






"""simple docstring"""





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

import cva
import numpy as np

# Parrameters
A__					: Tuple=	(7_20, 12_80)  # Height, Width
A__					: Tuple=	(0.4, 0.6)  # if height or width lower than this scale, drop it.
A__					: str=	1 / 1_00
A__					: int=	""""""
A__					: List[str]=	""""""
A__					: Optional[int]=	""""""
A__					: str=	2_50







def   lowerCAmelCase_( )					->	None:
    """simple docstring"""

    UpperCamelCase__       ,						UpperCamelCase__         =    get_dataset(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    for index in range(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    random.sample(range(len(SCREAMING_SNAKE_CASE					)					) ,	4					)
        UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    update_image_and_anno(
            SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	filter_scale=SCREAMING_SNAKE_CASE ,	)

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        UpperCamelCase__         =    random_chars(32					)
        UpperCamelCase__         =    path.split(os.sep					)[-1].rsplit('.' ,	1					)[0]
        UpperCamelCase__         =    F'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
        cva.imwrite(F'{file_root}.jpg' ,	SCREAMING_SNAKE_CASE ,	[cva.IMWRITE_JPEG_QUALITY, 85]					)
        print(F'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'					)
        UpperCamelCase__         =    []
        for anno in new_annos:
            UpperCamelCase__         =    anno[3] - anno[1]
            UpperCamelCase__         =    anno[4] - anno[2]
            UpperCamelCase__         =    anno[1] + width / 2
            UpperCamelCase__         =    anno[2] + height / 2
            UpperCamelCase__         =    F'{anno[0]} {x_center} {y_center} {width} {height}'
            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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	tuple[list, list]:
    """simple docstring"""

    UpperCamelCase__         =    []
    UpperCamelCase__         =    []
    for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE ,	'*.txt'					)					):
        UpperCamelCase__         =    label_file.split(os.sep					)[-1].rsplit('.' ,	1					)[0]
        with open(SCREAMING_SNAKE_CASE					) as in_file:
            UpperCamelCase__         =    in_file.readlines()
        UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	F'{label_name}.jpg'					)

        UpperCamelCase__         =    []
        for obj_list in obj_lists:
            UpperCamelCase__         =    obj_list.rstrip('\n'					).split(' '					)
            UpperCamelCase__         =    float(obj[1]					) - float(obj[3]					) / 2
            UpperCamelCase__         =    float(obj[2]					) - float(obj[4]					) / 2
            UpperCamelCase__         =    float(obj[1]					) + float(obj[3]					) / 2
            UpperCamelCase__         =    float(obj[2]					) + float(obj[4]					) / 2

            boxes.append([int(obj[0]					), xmin, ymin, xmax, ymax]					)
        if not boxes:
            continue
        img_paths.append(SCREAMING_SNAKE_CASE					)
        labels.append(SCREAMING_SNAKE_CASE					)
    return img_paths, labels







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = 0.0 ,	)					->	tuple[list, list, str]:
    """simple docstring"""

    UpperCamelCase__         =    np.zeros([output_size[0], output_size[1], 3] ,	dtype=np.uinta					)
    UpperCamelCase__         =    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    UpperCamelCase__         =    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    UpperCamelCase__         =    int(scale_x * output_size[1]					)
    UpperCamelCase__         =    int(scale_y * output_size[0]					)

    UpperCamelCase__         =    []
    UpperCamelCase__         =    []
    for i, index in enumerate(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    all_img_list[index]
        path_list.append(SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    all_annos[index]
        UpperCamelCase__         =    cva.imread(SCREAMING_SNAKE_CASE					)
        if i == 0:  # top-left
            UpperCamelCase__         =    cva.resize(SCREAMING_SNAKE_CASE ,	(divid_point_x, divid_point_y)					)
            UpperCamelCase__         =    img
            for bbox in img_annos:
                UpperCamelCase__         =    bbox[1] * scale_x
                UpperCamelCase__         =    bbox[2] * scale_y
                UpperCamelCase__         =    bbox[3] * scale_x
                UpperCamelCase__         =    bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
        elif i == 1:  # top-right
            UpperCamelCase__         =    cva.resize(SCREAMING_SNAKE_CASE ,	(output_size[1] - divid_point_x, divid_point_y)					)
            UpperCamelCase__         =    img
            for bbox in img_annos:
                UpperCamelCase__         =    scale_x + bbox[1] * (1 - scale_x)
                UpperCamelCase__         =    bbox[2] * scale_y
                UpperCamelCase__         =    scale_x + bbox[3] * (1 - scale_x)
                UpperCamelCase__         =    bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
        elif i == 2:  # bottom-left
            UpperCamelCase__         =    cva.resize(SCREAMING_SNAKE_CASE ,	(divid_point_x, output_size[0] - divid_point_y)					)
            UpperCamelCase__         =    img
            for bbox in img_annos:
                UpperCamelCase__         =    bbox[1] * scale_x
                UpperCamelCase__         =    scale_y + bbox[2] * (1 - scale_y)
                UpperCamelCase__         =    bbox[3] * scale_x
                UpperCamelCase__         =    scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
        else:  # bottom-right
            UpperCamelCase__         =    cva.resize(
                SCREAMING_SNAKE_CASE ,	(output_size[1] - divid_point_x, output_size[0] - divid_point_y)					)
            UpperCamelCase__         =    img
            for bbox in img_annos:
                UpperCamelCase__         =    scale_x + bbox[1] * (1 - scale_x)
                UpperCamelCase__         =    scale_y + bbox[2] * (1 - scale_y)
                UpperCamelCase__         =    scale_x + bbox[3] * (1 - scale_x)
                UpperCamelCase__         =    scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)

    # Remove bounding box small than scale of filter
    if filter_scale > 0:
        UpperCamelCase__         =    [
            anno
            for anno in new_anno
            if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
        ]

    return output_img, new_anno, path_list[0]







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    assert number_char > 1, "The number of character should greater than 1"
    UpperCamelCase__         =    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 argparse import ArgumentParser

from . import BaseTransformersCLICommand







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    return DownloadCommand(args.model ,	args.cache_dir ,	args.force ,	args.trust_remote_code					)





class    __lowerCamelCase    (  _a  ):

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    parser.add_parser('download'      )
       download_parser.add_argument(
           '--cache-dir' ,				type=snake_case_ ,				default=snake_case_ ,				help='Path to location to store the models'      )
       download_parser.add_argument(
           '--force' ,				action='store_true' ,				help='Force the model to be download even if already in cache-dir'      )
       download_parser.add_argument(
           '--trust-remote-code' ,				action='store_true' ,				help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine' ,				)
       download_parser.add_argument('model' ,				type=snake_case_ ,				help='Name of the model to download'      )
       download_parser.set_defaults(func=snake_case_      )

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       str:
       UpperCamelCase__         =    model
       UpperCamelCase__         =    cache
       UpperCamelCase__         =    force
       UpperCamelCase__         =    trust_remote_code







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       from ..models.auto import AutoModel, AutoTokenizer

       AutoModel.from_pretrained(
           self._model ,				cache_dir=self._cache ,				force_download=self._force ,				trust_remote_code=self._trust_remote_code      )
       AutoTokenizer.from_pretrained(
           self._model ,				cache_dir=self._cache ,				force_download=self._force ,				trust_remote_code=self._trust_remote_code      )




