple docstring"""






    assert number_char > 1, "The number of character should greater than 1"
    UpperCamelCase_			  =						ascii_lowercase + digits
    return "".join(random.choice(SCREAMING_SNAKE_CASE_			) for _ in range(SCREAMING_SNAKE_CASE_			)			)


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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Any						=     [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
