





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


A__:    str  	=	datasets.utils.logging.get_logger(__name__)




@dataclass
class        _a    (    datasets.BuilderConfig):

      """simple docstring"""


      UpperCamelCase__  		=       None
      UpperCamelCase__  		=       "utf-8"
      UpperCamelCase__  		=       None
      UpperCamelCase__  		=       None
      UpperCamelCase__  		=       True  # deprecated
      UpperCamelCase__  		=       None  # deprecated
      UpperCamelCase__  		=       10 << 20  # 10MB
      UpperCamelCase__  		=       None




class        _a    (    datasets.ArrowBasedBuilder):

      """simple docstring"""


      UpperCamelCase__  		=       JsonConfig






      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          if self.config.block_size is not None:
              logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"					)
              UpperCamelCase__:	Dict					=     self.config.block_size
          if self.config.use_threads is not True:
              logger.warning(
                  "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."					)
          if self.config.newlines_in_values is not None:
              raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"					)
          return datasets.DatasetInfo(features=self.config.features					)






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			Optional[Any]					):
          '''simple docstring'''


          if not self.config.data_files:
              raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"					)
          UpperCamelCase__:	Any					=     dl_manager.download_and_extract(self.config.data_files					)
          if isinstance(__lowerCamelCase	,    (str, list, tuple)					):
              UpperCamelCase__:	List[str]					=     data_files
              if isinstance(__lowerCamelCase	,    __lowerCamelCase					):
                  UpperCamelCase__:	Optional[int]					=     [files]
              UpperCamelCase__:	List[Any]					=     [dl_manager.iter_files(__lowerCamelCase					) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,    gen_kwargs={"files": files}					)]
          UpperCamelCase__:	Optional[Any]					=     []
          for split_name, files in data_files.items():
              if isinstance(__lowerCamelCase	,    __lowerCamelCase					):
                  UpperCamelCase__:	List[str]					=     [files]
              UpperCamelCase__:	Any					=     [dl_manager.iter_files(__lowerCamelCase					) for file in files]
              splits.append(datasets.SplitGenerator(name=__lowerCamelCase	,    gen_kwargs={"files": files}					)					)
          return splits






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			pa.Table					):
          '''simple docstring'''


          if self.config.features is not None:
              # adding missing columns
              for column_name in set(self.config.features					) - set(pa_table.column_names					):
                  UpperCamelCase__:	int					=     self.config.features.arrow_schema.field(__lowerCamelCase					).type
                  UpperCamelCase__:	List[str]					=     pa_table.append_column(__lowerCamelCase	,    pa.array([None] * len(__lowerCamelCase					)	,    type=__lowerCamelCase					)					)
              # more expensive cast to support nested structures with keys in a different order
              # allows str <-> int/float or str to Audio for example
              UpperCamelCase__:	Any					=     table_cast(__lowerCamelCase	,    self.config.features.arrow_schema					)
          return pa_table






      def 			UpperCAmelCase_       (		self:			Optional[Any]	,    __lowerCamelCase:			Union[str, Any]					):
          '''simple docstring'''


          for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase					)					):
              # If the file is one json object and if we need to look at the list of items in one specific field
              if self.config.field is not None:
                  with open(__lowerCamelCase	,    encoding=self.config.encoding	,    errors=self.config.encoding_errors					) as f:
                      UpperCamelCase__:	int					=     json.load(__lowerCamelCase					)

                  # We keep only the field we are interested in
                  UpperCamelCase__:	Any					=     dataset[self.config.field]

                  # We accept two format: a list of dicts or a dict of lists
                  if isinstance(__lowerCamelCase	,    (list, tuple)					):
                      UpperCamelCase__:	Any					=     set().union(*[row.keys() for row in dataset]					)
                      UpperCamelCase__:	Tuple					=     {col: [row.get(__lowerCamelCase					) for row in dataset] for col in keys}
                  else:
                      UpperCamelCase__:	Tuple					=     dataset
                  UpperCamelCase__:	Optional[Any]					=     pa.Table.from_pydict(__lowerCamelCase					)
                  yield file_idx, self._cast_table(__lowerCamelCase					)

