




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


_snake_case										=       datasets.utils.logging.get_logger(__name__)







@dataclass
class 						lowercase							(   datasets.BuilderConfig		):
     _a							 =						None
     _a							 =						"utf-8"
     _a							 =						None
     _a							 =						None
     _a							 =						True  # deprecated
     _a							 =						None  # deprecated
     _a							 =						1_0 << 2_0  # 10MB
     _a							 =						None







class 						lowercase							(   datasets.ArrowBasedBuilder		):
     _a							 =						JsonConfig

     def 					a__	(  self				)						->							Union[str, Any]:
           if self.config.block_size is not None:
                 logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""				)
                 _A							:     Optional[Any]			=						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 					a__	(  self		,     _a				)						->							Union[str, Any]:
           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							:     Optional[Any]			=						dl_manager.download_and_extract(self.config.data_files				)
           if isinstance(_a		,     (str, list, tuple)				):
                 _A							:     List[str]			=						data_files
                 if isinstance(_a		,     _a				):
                       _A							:     Optional[int]			=						[files]
                 _A							:     str			=						[dl_manager.iter_files(_a				) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={"""files""": files}				)]
           _A							:     Tuple			=						[]
           for split_name, files in data_files.items():
                 if isinstance(_a		,     _a				):
                       _A							:     Optional[int]			=						[files]
                 _A							:     Tuple			=						[dl_manager.iter_files(_a				) for file in files]
                 splits.append(datasets.SplitGenerator(name=_a		,     gen_kwargs={"""files""": files}				)				)
           return splits

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



     def 					a__	(  self		,     _a				)						->							Optional[int]:
           for file_idx, file in enumerate(itertools.chain.from_iterable(_a				)				):
                 # 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(_a		,     encoding=self.config.encoding		,     errors=self.config.encoding_errors				) as f:
                             _A							:     Any			=						json.load(_a				)

                       # We keep only the field we are interested in
                       _A							:     Optional[int]			=						dataset[self.config.field]

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

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


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

import cva
import numpy as np

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



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		get_dataset(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for index in range(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		random.sample(range(len(SCREAMING_SNAKE_CASE	)	)  ,			4	)
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :str			  =		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'
        __UpperCamelCase     :List[Any]			  =		random_chars(32	)
        __UpperCamelCase     :List[str]			  =		path.split(os.sep	)[-1].rsplit('''.'''  ,			1	)[0]
        __UpperCamelCase     :Tuple			  =		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}"""	)
        __UpperCamelCase     :Optional[Any]			  =		[]
        for anno in new_annos:
            __UpperCamelCase     :int			  =		anno[3] - anno[1]
            __UpperCamelCase     :Optional[int]			  =		anno[4] - anno[2]
            __UpperCamelCase     :int			  =		anno[1] + width / 2
            __UpperCamelCase     :List[str]			  =		anno[2] + height / 2
            __UpperCamelCase     :str			  =		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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

        __UpperCamelCase     :int			  =		[]
        for obj_list in obj_lists:
            __UpperCamelCase     :Optional[int]			  =		obj_list.rstrip('''\n'''	).split(''' '''	)
            __UpperCamelCase     :Any			  =		float(obj[1]	) - float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) - float(obj[4]	) / 2
            __UpperCamelCase     :Dict			  =		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(SCREAMING_SNAKE_CASE	)
        labels.append(SCREAMING_SNAKE_CASE	)
    return img_paths, labels



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 0.0  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		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     :int			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :Optional[int]			  =		int(scale_x * output_size[1]	)
    __UpperCamelCase     :Any			  =		int(scale_y * output_size[0]	)

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







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


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





