



"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits

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

def       _snake_case						(						):
 UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	get_dataset(UpperCamelCase			,		UpperCamelCase						)
 print("""Processing..."""						)
 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					int	=	update_image_and_anno(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

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

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		str						):
 UpperCAmelCase	:					List[Any]	=	[]
 UpperCAmelCase	:					Optional[int]	=	[]
 for label_file in glob.glob(os.path.join(UpperCamelCase			,		"""*.txt"""						)						):
  UpperCAmelCase	:					Tuple	=	label_file.split(os.sep						)[-1].rsplit("""."""			,		1						)[0]
  with open(UpperCamelCase						) as in_file:
   UpperCAmelCase	:					Union[str, Any]	=	in_file.readlines()
  UpperCAmelCase	:					Any	=	os.path.join(UpperCamelCase			,		F"{label_name}.jpg"						)

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

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

def       _snake_case						(						UpperCamelCase						:		int = 32						):
 assert number_char > 1, "The number of character should greater than 1"
 UpperCAmelCase	:					Union[str, Any]	=	ascii_lowercase + digits
 return "".join(random.choice(UpperCamelCase						) for _ in range(UpperCamelCase						)						)


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





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   str								=		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		["DistilBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[Any]								=		[
          "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DistilBertForMaskedLM",
          "DistilBertForMultipleChoice",
          "DistilBertForQuestionAnswering",
          "DistilBertForSequenceClassification",
          "DistilBertForTokenClassification",
          "DistilBertModel",
          "DistilBertPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		[
          "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDistilBertForMaskedLM",
          "TFDistilBertForMultipleChoice",
          "TFDistilBertForQuestionAnswering",
          "TFDistilBertForSequenceClassification",
          "TFDistilBertForTokenClassification",
          "TFDistilBertMainLayer",
          "TFDistilBertModel",
          "TFDistilBertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   int								=		[
          "FlaxDistilBertForMaskedLM",
          "FlaxDistilBertForMultipleChoice",
          "FlaxDistilBertForQuestionAnswering",
          "FlaxDistilBertForSequenceClassification",
          "FlaxDistilBertForTokenClassification",
          "FlaxDistilBertModel",
          "FlaxDistilBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      A:   Optional[int]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
