


'''simple docstring'''






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

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







def 				UpperCamelCase_			(    ):
  '''simple docstring'''





  lowerCAmelCase_,							lowerCAmelCase_			:      List[Any]			=   get_dataset(A__							,    A__					)
  print("""Processing..."""					)
  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Optional[int]			=   update_image_and_anno(A__							,    A__							,    A__					)

  for index, image in enumerate(A__					):
    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    lowerCAmelCase_			:      Optional[int]			=   random_chars(32					)
    lowerCAmelCase_			:      List[Any]			=   paths[index].split(os.sep					)[-1].rsplit("""."""							,    1					)[0]
    lowerCAmelCase_			:      Optional[Any]			=   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}'					)
    lowerCAmelCase_			:      List[str]			=   []
    for anno in new_annos[index]:
      lowerCAmelCase_			:      Dict			=   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 				UpperCamelCase_			(    A__					:   str							,    A__					:   str					):
  '''simple docstring'''





  lowerCAmelCase_			:      int			=   []
  lowerCAmelCase_			:      Any			=   []
  for label_file in glob.glob(os.path.join(A__							,    """*.txt"""					)					):
    lowerCAmelCase_			:      Optional[Any]			=   label_file.split(os.sep					)[-1].rsplit("""."""							,    1					)[0]
    with open(A__					) as in_file:
      lowerCAmelCase_			:      Any			=   in_file.readlines()
    lowerCAmelCase_			:      Optional[int]			=   os.path.join(A__							,    f'{label_name}.jpg'					)

    lowerCAmelCase_			:      Dict			=   []
    for obj_list in obj_lists:
      lowerCAmelCase_			:      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(A__					)
    labels.append(A__					)
  return img_paths, labels







def 				UpperCamelCase_			(    A__					:   list							,    A__					:   list							,    A__					:   int = 1					):
  '''simple docstring'''





  lowerCAmelCase_			:      List[str]			=   []
  lowerCAmelCase_			:      Optional[Any]			=   []
  lowerCAmelCase_			:      Optional[Any]			=   []
  for idx in range(len(A__					)					):
    lowerCAmelCase_			:      Any			=   []
    lowerCAmelCase_			:      int			=   img_list[idx]
    path_list.append(A__					)
    lowerCAmelCase_			:      List[str]			=   anno_list[idx]
    lowerCAmelCase_			:      Optional[int]			=   cva.imread(A__					)
    if flip_type == 1:
      lowerCAmelCase_			:      List[str]			=   cva.flip(A__							,    A__					)
      for bbox in img_annos:
        lowerCAmelCase_			:      int			=   1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]					)
    elif flip_type == 0:
      lowerCAmelCase_			:      Any			=   cva.flip(A__							,    A__					)
      for bbox in img_annos:
        lowerCAmelCase_			:      List[Any]			=   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 				UpperCamelCase_			(    A__					:   int = 32					):
  '''simple docstring'''





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


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







'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A					:  List[Any]											=      logging.get_logger(__name__)

__A					:  Optional[Any]											=      {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""		)





    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) or len(self.rope_scaling		) != 2:
        raise ValueError(
            """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
            F'got {self.rope_scaling}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
      if rope_scaling_factor is None or not isinstance(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



