




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

import cva
import numpy as np

# Parrameters
_lowerCamelCase	:		Union[str, Any]							       =   (720, 1280)  # Height, Width
_lowerCamelCase	:		Tuple							       =   (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCamelCase	:		str							       =   1 / 100
_lowerCamelCase	:		List[str]							       =   """"""
_lowerCamelCase	:		List[str]							       =   """"""
_lowerCamelCase	:		Optional[Any]							       =   """"""
_lowerCamelCase	:		Dict							       =   250







def SCREAMING_SNAKE_CASE				(       )   ->       None:



       """simple docstring"""

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

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              A__       =    random_chars(32						)
              A__       =    path.split(os.sep						)[-1].rsplit('''.''' ,				1						)[0]
              A__       =    f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg""" ,				lowercase_ ,				[cva.IMWRITE_JPEG_QUALITY, 85]						)
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""						)
              A__       =    []
              for anno in new_annos:
                     A__       =    anno[3] - anno[1]
                     A__       =    anno[4] - anno[2]
                     A__       =    anno[1] + width / 2
                     A__       =    anno[2] + height / 2
                     A__       =    f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(lowercase_						)
              with open(f"""{file_root}.txt""" ,				'''w'''						) as outfile:
                     outfile.write('''\n'''.join(line for line in annos_list						)						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       tuple[list, list]:



       """simple docstring"""

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

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

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







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ = 0.0 ,				)   ->       tuple[list, list, str]:



       """simple docstring"""

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

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



       """simple docstring"""

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


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








import copy
import os
from typing import Union

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


_lowerCamelCase	:		Tuple							       =   logging.get_logger(__name__)

_lowerCamelCase	:		str							       =   {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''git_vision_model'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Any=768					, UpperCAmelCase__      :	int=3_072					, UpperCAmelCase__      :	List[str]=12					, UpperCAmelCase__      :	Dict=12					, UpperCAmelCase__      :	Optional[int]=3					, UpperCAmelCase__      :	List[Any]=224					, UpperCAmelCase__      :	Union[str, Any]=16					, UpperCAmelCase__      :	Union[str, Any]="quick_gelu"					, UpperCAmelCase__      :	Dict=1e-5					, UpperCAmelCase__      :	Union[str, Any]=0.0					, UpperCAmelCase__      :	Any=0.02					, **UpperCAmelCase__      :	Any					, )						->Optional[int]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           A__       =    hidden_size
           A__       =    intermediate_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    num_channels
           A__       =    patch_size
           A__       =    image_size
           A__       =    initializer_range
           A__       =    attention_dropout
           A__       =    layer_norm_eps
           A__       =    hidden_act





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Any					, UpperCAmelCase__      :	Union[str, os.PathLike]					, **UpperCAmelCase__      :	int)						->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__ ,							A__       =    cls.get_config_dict(UpperCAmelCase__					, **UpperCAmelCase__)

           # get the vision config dict if we are loading from GITConfig
           if config_dict.get('''model_type''') == "git":
                  A__       =    config_dict['''vision_config''']

           if "model_type" in config_dict and hasattr(cls					, '''model_type''') and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                      f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

           return cls.from_dict(UpperCAmelCase__					, **UpperCAmelCase__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''git'''


    def __init__(			self      :	Dict					, UpperCAmelCase__      :	Dict=None					, UpperCAmelCase__      :	int=30_522					, UpperCAmelCase__      :	Optional[int]=768					, UpperCAmelCase__      :	Dict=6					, UpperCAmelCase__      :	int=12					, UpperCAmelCase__      :	List[str]=3_072					, UpperCAmelCase__      :	str="gelu"					, UpperCAmelCase__      :	int=0.1					, UpperCAmelCase__      :	Union[str, Any]=0.1					, UpperCAmelCase__      :	List[Any]=1_024					, UpperCAmelCase__      :	List[str]=0.02					, UpperCAmelCase__      :	Any=1e-12					, UpperCAmelCase__      :	Union[str, Any]=0					, UpperCAmelCase__      :	List[Any]="absolute"					, UpperCAmelCase__      :	int=True					, UpperCAmelCase__      :	Any=False					, UpperCAmelCase__      :	int=101					, UpperCAmelCase__      :	Tuple=102					, UpperCAmelCase__      :	Dict=None					, **UpperCAmelCase__      :	List[str]					, )						->Any:





           '''simple docstring'''

           super().__init__(bos_token_id=UpperCAmelCase__					, eos_token_id=UpperCAmelCase__					, pad_token_id=UpperCAmelCase__					, **UpperCAmelCase__)

           if vision_config is None:
                  A__       =    {}
                  logger.info('''vision_config is None. initializing the GitVisionConfig with default values.''')

           A__       =    GitVisionConfig(**UpperCAmelCase__)
           A__       =    vocab_size
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    hidden_act
           A__       =    intermediate_size
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    max_position_embeddings
           A__       =    initializer_range
           A__       =    layer_norm_eps
           A__       =    position_embedding_type
           A__       =    use_cache
           A__       =    tie_word_embeddings
           A__       =    num_image_with_embedding

           A__       =    bos_token_id
           A__       =    eos_token_id





    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[Any]:





           '''simple docstring'''

           A__       =    copy.deepcopy(self.__dict__)
           A__       =    self.vision_config.to_dict()
           A__       =    self.__class__.model_type
           return output


