






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

import cva
import numpy as np

# Parrameters
__UpperCAmelCase								=						(720, 1280)  # Height, Width
__UpperCAmelCase								=						(0.4, 0.6)  # if height or width lower than this scale, drop it.
__UpperCAmelCase								=						1 / 100
__UpperCAmelCase								=						''
__UpperCAmelCase								=						''
__UpperCAmelCase								=						''
__UpperCAmelCase								=						250




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						get_dataset(__snake_case     ,       __snake_case  )
					for index in range(__snake_case  ):
										UpperCAmelCase_    :      Optional[int]            =						random.sample(range(len(__snake_case  )  )     ,       4  )
										UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						update_image_and_anno(
										    __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       filter_scale=__snake_case     ,       )

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										UpperCAmelCase_    :      Optional[Any]            =						random_chars(32  )
										UpperCAmelCase_    :      Optional[int]            =						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"     ,       __snake_case     ,       [cva.IMWRITE_JPEG_QUALITY, 85]  )
										print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"  )
										UpperCAmelCase_    :      Optional[Any]            =						[]
										for anno in new_annos:
															UpperCAmelCase_    :      str            =						anno[3] - anno[1]
															UpperCAmelCase_    :      Any            =						anno[4] - anno[2]
															UpperCAmelCase_    :      Any            =						anno[1] + width / 2
															UpperCAmelCase_    :      Dict            =						anno[2] + height / 2
															UpperCAmelCase_    :      Union[str, Any]            =						F"{anno[0]} {x_center} {y_center} {width} {height}"
															annos_list.append(__snake_case  )
										with open(F"{file_root}.txt"     ,       'w'  ) as outfile:
															outfile.write('\n'.join(line for line in annos_list  )  )




def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							str  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						[]
					UpperCAmelCase_    :      Dict            =						[]
					for label_file in glob.glob(os.path.join(__snake_case     ,       '*.txt'  )  ):
										UpperCAmelCase_    :      Union[str, Any]            =						label_file.split(os.sep  )[-1].rsplit('.'     ,       1  )[0]
										with open(__snake_case  ) as in_file:
															UpperCAmelCase_    :      str            =						in_file.readlines()
										UpperCAmelCase_    :      Optional[int]            =						os.path.join(__snake_case     ,       F"{label_name}.jpg"  )

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

															boxes.append([int(obj[0]  ), xmin, ymin, xmax, ymax]  )
										if not boxes:
															continue
										img_paths.append(__snake_case  )
										labels.append(__snake_case  )
					return img_paths, labels




def      lowercase__      (     __snake_case    :							list     ,       __snake_case    :							list     ,       __snake_case    :							list[int]     ,       __snake_case    :							tuple[int, int]     ,       __snake_case    :							tuple[float, float]     ,       __snake_case    :							float = 0.0     ,       ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						np.zeros([output_size[0], output_size[1], 3]     ,       dtype=np.uinta  )
					UpperCAmelCase_    :      List[Any]            =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					UpperCAmelCase_    :      List[str]            =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					UpperCAmelCase_    :      Any            =						int(scale_x * output_size[1]  )
					UpperCAmelCase_    :      List[str]            =						int(scale_y * output_size[0]  )

					UpperCAmelCase_    :      Any            =						[]
					UpperCAmelCase_    :      Tuple            =						[]
					for i, index in enumerate(__snake_case  ):
										UpperCAmelCase_    :      Any            =						all_img_list[index]
										path_list.append(__snake_case  )
										UpperCAmelCase_    :      Optional[Any]            =						all_annos[index]
										UpperCAmelCase_    :      Tuple            =						cva.imread(__snake_case  )
										if i == 0:  # top-left
															UpperCAmelCase_    :      Any            =						cva.resize(__snake_case     ,       (divid_point_x, divid_point_y)  )
															UpperCAmelCase_    :      str            =						img
															for bbox in img_annos:
																				UpperCAmelCase_    :      Optional[int]            =						bbox[1] * scale_x
																				UpperCAmelCase_    :      str            =						bbox[2] * scale_y
																				UpperCAmelCase_    :      Dict            =						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_    :      Optional[int]            =						cva.resize(__snake_case     ,       (output_size[1] - divid_point_x, divid_point_y)  )
															UpperCAmelCase_    :      Dict            =						img
															for bbox in img_annos:
																				UpperCAmelCase_    :      Any            =						scale_x + bbox[1] * (1 - scale_x)
																				UpperCAmelCase_    :      Dict            =						bbox[2] * scale_y
																				UpperCAmelCase_    :      Dict            =						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_    :      Dict            =						cva.resize(__snake_case     ,       (divid_point_x, output_size[0] - divid_point_y)  )
															UpperCAmelCase_    :      Union[str, Any]            =						img
															for bbox in img_annos:
																				UpperCAmelCase_    :      str            =						bbox[1] * scale_x
																				UpperCAmelCase_    :      Union[str, Any]            =						scale_y + bbox[2] * (1 - scale_y)
																				UpperCAmelCase_    :      str            =						bbox[3] * scale_x
																				UpperCAmelCase_    :      Optional[Any]            =						scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
										else:  # bottom-right
															UpperCAmelCase_    :      Optional[Any]            =						cva.resize(
															    __snake_case     ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)  )
															UpperCAmelCase_    :      List[Any]            =						img
															for bbox in img_annos:
																				UpperCAmelCase_    :      List[str]            =						scale_x + bbox[1] * (1 - scale_x)
																				UpperCAmelCase_    :      int            =						scale_y + bbox[2] * (1 - scale_y)
																				UpperCAmelCase_    :      str            =						scale_x + bbox[3] * (1 - scale_x)
																				UpperCAmelCase_    :      Tuple            =						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_    :      int            =						[
										    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__      (     __snake_case    :							int  ):


