

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__       = logging.get_logger(__name__)






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						["""pixel_values"""]


 def __init__(						self		: List[Any]			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Optional[Dict[str, int]] = None			,						lowerCAmelCase__		: PILImageResampling = PILImageResampling.BILINEAR			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Dict[str, int] = None			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Union[int, float] = 1 / 2_5_5			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Optional[Union[float, List[float]]] = None			,						lowerCAmelCase__		: Optional[Union[float, List[float]]] = None			,						**lowerCAmelCase__		: List[str]			,						)    ->      None:
   super().__init__(**lowerCAmelCase__    )
   UpperCAmelCase					      =  size if size is not None else {"shortest_edge": 2_5_6}
   UpperCAmelCase					      =  get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__    )
   UpperCAmelCase					      =  crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
   UpperCAmelCase					      =  get_size_dict(lowerCAmelCase__    )
   UpperCAmelCase					      =  do_resize
   UpperCAmelCase					      =  size
   UpperCAmelCase					      =  resample
   UpperCAmelCase					      =  do_center_crop
   UpperCAmelCase					      =  crop_size
   UpperCAmelCase					      =  do_rescale
   UpperCAmelCase					      =  rescale_factor
   UpperCAmelCase					      =  do_normalize
   UpperCAmelCase					      =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   UpperCAmelCase					      =  image_std if image_std is not None else IMAGENET_STANDARD_STD


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: np.ndarray			,						lowerCAmelCase__		: Dict[str, int]			,						lowerCAmelCase__		: PILImageResampling = PILImageResampling.BICUBIC			,						lowerCAmelCase__		: Optional[Union[str, ChannelDimension]] = None			,						**lowerCAmelCase__		: Tuple			,						)    ->      np.ndarray:
   UpperCAmelCase					      =  get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__    )
   if "shortest_edge" not in size:
     raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"    )
   UpperCAmelCase					      =  get_resize_output_image_size(lowerCAmelCase__			,						size=size["shortest_edge"]			,						default_to_square=lowerCAmelCase__    )
   return resize(lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: np.ndarray			,						lowerCAmelCase__		: Dict[str, int]			,						lowerCAmelCase__		: Optional[Union[str, ChannelDimension]] = None			,						**lowerCAmelCase__		: int			,						)    ->      np.ndarray:
   UpperCAmelCase					      =  get_size_dict(lowerCAmelCase__    )
   return center_crop(lowerCAmelCase__			,						size=(size["height"], size["width"])			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[Any]			,						lowerCAmelCase__		: np.ndarray			,						lowerCAmelCase__		: float			,						lowerCAmelCase__		: Optional[Union[str, ChannelDimension]] = None			,						**lowerCAmelCase__		: Any    )    ->      np.ndarray:
   return rescale(lowerCAmelCase__			,						scale=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: np.ndarray			,						lowerCAmelCase__		: Union[float, List[float]]			,						lowerCAmelCase__		: Union[float, List[float]]			,						lowerCAmelCase__		: Optional[Union[str, ChannelDimension]] = None			,						**lowerCAmelCase__		: Optional[int]			,						)    ->      np.ndarray:
   return normalize(lowerCAmelCase__			,						mean=lowerCAmelCase__			,						std=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Tuple			,						lowerCAmelCase__		: ImageInput			,						lowerCAmelCase__		: Optional[bool] = None			,						lowerCAmelCase__		: Dict[str, int] = None			,						lowerCAmelCase__		: PILImageResampling = None			,						lowerCAmelCase__		: bool = None			,						lowerCAmelCase__		: Dict[str, int] = None			,						lowerCAmelCase__		: Optional[bool] = None			,						lowerCAmelCase__		: Optional[float] = None			,						lowerCAmelCase__		: Optional[bool] = None			,						lowerCAmelCase__		: Optional[Union[float, List[float]]] = None			,						lowerCAmelCase__		: Optional[Union[float, List[float]]] = None			,						lowerCAmelCase__		: Optional[Union[str, TensorType]] = None			,						lowerCAmelCase__		: Union[str, ChannelDimension] = ChannelDimension.FIRST			,						**lowerCAmelCase__		: str			,						)    ->      Dict:
   UpperCAmelCase					      =  do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase					      =  size if size is not None else self.size
   UpperCAmelCase					      =  get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__    )
   UpperCAmelCase					      =  resample if resample is not None else self.resample
   UpperCAmelCase					      =  do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase					      =  crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase					      =  get_size_dict(lowerCAmelCase__    )
   UpperCAmelCase					      =  do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase					      =  rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase					      =  do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase					      =  image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase					      =  image_std if image_std is not None else self.image_std

