




'''simple docstring'''


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

import cva
UpperCAmelCase_ 		=      ""
UpperCAmelCase_ 		=      ""
UpperCAmelCase_ 		=      ""
UpperCAmelCase_ 		=      1  # (0 is vertical, 1 is horizontal)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					get_dataset(_lowerCamelCase				,					_lowerCamelCase			)
  print('Processing...'			)
  __a			=					update_image_and_anno(_lowerCamelCase				,					_lowerCamelCase				,					_lowerCamelCase			)

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							str			):
  __a			=					[]
  __a			=					[]
  for label_file in glob.glob(os.path.join(_lowerCamelCase				,					'*.txt'			)			):
    __a			=					label_file.split(os.sep			)[-1].rsplit('.'				,					1			)[0]
    with open(_lowerCamelCase			) as in_file:
      __a			=					in_file.readlines()
    __a			=					os.path.join(_lowerCamelCase				,					f"{label_name}.jpg"			)

    __a			=					[]
    for obj_list in obj_lists:
      __a			=					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(_lowerCamelCase			)
    labels.append(_lowerCamelCase			)
  return img_paths, labels


def 						SCREAMING_SNAKE_CASE       (      a_	:							list				,					a_	:							list				,					a_	:							int = 1			):
  __a			=					[]
  __a			=					[]
  __a			=					[]
  for idx in range(len(_lowerCamelCase			)			):
    __a			=					[]
    __a			=					img_list[idx]
    path_list.append(_lowerCamelCase			)
    __a			=					anno_list[idx]
    __a			=					cva.imread(_lowerCamelCase			)
    if flip_type == 1:
      __a			=					cva.flip(_lowerCamelCase				,					_lowerCamelCase			)
      for bbox in img_annos:
        __a			=					1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
    elif flip_type == 0:
      __a			=					cva.flip(_lowerCamelCase				,					_lowerCamelCase			)
      for bbox in img_annos:
        __a			=					1 - bbox[2]
        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
    new_annos_lists.append(_lowerCamelCase			)
    new_imgs_list.append(_lowerCamelCase			)
  return new_imgs_list, new_annos_lists, path_list


def 						SCREAMING_SNAKE_CASE       (      a_	:							int = 32			):
  assert number_char > 1, "The number of character should greater than 1"
  __a			=					ascii_lowercase + digits
  return "".join(random.choice(_lowerCamelCase			) for _ in range(_lowerCamelCase			)			)


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









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}





class    __lowercase						(  __magic_name__ ):
   _a							=      """distilbert"""
   _a							=      {
       """hidden_size""": """dim""",
       """num_attention_heads""": """n_heads""",
       """num_hidden_layers""": """n_layers""",
   }







   def __init__(	self      ,							UpperCamelCase=3_0522      ,							UpperCamelCase=512      ,							UpperCamelCase=False      ,							UpperCamelCase=6      ,							UpperCamelCase=12      ,							UpperCamelCase=768      ,							UpperCamelCase=4 * 768      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.1      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=0.02      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.2      ,							UpperCamelCase=0      ,							**UpperCamelCase      ,							)				->    int:
     __a			=					vocab_size
     __a			=					max_position_embeddings
     __a			=					sinusoidal_pos_embds
     __a			=					n_layers
     __a			=					n_heads
     __a			=					dim
     __a			=					hidden_dim
     __a			=					dropout
     __a			=					attention_dropout
     __a			=					activation
     __a			=					initializer_range
     __a			=					qa_dropout
     __a			=					seq_classif_dropout
     super().__init__(**UpperCamelCase      ,							pad_token_id=UpperCamelCase      )





class    __lowercase						(  __magic_name__ ):







   @property
   def 		UpperCamelCase__			(	self      )				->    Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
       __a			=					{0: 'batch', 1: 'choice', 2: 'sequence'}
     else:
       __a			=					{0: 'batch', 1: 'sequence'}
     return OrderedDict(
         [
             ('input_ids', dynamic_axis),
             ('attention_mask', dynamic_axis),
         ]      )



