'''simple docstring'''






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

import cva
import numpy as np

# Parrameters
lowerCAmelCase: List[str]							=						(7_2_0, 1_2_8_0)  # Height, Width
lowerCAmelCase: str							=						(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase: List[str]							=						1 / 1_0_0
lowerCAmelCase: Dict							=						''
lowerCAmelCase: Any							=						''
lowerCAmelCase: str							=						''
lowerCAmelCase: Union[str, Any]							=						2_5_0
def 				lowerCamelCase__	(						):
  a						: Any					       =					get_dataset(_A			,      _A    )
  for index in range(_A    ):
    a						: List[str]					       =					random.sample(range(len(_A    )    )			,      4    )
    a						: List[Any]					       =					update_image_and_anno(
        _A			,      _A			,      _A			,      _A			,      _A			,      filter_scale=_A			,      )

    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    a						: str					       =					random_chars(32    )
    a						: Tuple					       =					path.split(os.sep    )[-1].rsplit('.'			,      1    )[0]
    a						: str					       =					f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
    cva.imwrite(f"""{file_root}.jpg"""			,      _A			,      [cva.IMWRITE_JPEG_QUALITY, 85]    )
    print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""    )
    a						: List[str]					       =					[]
    for anno in new_annos:
      a						: Optional[Any]					       =					anno[3] - anno[1]
      a						: Dict					       =					anno[4] - anno[2]
      a						: List[str]					       =					anno[1] + width / 2
      a						: Union[str, Any]					       =					anno[2] + height / 2
      a						: int					       =					f"""{anno[0]} {x_center} {y_center} {width} {height}"""
      annos_list.append(_A    )
    with open(f"""{file_root}.txt"""			,      'w'    ) as outfile:
      outfile.write('\n'.join(line for line in annos_list    )    )
def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: Optional[int]					       =					[]
  a						: Dict					       =					[]
  for label_file in glob.glob(os.path.join(_A			,      '*.txt'    )    ):
    a						: int					       =					label_file.split(os.sep    )[-1].rsplit('.'			,      1    )[0]
    with open(_A    ) as in_file:
      a						: Tuple					       =					in_file.readlines()
    a						: List[str]					       =					os.path.join(_A			,      f"""{label_name}.jpg"""    )

    a						: Optional[int]					       =					[]
    for obj_list in obj_lists:
      a						: Tuple					       =					obj_list.rstrip('\n'    ).split(' '    )
      a						: List[str]					       =					float(obj[1]    ) - float(obj[3]    ) / 2
      a						: List[str]					       =					float(obj[2]    ) - float(obj[4]    ) / 2
      a						: Any					       =					float(obj[1]    ) + float(obj[3]    ) / 2
      a						: str					       =					float(obj[2]    ) + float(obj[4]    ) / 2

      boxes.append([int(obj[0]    ), xmin, ymin, xmax, ymax]    )
    if not boxes:
      continue
    img_paths.append(_A    )
    labels.append(_A    )
  return img_paths, labels
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A			,      _A			,      _A = 0.0			,      ):
  a						: Any					       =					np.zeros([output_size[0], output_size[1], 3]			,      dtype=np.uinta    )
  a						: Union[str, Any]					       =					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  a						: Union[str, Any]					       =					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  a						: Any					       =					int(scale_x * output_size[1]    )
  a						: List[Any]					       =					int(scale_y * output_size[0]    )

  a						: Dict					       =					[]
  a						: Any					       =					[]
  for i, index in enumerate(_A    ):
    a						: List[Any]					       =					all_img_list[index]
    path_list.append(_A    )
    a						: str					       =					all_annos[index]
    a						: str					       =					cva.imread(_A    )
    if i == 0:  # top-left
      a						: Any					       =					cva.resize(_A			,      (divid_point_x, divid_point_y)    )
      a						: Optional[Any]					       =					img
      for bbox in img_annos:
        a						: Any					       =					bbox[1] * scale_x
        a						: Union[str, Any]					       =					bbox[2] * scale_y
        a						: int					       =					bbox[3] * scale_x
        a						: Dict					       =					bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )
    elif i == 1:  # top-right
      a						: Union[str, Any]					       =					cva.resize(_A			,      (output_size[1] - divid_point_x, divid_point_y)    )
      a						: Optional[Any]					       =					img
      for bbox in img_annos:
        a						: List[Any]					       =					scale_x + bbox[1] * (1 - scale_x)
        a						: List[Any]					       =					bbox[2] * scale_y
        a						: Optional[int]					       =					scale_x + bbox[3] * (1 - scale_x)
        a						: Optional[int]					       =					bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )
    elif i == 2:  # bottom-left
      a						: int					       =					cva.resize(_A			,      (divid_point_x, output_size[0] - divid_point_y)    )
      a						: Optional[int]					       =					img
      for bbox in img_annos:
        a						: int					       =					bbox[1] * scale_x
        a						: Optional[int]					       =					scale_y + bbox[2] * (1 - scale_y)
        a						: int					       =					bbox[3] * scale_x
        a						: Dict					       =					scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )
    else:  # bottom-right
      a						: List[str]					       =					cva.resize(
          _A			,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y)    )
      a						: Any					       =					img
      for bbox in img_annos:
        a						: Optional[int]					       =					scale_x + bbox[1] * (1 - scale_x)
        a						: str					       =					scale_y + bbox[2] * (1 - scale_y)
        a						: Dict					       =					scale_x + bbox[3] * (1 - scale_x)
        a						: int					       =					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						: Optional[Any]					       =					[
        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__	(						_A    ):
  assert number_char > 1, "The number of character should greater than 1"
  a						: List[Any]					       =					ascii_lowercase + digits
  return "".join(random.choice(_A    ) for _ in range(_A    )    )


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




'''simple docstring'''






import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 							a__( lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   CTRLTokenizer
    lowercase__			=   False
    lowercase__			=   False







    def       lowercase_	(				self			:  Dict     ):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a						: Tuple					       =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
      a						: Union[str, Any]					       =					dict(zip(__snake_case						,      range(len(__snake_case     )     )     )     )
      a						: Union[str, Any]					       =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
      a						: Optional[Any]					       =					{'unk_token': '<unk>'}

      a						: Dict					       =					os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['vocab_file']     )
      a						: Optional[Any]					       =					os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['merges_file']     )
      with open(self.vocab_file						,      'w'						,      encoding='utf-8'     ) as fp:
        fp.write(json.dumps(__snake_case     ) + '\n'     )
      with open(self.merges_file						,      'w'						,      encoding='utf-8'     ) as fp:
        fp.write('\n'.join(__snake_case     )     )







    def       lowercase_	(				self			:  int						,      **__snake_case			:  str     ):
      kwargs.update(self.special_tokens_map     )
      return CTRLTokenizer.from_pretrained(self.tmpdirname						,      **__snake_case     )







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Any     ):
      a						: int					       =					'adapt react readapt apt'
      a						: Any					       =					'adapt react readapt apt'
      return input_text, output_text







    def       lowercase_	(				self			:  Dict     ):
      a						: Dict					       =					CTRLTokenizer(self.vocab_file						,      self.merges_file						,      **self.special_tokens_map     )
      a						: List[str]					       =					'adapt react readapt apt'
      a						: Dict					       =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
      a						: Any					       =					tokenizer.tokenize(__snake_case     )
      self.assertListEqual(__snake_case						,      __snake_case     )

      a						: Dict					       =					tokens + [tokenizer.unk_token]

      a						: Optional[Any]					       =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case     )						,      __snake_case     )