   UpperCAmelCase					      =  make_list_of_images(lowerCAmelCase__    )

   if not valid_images(lowerCAmelCase__    ):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray."    )

   if do_resize and size is None:
     raise ValueError("Size must be specified if do_resize is True."    )

   if do_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True."    )

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True."    )

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("Image mean and std must be specified if do_normalize is True."    )

   # All transformations expect numpy arrays.
   UpperCAmelCase					      =  [to_numpy_array(lowerCAmelCase__    ) for image in images]

   if do_resize:
     UpperCAmelCase					      =  [self.resize(image=lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__    ) for image in images]

   if do_center_crop:
     UpperCAmelCase					      =  [self.center_crop(image=lowerCAmelCase__			,						size=lowerCAmelCase__    ) for image in images]

   if do_rescale:
     UpperCAmelCase					      =  [self.rescale(image=lowerCAmelCase__			,						scale=lowerCAmelCase__    ) for image in images]

   if do_normalize:
     UpperCAmelCase					      =  [self.normalize(image=lowerCAmelCase__			,						mean=lowerCAmelCase__			,						std=lowerCAmelCase__    ) for image in images]

   UpperCAmelCase					      =  [to_channel_dimension_format(lowerCAmelCase__			,						lowerCAmelCase__    ) for image in images]

   UpperCAmelCase					      =  {"pixel_values": images}
   return BatchFeature(data=lowerCAmelCase__			,						tensor_type=lowerCAmelCase__    )






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      _lowerCAmelCase(	):
  UpperCAmelCase      ,	UpperCAmelCase					      =  get_dataset(__A   ,       __A			)
  print("Processing..."			)
  UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  update_image_and_anno(__A   ,       __A   ,       __A			)

  for index, image in enumerate(__A			):
    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    UpperCAmelCase					      =  random_chars(32			)
    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"   ,       __A   ,       [cva.IMWRITE_JPEG_QUALITY, 85]			)
    print(F"Success {index+1}/{len(__A			)} 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(__A			)
    with open(F"/{file_root}.txt"   ,       "w"			) as outfile:
      outfile.write("\n".join(line for line in annos_list			)			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  for label_file in glob.glob(os.path.join(__A   ,       "*.txt"			)			):
    UpperCAmelCase					      =  label_file.split(os.sep			)[-1].rsplit("."   ,       1			)[0]
    with open(__A			) as in_file:
      UpperCAmelCase					      =  in_file.readlines()
    UpperCAmelCase					      =  os.path.join(__A   ,       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(__A			)
    labels.append(__A			)
  return img_paths, labels
def      _lowerCAmelCase(	__A   ,       __A   ,       __A = 1			):
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  for idx in range(len(__A			)			):
    UpperCAmelCase					      =  []
    UpperCAmelCase					      =  img_list[idx]
    path_list.append(__A			)
    UpperCAmelCase					      =  anno_list[idx]
    UpperCAmelCase					      =  cva.imread(__A			)
    if flip_type == 1:
      UpperCAmelCase					      =  cva.flip(__A   ,       __A			)
      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(__A   ,       __A			)
      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(__A			)
    new_imgs_list.append(__A			)
  return new_imgs_list, new_annos_lists, path_list
def      _lowerCAmelCase(	__A = 32			):
  assert number_char > 1, "The number of character should greater than 1"
  UpperCAmelCase					      =  ascii_lowercase + digits
  return "".join(random.choice(__A			) for _ in range(__A			)			)


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



