






'''simple docstring'''



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

import cva
import numpy as np

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



def  __UpperCAmelCase						(					)	->      None:

   """simple docstring"""
   __a  ,      __a					  =     get_dataset(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   for index in range(SCREAMING_SNAKE_CASE__     ):
      __a					  =     random.sample(range(len(SCREAMING_SNAKE_CASE__     )     ),       4     )
      __a  ,      __a  ,      __a					  =     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'
      __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""",       SCREAMING_SNAKE_CASE__,       [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(SCREAMING_SNAKE_CASE__     )
      with open(f"""{file_root}.txt""",       'w'     ) as outfile:
         outfile.write('\n'.join(line for line in annos_list     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str     )	->      tuple[list, list]:

   """simple docstring"""
   __a					  =     []
   __a					  =     []
   for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE__,       '*.txt'     )     ):
      __a					  =     label_file.split(os.sep     )[-1].rsplit('.',       1     )[0]
      with open(SCREAMING_SNAKE_CASE__     ) as in_file:
         __a					  =     in_file.readlines()
      __a					  =     os.path.join(SCREAMING_SNAKE_CASE__,       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(SCREAMING_SNAKE_CASE__     )
      labels.append(SCREAMING_SNAKE_CASE__     )
   return img_paths, labels



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						list,       SCREAMING_SNAKE_CASE__:						list,       SCREAMING_SNAKE_CASE__:						list[int],       SCREAMING_SNAKE_CASE__:						tuple[int, int],       SCREAMING_SNAKE_CASE__:						tuple[float, float],       SCREAMING_SNAKE_CASE__:						float = 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(SCREAMING_SNAKE_CASE__     ):
      __a					  =     all_img_list[index]
      path_list.append(SCREAMING_SNAKE_CASE__     )
      __a					  =     all_annos[index]
      __a					  =     cva.imread(SCREAMING_SNAKE_CASE__     )
      if i == 0:  # top-left
         __a					  =     cva.resize(SCREAMING_SNAKE_CASE__,       (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(SCREAMING_SNAKE_CASE__,       (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(SCREAMING_SNAKE_CASE__,       (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(
             SCREAMING_SNAKE_CASE__,       (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						(					SCREAMING_SNAKE_CASE__:						int     )	->      str:

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


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







'''simple docstring'''



import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase   :							int         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["input_ids", "attention_mask"]




      def __init__(	self			, lowerCamelCase="</s>"			, lowerCamelCase="<unk>"			, lowerCamelCase="<pad>"			, lowerCamelCase=125			, lowerCamelCase=None			, **lowerCamelCase			, )				->None:



         '''simple docstring'''
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
            __a					  =     [F"""<extra_id_{i}>""" for i in range(lowerCamelCase   )]
         elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra_id special tokens
            __a					  =     len(set(filter(lambda lowerCamelCase   : bool('extra_id' in str(lowerCamelCase   )   )			, lowerCamelCase   )   )   )
            if extra_tokens != extra_ids:
               raise ValueError(
                   F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                   ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
                   ' extra_ids tokens'   )

         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else pad_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else eos_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else unk_token

         super().__init__(
             eos_token=lowerCamelCase			, unk_token=lowerCamelCase			, pad_token=lowerCamelCase			, extra_ids=lowerCamelCase			, additional_special_tokens=lowerCamelCase			, **lowerCamelCase			, )

         __a					  =     extra_ids

         __a					  =     2**8  # utf is 8 bits

         # define special tokens dict
         __a					  =     {
             self.pad_token: 0,
             self.eos_token: 1,
             self.unk_token: 2,
         }
         __a					  =     len(self.special_tokens_encoder   )
         __a					  =     len(lowerCamelCase   )
         for i, token in enumerate(lowerCamelCase   ):
            __a					  =     self.vocab_size + i - n
         __a					  =     {v: k for k, v in self.special_tokens_encoder.items()}




      @property
      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = False   )				->List[int]:



         '''simple docstring'''
         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowerCamelCase			, token_ids_a=lowerCamelCase			, already_has_special_tokens=lowerCamelCase   )

         # normal case: some special tokens
         if token_ids_a is None:
            return ([0] * len(lowerCamelCase   )) + [1]
         return ([0] * len(lowerCamelCase   )) + [1] + ([0] * len(lowerCamelCase   )) + [1]




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[int]:



         '''simple docstring'''
         if len(lowerCamelCase   ) > 0 and token_ids[-1] == self.eos_token_id:
            warnings.warn(
                F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                ' eos tokens being added.'   )
            return token_ids
         else:
            return token_ids + [self.eos_token_id]




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     [self.eos_token_id]

         if token_ids_a is None:
            return len(token_ids_a + eos   ) * [0]
         return len(token_ids_a + eos + token_ids_a + eos   ) * [0]




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     self._add_eos_if_not_present(lowerCamelCase   )
         if token_ids_a is None:
            return token_ids_a
         else:
            __a					  =     self._add_eos_if_not_present(lowerCamelCase   )
            return token_ids_a + token_ids_a




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     [chr(lowerCamelCase   ) for i in text.encode('utf-8'   )]
         return tokens




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Optional[Any]:



         '''simple docstring'''
         if token in self.special_tokens_encoder:
            __a					  =     self.special_tokens_encoder[token]
         elif token in self.added_tokens_encoder:
            __a					  =     self.added_tokens_encoder[token]
         elif len(lowerCamelCase   ) != 1:
            __a					  =     self.unk_token_id
         else:
            __a					  =     ord(lowerCamelCase   ) + self._num_special_tokens
         return token_id




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         if index in self.special_tokens_decoder:
            __a					  =     self.special_tokens_decoder[index]
         else:
            __a					  =     chr(index - self._num_special_tokens   )
         return token




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     B''
         for token in tokens:
            if token in self.special_tokens_decoder:
               __a					  =     self.special_tokens_decoder[token].encode('utf-8'   )
            elif token in self.added_tokens_decoder:
               __a					  =     self.special_tokens_decoder[token].encode('utf-8'   )
            elif token in self.special_tokens_encoder:
               __a					  =     token.encode('utf-8'   )
            elif token in self.added_tokens_encoder:
               __a					  =     token.encode('utf-8'   )
            else:
               __a					  =     bytes([ord(lowerCamelCase   )]   )
            bstring += tok_string
         __a					  =     bstring.decode('utf-8'			, errors='ignore'   )
         return string






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->Tuple[str]:



         '''simple docstring'''
         return ()