              # If the file has one json object per line
              else:
                  with open(__lowerCamelCase	,    "rb"					) as f:
                      UpperCamelCase__:	Optional[int]					=     0
                      # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                      # Set a default minimum value of 16kB if the chunk size is really small
                      UpperCamelCase__:	Optional[int]					=     max(self.config.chunksize // 32	,    16 << 10					)
                      UpperCamelCase__:	Optional[Any]					=     (
                          self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                      )
                      while True:
                          UpperCamelCase__:	Any					=     f.read(self.config.chunksize					)
                          if not batch:
                              break
                          # Finish current line
                          try:
                              batch += f.readline()
                          except (AttributeError, io.UnsupportedOperation):
                              batch += readline(__lowerCamelCase					)
                          # PyArrow only accepts utf-8 encoded bytes
                          if self.config.encoding != "utf-8":
                              UpperCamelCase__:	Optional[Any]					=     batch.decode(self.config.encoding	,    errors=__lowerCamelCase					).encode("utf-8"					)
                          try:
                              while True:
                                  try:
                                      UpperCamelCase__:	Any					=     paj.read_json(
                                          io.BytesIO(__lowerCamelCase					)	,    read_options=paj.ReadOptions(block_size=__lowerCamelCase					)					)
                                      break
                                  except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                      if (
                                          isinstance(__lowerCamelCase	,    pa.ArrowInvalid					)
                                          and "straddling" not in str(__lowerCamelCase					)
                                          or block_size > len(__lowerCamelCase					)
                                      ):
                                          raise
                                      else:
                                          # Increase the block size in case it was too small.
                                          # The block size will be reset for the next file.
                                          logger.debug(
                                              F"Batch of {len(__lowerCamelCase					)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."					)
                                          block_size *= 2
                          except pa.ArrowInvalid as e:
                              try:
                                  with open(
                                      __lowerCamelCase	,    encoding=self.config.encoding	,    errors=self.config.encoding_errors					) as f:
                                      UpperCamelCase__:	Dict					=     json.load(__lowerCamelCase					)
                              except json.JSONDecodeError:
                                  logger.error(F"Failed to read file '{file}' with error {type(__lowerCamelCase					)}: {e}"					)
                                  raise e
                              # If possible, parse the file as a list of json objects and exit the loop
                              if isinstance(__lowerCamelCase	,    __lowerCamelCase					):  # list is the only sequence type supported in JSON
                                  try:
                                      UpperCamelCase__:	Optional[int]					=     set().union(*[row.keys() for row in dataset]					)
                                      UpperCamelCase__:	List[str]					=     {col: [row.get(__lowerCamelCase					) for row in dataset] for col in keys}
                                      UpperCamelCase__:	List[str]					=     pa.Table.from_pydict(__lowerCamelCase					)
                                  except (pa.ArrowInvalid, AttributeError) as e:
                                      logger.error(F"Failed to read file '{file}' with error {type(__lowerCamelCase					)}: {e}"					)
                                      raise ValueError(F"Not able to read records in the JSON file at {file}."					) from None
                                  yield file_idx, self._cast_table(__lowerCamelCase					)
                                  break
                              else:
                                  logger.error(F"Failed to read file '{file}' with error {type(__lowerCamelCase					)}: {e}"					)
                                  raise ValueError(
                                      F"Not able to read records in the JSON file at {file}. "
                                      F"You should probably indicate the field of the JSON file containing your records. "
                                      F"This JSON file contain the following fields: {str(list(dataset.keys()					)					)}. "
                                      F"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "					) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                          yield (file_idx, batch_idx), self._cast_table(__lowerCamelCase					)
                          batch_idx += 1








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

import cva
import numpy as np

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



def 				lowerCAmelCase_ (							):
    UpperCamelCase__				, UpperCamelCase__:	Dict					=     get_dataset(A_    ,A_)
    for index in range(A_):
        UpperCamelCase__:	Any					=     random.sample(range(len(A_))    ,4)
        UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	Union[str, Any]					=     update_image_and_anno(
            A_    ,A_    ,A_    ,A_    ,A_    ,filter_scale=A_    ,)

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        UpperCamelCase__:	Tuple					=     random_chars(32)
        UpperCamelCase__:	Tuple					=     path.split(os.sep)[-1].rsplit("."    ,1)[0]
        UpperCamelCase__:	Optional[int]					=     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}")
        UpperCamelCase__:	Union[str, Any]					=     []
        for anno in new_annos:
            UpperCamelCase__:	int					=     anno[3] - anno[1]
            UpperCamelCase__:	Any					=     anno[4] - anno[2]
            UpperCamelCase__:	Optional[Any]					=     anno[1] + width / 2
            UpperCamelCase__:	Optional[int]					=     anno[2] + height / 2
            UpperCamelCase__:	Union[str, Any]					=     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_):
    UpperCamelCase__:	Any					=     []
    UpperCamelCase__:	List[Any]					=     []
    for label_file in glob.glob(os.path.join(A_    ,"*.txt")):
        UpperCamelCase__:	List[str]					=     label_file.split(os.sep)[-1].rsplit("."    ,1)[0]
        with open(A_) as in_file:
            UpperCamelCase__:	Optional[int]					=     in_file.readlines()
        UpperCamelCase__:	Any					=     os.path.join(A_    ,F"{label_name}.jpg")

        UpperCamelCase__:	Optional[Any]					=     []
        for obj_list in obj_lists:
            UpperCamelCase__:	Union[str, Any]					=     obj_list.rstrip("\n").split(" ")
            UpperCamelCase__:	str					=     float(obj[1]) - float(obj[3]) / 2
            UpperCamelCase__:	List[str]					=     float(obj[2]) - float(obj[4]) / 2
            UpperCamelCase__:	List[str]					=     float(obj[1]) + float(obj[3]) / 2
            UpperCamelCase__:	List[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    ,):
    UpperCamelCase__:	List[str]					=     np.zeros([output_size[0], output_size[1], 3]    ,dtype=np.uinta)
    UpperCamelCase__:	Tuple					=     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    UpperCamelCase__:	Tuple					=     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    UpperCamelCase__:	str					=     int(scale_x * output_size[1])
    UpperCamelCase__:	Tuple					=     int(scale_y * output_size[0])

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


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

