




import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


_lowerCamelCase			:	Optional[int]							 =     logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					set()
       A__   				=					[]

       def parse_line(lowercase_		):
              for line in fp:
                     if isinstance(lowercase_				,       lowercase_		):
                            A__   				=					line.decode('''UTF-8'''		)
                     if "warnings summary (final)" in line:
                            continue
                     # This means we are outside the body of a warning
                     elif not line.startswith(''' '''		):
                            # process a single warning and move it to `selected_warnings`.
                            if len(lowercase_		) > 0:
                                   A__   				=					'''\n'''.join(lowercase_		)
                                   # Only keep the warnings specified in `targets`
                                   if any(f""": {x}: """ in warning for x in targets		):
                                          selected_warnings.add(lowercase_		)
                                   buffer.clear()
                            continue
                     else:
                            A__   				=					line.strip()
                            buffer.append(lowercase_		)

       if from_gh:
              for filename in os.listdir(lowercase_		):
                     A__   				=					os.path.join(lowercase_				,       lowercase_		)
                     if not os.path.isdir(lowercase_		):
                            # read the file
                            if filename != "warnings.txt":
                                   continue
                            with open(lowercase_		) as fp:
                                   parse_line(lowercase_		)
       else:
              try:
                     with zipfile.ZipFile(lowercase_		) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(lowercase_		):
                                          # read the file
                                          if filename != "warnings.txt":
                                                 continue
                                          with z.open(lowercase_		) as fp:
                                                 parse_line(lowercase_		)
              except Exception:
                     logger.warning(
                         f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""		)

       return selected_warnings







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Tuple:



       """simple docstring"""

       A__   				=					set()

       A__   				=					[os.path.join(lowercase_				,       lowercase_		) for p in os.listdir(lowercase_		) if (p.endswith('''.zip'''		) or from_gh)]
       for p in paths:
              selected_warnings.update(extract_warnings_from_single_artifact(lowercase_				,       lowercase_		)		)

       return selected_warnings


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Optional[int]:



         """simple docstring"""

         return values.split(''','''		)

  _lowerCamelCase			:	str							 =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
  parser.add_argument(
      """--output_dir""",
      type=str,
      required=True,
      help="""Where to store the downloaded artifacts and other result files.""",
  )
  parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
  # optional parameters
  parser.add_argument(
      """--targets""",
      default="""DeprecationWarning,UserWarning,FutureWarning""",
      type=list_str,
      help="""Comma-separated list of target warning(s) which we want to extract.""",
  )
  parser.add_argument(
      """--from_gh""",
      action="""store_true""",
      help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
  )

  _lowerCamelCase			:	Union[str, Any]							 =     parser.parse_args()

  _lowerCamelCase			:	List[str]							 =     args.from_gh
  if from_gh:
    # The artifacts have to be downloaded using `actions/download-artifact@v3`
    pass
  else:
    os.makedirs(args.output_dir, exist_ok=True)

    # get download links
    _lowerCamelCase			:	int							 =     get_artifacts_links(args.workflow_run_id, token=args.token)
    with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
      json.dump(artifacts, fp, ensure_ascii=False, indent=4)

    # download artifacts
    for idx, (name, url) in enumerate(artifacts.items()):
      print(name)
      print(url)
      print("""=""" * 80)
      download_artifact(name, url, args.output_dir, args.token)
      # Be gentle to GitHub
      time.sleep(1)

    # extract warnings from artifacts
  _lowerCamelCase			:	Optional[int]							 =     extract_warnings(args.output_dir, args.targets)
  _lowerCamelCase			:	int							 =     sorted(selected_warnings)
  with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
    json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)








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

import cva
import numpy as np

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







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

       A__				,							A__   				=					get_dataset(lowercase_				,       lowercase_		)
       for index in range(lowercase_		):
              A__   				=					random.sample(range(len(lowercase_		)		)				,       4		)
              A__				,							A__				,							A__   				=					update_image_and_anno(
                  lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       filter_scale=lowercase_				,       )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              A__   				=					random_chars(32		)
              A__   				=					path.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              A__   				=					f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""				,       lowercase_				,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""		)
              A__   				=					[]
              for anno in new_annos:
                     A__   				=					anno[3] - anno[1]
                     A__   				=					anno[4] - anno[2]
                     A__   				=					anno[1] + width / 2
                     A__   				=					anno[2] + height / 2
                     A__   				=					f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(lowercase_		)
              with open(f"""{file_root}.txt"""				,       '''w'''		) as outfile:
                     outfile.write('''\n'''.join(line for line in annos_list		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> tuple[list, list]:



       """simple docstring"""

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

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

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = 0.0				,       )     -> tuple[list, list, str]:



       """simple docstring"""

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

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



       """simple docstring"""

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


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


