





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

import cva
import numpy as np

# Parrameters
SCREAMING_SNAKE_CASE_						       =	(7_2_0, 1_2_8_0)  # Height, Width
SCREAMING_SNAKE_CASE_						       =	(0.4, 0.6)  # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE_						       =	1 / 1_0_0
SCREAMING_SNAKE_CASE_						       =	""""""
SCREAMING_SNAKE_CASE_						       =	""""""
SCREAMING_SNAKE_CASE_						       =	""""""
SCREAMING_SNAKE_CASE_						       =	2_5_0



def        __lowercase       (				)			->       None:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						get_dataset(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )
      for index in range(_SCREAMING_SNAKE_CASE     ):
            SCREAMING_SNAKE_CASE		=						random.sample(range(len(_SCREAMING_SNAKE_CASE     )     )			,						4     )
            SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						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'
            SCREAMING_SNAKE_CASE		=						random_chars(32     )
            SCREAMING_SNAKE_CASE		=						path.split(os.sep     )[-1].rsplit("""."""			,						1     )[0]
            SCREAMING_SNAKE_CASE		=						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}"""     )
            SCREAMING_SNAKE_CASE		=						[]
            for anno in new_annos:
                  SCREAMING_SNAKE_CASE		=						anno[3] - anno[1]
                  SCREAMING_SNAKE_CASE		=						anno[4] - anno[2]
                  SCREAMING_SNAKE_CASE		=						anno[1] + width / 2
                  SCREAMING_SNAKE_CASE		=						anno[2] + height / 2
                  SCREAMING_SNAKE_CASE		=						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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       tuple[list, list]:


      '''simple docstring'''


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

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


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

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


      '''simple docstring'''


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


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









from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_						       =	TypeVar("""DatasetType""", Dataset, IterableDataset)



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "first_exhausted"			,						)			->       DatasetType:


      '''simple docstring'''


      from .arrow_dataset import Dataset
      from .iterable_dataset import IterableDataset

      if not datasets:
            raise ValueError("""Unable to interleave an empty list of datasets."""     )
      for i, dataset in enumerate(_SCREAMING_SNAKE_CASE     ):
            if not isinstance(_SCREAMING_SNAKE_CASE			,						(Dataset, IterableDataset)     ):
                  if isinstance(_SCREAMING_SNAKE_CASE			,						(DatasetDict, IterableDatasetDict)     ):
                        if not dataset:
                              raise ValueError(
                                  F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                  """is an empty dataset dictionary."""     )
                        raise ValueError(
                            F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE     )}\n"""
                            F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE     )     )}']"""     )
                  raise ValueError(
                      F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE     ).__name__}."""     )
            if i == 0:
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
                      (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                  raise ValueError(
                      F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
      if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
            raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""     )
      if dataset_type is Dataset:
            return _interleave_map_style_datasets(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						stopping_strategy=_SCREAMING_SNAKE_CASE     )
      else:
            return _interleave_iterable_datasets(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						stopping_strategy=_SCREAMING_SNAKE_CASE     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = 0			,						)			->       DatasetType:


      '''simple docstring'''


      if not dsets:
            raise ValueError("""Unable to concatenate an empty list of datasets."""     )
      for i, dataset in enumerate(_SCREAMING_SNAKE_CASE     ):
            if not isinstance(_SCREAMING_SNAKE_CASE			,						(Dataset, IterableDataset)     ):
                  if isinstance(_SCREAMING_SNAKE_CASE			,						(DatasetDict, IterableDatasetDict)     ):
                        if not dataset:
                              raise ValueError(
                                  F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                  """is an empty dataset dictionary."""     )
                        raise ValueError(
                            F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE     )}\n"""
                            F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE     )     )}']"""     )
                  raise ValueError(
                      F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE     ).__name__}."""     )
            if i == 0:
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
                      (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                  raise ValueError(
                      F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
      if dataset_type is Dataset:
            return _concatenate_map_style_datasets(_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						axis=_SCREAMING_SNAKE_CASE     )
      else:
            return _concatenate_iterable_datasets(_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						axis=_SCREAMING_SNAKE_CASE     )