					'''simple docstring'''


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


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












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


_lowerCamelCase	:		Optional[Any]							       =   datasets.utils.logging.get_logger(__name__)


@dataclass
class 				UpperCamelCase_    ( 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 				UpperCamelCase_    ( datasets.ArrowBasedBuilder     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      JsonConfig


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->str:





           '''simple docstring'''

           if self.config.block_size is not None:
                  logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''')
                  A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[Any])						->Dict:





           '''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}""")
           A__       =    dl_manager.download_and_extract(self.config.data_files)
           if isinstance(UpperCAmelCase__					, (str, list, tuple)):
                  A__       =    data_files
                  if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                         A__       =    [files]
                  A__       =    [dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  return [datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={'''files''': files})]
           A__       =    []
           for split_name, files in data_files.items():
                  if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                         A__       =    [files]
                  A__       =    [dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  splits.append(datasets.SplitGenerator(name=UpperCAmelCase__					, gen_kwargs={'''files''': files}))
           return splits


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	pa.Table)						->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):
                         A__       =    self.config.features.arrow_schema.field(UpperCAmelCase__).type
                         A__       =    pa_table.append_column(UpperCAmelCase__					, pa.array([None] * len(UpperCAmelCase__)					, type=UpperCAmelCase__))
                  # more expensive cast to support nested structures with keys in a different order
                  # allows str <-> int/float or str to Audio for example
                  A__       =    table_cast(UpperCAmelCase__					, self.config.features.arrow_schema)
           return pa_table





    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Tuple)						->str:





           '''simple docstring'''

           for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__)):
                  # 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(UpperCAmelCase__					, encoding=self.config.encoding					, errors=self.config.encoding_errors) as f:
                                A__       =    json.load(UpperCAmelCase__)

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

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

                  # If the file has one json object per line
                  else:
                         with open(UpperCAmelCase__					, '''rb''') as f:
                                A__       =    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
                                A__       =    max(self.config.chunksize // 32					, 16 << 10)
                                A__       =    (
                                    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                                )
                                while True:
                                       A__       =    f.read(self.config.chunksize)
                                       if not batch:
                                              break
                                       # Finish current line
                                       try:
                                              batch += f.readline()
                                       except (AttributeError, io.UnsupportedOperation):
                                              batch += readline(UpperCAmelCase__)
                                       # PyArrow only accepts utf-8 encoded bytes
                                       if self.config.encoding != "utf-8":
                                              A__       =    batch.decode(self.config.encoding					, errors=UpperCAmelCase__).encode('''utf-8''')
                                       try:
                                              while True:
                                                     try:
                                                            A__       =    paj.read_json(
                                                                io.BytesIO(UpperCAmelCase__)					, read_options=paj.ReadOptions(block_size=UpperCAmelCase__))
                                                            break
                                                     except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                            if (
                                                                isinstance(UpperCAmelCase__					, pa.ArrowInvalid)
                                                                and "straddling" not in str(UpperCAmelCase__)
                                                                or block_size > len(UpperCAmelCase__)
                                                            ):
                                                                   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(UpperCAmelCase__)} 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(
                                                         UpperCAmelCase__					, encoding=self.config.encoding					, errors=self.config.encoding_errors) as f:
                                                            A__       =    json.load(UpperCAmelCase__)
                                              except json.JSONDecodeError:
                                                     logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                     raise e
                                              # If possible, parse the file as a list of json objects and exit the loop
                                              if isinstance(UpperCAmelCase__					, UpperCAmelCase__):  # list is the only sequence type supported in JSON
                                                     try:
                                                            A__       =    set().union(*[row.keys() for row in dataset])
                                                            A__       =    {col: [row.get(UpperCAmelCase__) for row in dataset] for col in keys}
                                                            A__       =    pa.Table.from_pydict(UpperCAmelCase__)
                                                     except (pa.ArrowInvalid, AttributeError) as e:
                                                            logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                            raise ValueError(f"""Not able to read records in the JSON file at {file}.""") from None
                                                     yield file_idx, self._cast_table(UpperCAmelCase__)
                                                     break
                                              else:
                                                     logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {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(UpperCAmelCase__)
                                       batch_idx += 1


