





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

import cva
import numpy as np

# Parrameters
a__:      Tuple									=		(720, 1_280)  # Height, Width
a__:      Any									=		(0.4, 0.6)  # if height or width lower than this scale, drop it.
a__:      List[Any]									=		1 / 100
a__:      Tuple									=		''
a__:      Union[str, Any]									=		''
a__:      Tuple									=		''
a__:      Optional[int]									=		250


def 	UpperCamelCase__(       )->None:
       A__					,	A__											=						get_dataset(UpperCamelCase__ ,     UpperCamelCase__  )
       for index in range(UpperCamelCase__  ):
              A__											=						random.sample(range(len(UpperCamelCase__  )  ) ,     4  )
              A__					,	A__					,	A__											=						update_image_and_anno(
                  UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     filter_scale=UpperCamelCase__ ,     )

              # 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" ,     UpperCamelCase__ ,     [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(UpperCamelCase__  )
              with open(f"{file_root}.txt" ,     '''w'''  ) as outfile:
                     outfile.write('''\n'''.join(line for line in annos_list  )  )


def 	UpperCamelCase__(       UpperCamelCase__			:       str ,     UpperCamelCase__			:       str  )->tuple[list, list]:
       A__											=						[]
       A__											=						[]
       for label_file in glob.glob(os.path.join(UpperCamelCase__ ,     '''*.txt'''  )  ):
              A__											=						label_file.split(os.sep  )[-1].rsplit('''.''' ,     1  )[0]
              with open(UpperCamelCase__  ) as in_file:
                     A__											=						in_file.readlines()
              A__											=						os.path.join(UpperCamelCase__ ,     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(UpperCamelCase__  )
              labels.append(UpperCamelCase__  )
       return img_paths, labels


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       list ,     UpperCamelCase__			:       list[int] ,     UpperCamelCase__			:       tuple[int, int] ,     UpperCamelCase__			:       tuple[float, float] ,     UpperCamelCase__			:       float = 0.0 ,     )->tuple[list, list, str]:
       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(UpperCamelCase__  ):
              A__											=						all_img_list[index]
              path_list.append(UpperCamelCase__  )
              A__											=						all_annos[index]
              A__											=						cva.imread(UpperCamelCase__  )
              if i == 0:  # top-left
                     A__											=						cva.resize(UpperCamelCase__ ,     (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(UpperCamelCase__ ,     (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(UpperCamelCase__ ,     (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(
                         UpperCamelCase__ ,     (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 	UpperCamelCase__(       UpperCamelCase__			:       int  )->str:
       assert number_char > 1, "The number of character should greater than 1"
       A__											=						ascii_lowercase + digits
       return "".join(random.choice(UpperCamelCase__  ) for _ in range(UpperCamelCase__  )  )


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












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


a__:      Dict									=		logging.get_logger(__name__)

a__:      str									=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a__:      Any									=		{
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

a__:      Any									=		{
    'distilbert-base-uncased': 512,
    'distilbert-base-uncased-distilled-squad': 512,
    'distilbert-base-cased': 512,
    'distilbert-base-cased-distilled-squad': 512,
    'distilbert-base-german-cased': 512,
    'distilbert-base-multilingual-cased': 512,
}


a__:      Optional[Any]									=		{
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	DistilBertTokenizer



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=True,__lowerCamelCase="[UNK]",__lowerCamelCase="[SEP]",__lowerCamelCase="[PAD]",__lowerCamelCase="[CLS]",__lowerCamelCase="[MASK]",__lowerCamelCase=True,__lowerCamelCase=None,**__lowerCamelCase,):
        super().__init__(
            __lowerCamelCase,tokenizer_file=__lowerCamelCase,do_lower_case=__lowerCamelCase,unk_token=__lowerCamelCase,sep_token=__lowerCamelCase,pad_token=__lowerCamelCase,cls_token=__lowerCamelCase,mask_token=__lowerCamelCase,tokenize_chinese_chars=__lowerCamelCase,strip_accents=__lowerCamelCase,**__lowerCamelCase,)

        A__											=						json.loads(self.backend_tokenizer.normalizer.__getstate__() )
        if (
            normalizer_state.get('''lowercase''',__lowerCamelCase ) != do_lower_case
            or normalizer_state.get('''strip_accents''',__lowerCamelCase ) != strip_accents
            or normalizer_state.get('''handle_chinese_chars''',__lowerCamelCase ) != tokenize_chinese_chars
        ):
               A__											=						getattr(__lowerCamelCase,normalizer_state.pop('''type''' ) )
               A__											=						do_lower_case
               A__											=						strip_accents
               A__											=						tokenize_chinese_chars
               A__											=						normalizer_class(**__lowerCamelCase )

        A__											=						do_lower_case



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=None ):
        A__											=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

        return output



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						[self.sep_token_id]
        A__											=						[self.cls_token_id]
        if token_ids_a is None:
               return len(cls + token_ids_a + sep ) * [0]
        return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						self._tokenizer.model.save(__lowerCamelCase,name=__lowerCamelCase )
        return tuple(__lowerCamelCase )





