


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

import cva
import numpy as np

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







def  SCREAMING_SNAKE_CASE_		(    )			->	None:

       """simple docstring"""

       a_							,	a_      :	Any							=						get_dataset(__A						,					__A       )
       for index in range(__A       ):
              a_      :	Tuple							=						random.sample(range(len(__A       )       )						,					4       )
              a_							,	a_							,	a_      :	Optional[int]							=						update_image_and_anno(
                  __A						,					__A						,					__A						,					__A						,					__A						,					filter_scale=__A						,					)

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              a_      :	Dict							=						random_chars(32       )
              a_      :	Any							=						path.split(os.sep       )[-1].rsplit('.'						,					1       )[0]
              a_      :	Tuple							=						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_      :	Tuple							=						[]
              for anno in new_annos:
                     a_      :	Optional[Any]							=						anno[3] - anno[1]
                     a_      :	Optional[Any]							=						anno[4] - anno[2]
                     a_      :	List[str]							=						anno[1] + width / 2
                     a_      :	List[Any]							=						anno[2] + height / 2
                     a_      :	List[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  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	tuple[list, list]:

       """simple docstring"""

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

              a_      :	Dict							=						[]
              for obj_list in obj_lists:
                     a_      :	List[Any]							=						obj_list.rstrip('\n'       ).split(' '       )
                     a_      :	Optional[Any]							=						float(obj[1]       ) - float(obj[3]       ) / 2
                     a_      :	List[Any]							=						float(obj[2]       ) - float(obj[4]       ) / 2
                     a_      :	Dict							=						float(obj[1]       ) + float(obj[3]       ) / 2
                     a_      :	int							=						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  SCREAMING_SNAKE_CASE_		(    __A				:    list						,					__A				:    list						,					__A				:    list[int]						,					__A				:    tuple[int, int]						,					__A				:    tuple[float, float]						,					__A				:    float = 0.0						,					)			->	tuple[list, list, str]:

       """simple docstring"""

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

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

       """simple docstring"""

       assert number_char > 1, "The number of character should greater than 1"
       a_      :	List[str]							=						ascii_lowercase + digits
       return "".join(random.choice(__A       ) for _ in range(__A       )       )


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






# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    List[Any]						,					__A				:    List[Any]						,					__A				:    Optional[int]       )			->	Dict:

       """simple docstring"""

       a_      :	Optional[Any]							=						multiprocessing.Manager()
       a_      :	Tuple							=						manager.list()

       a_      :	Optional[int]							=						multiprocessing.Process(target=__A						,					args=(check_program, result, timeout)       )
       p.start()
       p.join(timeout=timeout + 1       )
       if p.is_alive():
              p.kill()

       if not result:
              result.append('timed out'       )

       return {
           "task_id": task_id,
           "passed": result[0] == "passed",
           "result": result[0],
           "completion_id": completion_id,
       }







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    Optional[Any]						,					__A				:    Optional[Any]       )			->	Dict:

       """simple docstring"""

       with create_tempdir():
              # These system calls are needed when cleaning up tempdir.
              import os
              import shutil

              a_      :	Any							=						shutil.rmtree
              a_      :	List[str]							=						os.rmdir
              a_      :	List[Any]							=						os.chdir

              # Disable functionalities that can make destructive changes to the test.
              reliability_guard()

              # Run program.
              try:
                     a_      :	Any							=						{}
                     with swallow_io():
                            with time_limit(__A       ):
                                   exec(__A						,					__A       )
                     result.append('passed'       )
              except TimeoutException:
                     result.append('timed out'       )
              except BaseException as e:
                     result.append(F"""failed: {e}"""       )

              # Needed for cleaning up.
              a_      :	Tuple							=						rmtree
              a_      :	Any							=						rmdir
              a_      :	Optional[int]							=						chdir







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]       )			->	str:

       """simple docstring"""

       def signal_handler(__A				:    List[str]						,					__A				:    Dict       ):
              raise TimeoutException('Timed out!'       )

       signal.setitimer(signal.ITIMER_REAL						,					__A       )
       signal.signal(signal.SIGALRM						,					__A       )
       try:
              yield
       finally:
              signal.setitimer(signal.ITIMER_REAL						,					0       )







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    )			->	Dict:

       """simple docstring"""

       a_      :	int							=						WriteOnlyStringIO()
       with contextlib.redirect_stdout(__A       ):
              with contextlib.redirect_stderr(__A       ):
                     with redirect_stdin(__A       ):
                            yield







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    )			->	Optional[int]:

       """simple docstring"""

       with tempfile.TemporaryDirectory() as dirname:
              with chdir(__A       ):
                     yield dirname
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      pass
class 				SCREAMING_SNAKE_CASE__						(       io.StringIO    ):
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							*SCREAMING_SNAKE_CASE__       : List[str]  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    int:
             raise OSError
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							*SCREAMING_SNAKE_CASE__       : Tuple  ,							**SCREAMING_SNAKE_CASE__       : Dict  )	->    List[str]:
             raise OSError
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							*SCREAMING_SNAKE_CASE__       : Dict  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    Optional[Any]:
             raise OSError

      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							*SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             return False
class 				SCREAMING_SNAKE_CASE__						(       contextlib._RedirectStream    ):  # type: ignore
      snake_case__       :  Optional[int]       =		'''stdin'''







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple       )			->	Union[str, Any]:

       """simple docstring"""

       if root == ".":
              yield
              return
       a_      :	str							=						os.getcwd()
       os.chdir(__A       )
       try:
              yield
       except BaseException as exc:
              raise exc
       finally:
              os.chdir(__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict=None       )			->	Dict:

       """simple docstring"""

       if maximum_memory_bytes is not None:
              import resource

              resource.setrlimit(resource.RLIMIT_AS						,					(maximum_memory_bytes, maximum_memory_bytes)       )
              resource.setrlimit(resource.RLIMIT_DATA						,					(maximum_memory_bytes, maximum_memory_bytes)       )
              if not platform.uname().system == "Darwin":
                     resource.setrlimit(resource.RLIMIT_STACK						,					(maximum_memory_bytes, maximum_memory_bytes)       )

       faulthandler.disable()

       import builtins

       a_      :	str							=						None
       a_      :	str							=						None

       import os

       a_      :	Optional[int]							=						'1'

       a_      :	Any							=						None
       a_      :	int							=						None
       a_      :	int							=						None
       a_      :	Tuple							=						None
       a_      :	Dict							=						None
       a_      :	Any							=						None
       a_      :	Dict							=						None
       a_      :	Dict							=						None
       a_      :	List[str]							=						None
       a_      :	Optional[Any]							=						None
       a_      :	Tuple							=						None
       a_      :	Union[str, Any]							=						None
       a_      :	Dict							=						None
       a_      :	Dict							=						None
       a_      :	int							=						None
       a_      :	str							=						None
       a_      :	Union[str, Any]							=						None
       a_      :	str							=						None
       a_      :	Optional[int]							=						None
       a_      :	List[Any]							=						None
       a_      :	Union[str, Any]							=						None
       a_      :	Dict							=						None
       a_      :	Tuple							=						None
       a_      :	List[str]							=						None
       a_      :	str							=						None
       a_      :	str							=						None
       a_      :	str							=						None

       import shutil

       a_      :	Dict							=						None
       a_      :	Tuple							=						None
       a_      :	str							=						None

       import subprocess

       a_      :	str							=						None  # type: ignore

       a_      :	List[Any]							=						None

       import sys

       a_      :	Optional[int]							=						None
       a_      :	int							=						None
       a_      :	Any							=						None
       a_      :	str							=						None
       a_      :	str							=						None


