


"""simple docstring"""







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 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase						,  lowerCAmelCase     :     int								=							get_dataset(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  print("Processing..."				)
  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Any								=							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'
    lowerCAmelCase     :     int								=							random_chars(3_2				)
    lowerCAmelCase     :     List[str]								=							paths[index].split(os.sep				)[-1].rsplit("."				,      1				)[0]
    lowerCAmelCase     :     Dict								=							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}"""				)
    lowerCAmelCase     :     Optional[Any]								=							[]
    for anno in new_annos[index]:
      lowerCAmelCase     :     str								=							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 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    str				):


  '''simple docstring'''







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

    lowerCAmelCase     :     str								=							[]
    for obj_list in obj_lists:
      lowerCAmelCase     :     List[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(SCREAMING_SNAKE_CASE				)
    labels.append(SCREAMING_SNAKE_CASE				)
  return img_paths, labels
def 					a__			(				SCREAMING_SNAKE_CASE    :    list				,      SCREAMING_SNAKE_CASE    :    list				,      SCREAMING_SNAKE_CASE    :    int = 1				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							[]
  lowerCAmelCase     :     str								=							[]
  lowerCAmelCase     :     List[Any]								=							[]
  for idx in range(len(SCREAMING_SNAKE_CASE				)				):
    lowerCAmelCase     :     str								=							[]
    lowerCAmelCase     :     str								=							img_list[idx]
    path_list.append(SCREAMING_SNAKE_CASE				)
    lowerCAmelCase     :     Any								=							anno_list[idx]
    lowerCAmelCase     :     int								=							cva.imread(SCREAMING_SNAKE_CASE				)
    if flip_type == 1:
      lowerCAmelCase     :     Any								=							cva.flip(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
      for bbox in img_annos:
        lowerCAmelCase     :     List[Any]								=							1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]				)
    elif flip_type == 0:
      lowerCAmelCase     :     Dict								=							cva.flip(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
      for bbox in img_annos:
        lowerCAmelCase     :     Tuple								=							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 					a__			(				SCREAMING_SNAKE_CASE    :    int = 3_2				):


  '''simple docstring'''







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


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









"""simple docstring"""







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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="llama"
       a  :				List[str]   =["past_key_values"]

       def __init__(						self		,     snake_case__=32_000		,     snake_case__=4_096		,     snake_case__=11_008		,     snake_case__=32		,     snake_case__=32		,     snake_case__=None		,     snake_case__="silu"		,     snake_case__=2_048		,     snake_case__=0.02		,     snake_case__=1e-6		,     snake_case__=True		,     snake_case__=0		,     snake_case__=1		,     snake_case__=2		,     snake_case__=1		,     snake_case__=False		,     snake_case__=None		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							vocab_size
         lowerCAmelCase     :     str								=							max_position_embeddings
         lowerCAmelCase     :     str								=							hidden_size
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Any								=							num_hidden_layers
         lowerCAmelCase     :     List[str]								=							num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
           lowerCAmelCase     :     Tuple								=							num_attention_heads

         lowerCAmelCase     :     Dict								=							num_key_value_heads
         lowerCAmelCase     :     Optional[Any]								=							hidden_act
         lowerCAmelCase     :     Optional[Any]								=							initializer_range
         lowerCAmelCase     :     Any								=							rms_norm_eps
         lowerCAmelCase     :     List[Any]								=							pretraining_tp
         lowerCAmelCase     :     int								=							use_cache
         lowerCAmelCase     :     List[str]								=							rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__		,     tie_word_embeddings=snake_case__		,     **snake_case__		,     )







       def        lowercase__							(						self						):






         """simple docstring"""





         if self.rope_scaling is None:
           return

         if not isinstance(self.rope_scaling		,     snake_case__						) 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"		,     snake_case__						)
         lowerCAmelCase     :     int								=							self.rope_scaling.get("factor"		,     snake_case__						)
         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(snake_case__		,     snake_case__						) or rope_scaling_factor <= 1.0:
           raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""						)





