





import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase     :     Tuple							=	{
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

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







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : int            =      """mask2former"""
      _lowercase    : List[str]            =      ["""swin"""]
      _lowercase    : int            =      {"""hidden_size""": """hidden_dim"""}







      def __init__(   self		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 2_5_6		,				lowerCAmelCase__ = 2_5_6		,				lowerCAmelCase__ = 2_5_6		,				lowerCAmelCase__ = 1_0_2_4		,				lowerCAmelCase__ = "relu"		,				lowerCAmelCase__ = 6		,				lowerCAmelCase__ = 1_0		,				lowerCAmelCase__ = 8		,				lowerCAmelCase__ = 0.0		,				lowerCAmelCase__ = 2_0_4_8		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = 4		,				lowerCAmelCase__ = 2_5_5		,				lowerCAmelCase__ = 1_0_0		,				lowerCAmelCase__ = 0.1		,				lowerCAmelCase__ = 2.0		,				lowerCAmelCase__ = 5.0		,				lowerCAmelCase__ = 5.0		,				lowerCAmelCase__ = 1_2_5_4_4		,				lowerCAmelCase__ = 3.0		,				lowerCAmelCase__ = 0.75		,				lowerCAmelCase__ = 0.02		,				lowerCAmelCase__ = 1.0		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = [4, 8, 1_6, 3_2]		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						str:



        '''simple docstring'''




        if backbone_config is None:
          logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."							)
          a__     : Dict  =CONFIG_MAPPING["swin"](
              image_size=2_2_4		,				in_channels=3		,				patch_size=4		,				embed_dim=9_6		,				depths=[2, 2, 1_8, 2]		,				num_heads=[3, 6, 1_2, 2_4]		,				window_size=7		,				drop_path_rate=0.3		,				use_absolute_embeddings=lowerCAmelCase__		,				out_features=["stage1", "stage2", "stage3", "stage4"]		,				)

        if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          a__     : Tuple  =backbone_config.pop("model_type"							)
          a__     : Dict  =CONFIG_MAPPING[backbone_model_type]
          a__     : Dict  =config_class.from_dict(lowerCAmelCase__							)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
          logger.warning_once(
              F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
              F'''Supported model types: {",".join(self.backbones_supported							)}'''							)

        a__     : Dict  =backbone_config
        a__     : List[Any]  =feature_size
        a__     : Union[str, Any]  =mask_feature_size
        a__     : List[str]  =hidden_dim
        a__     : List[Any]  =encoder_feedforward_dim
        a__     : Optional[Any]  =activation_function
        a__     : str  =encoder_layers
        a__     : Dict  =decoder_layers
        a__     : Optional[int]  =num_attention_heads
        a__     : int  =dropout
        a__     : Optional[int]  =dim_feedforward
        a__     : str  =pre_norm
        a__     : str  =enforce_input_projection
        a__     : Any  =common_stride
        a__     : List[Any]  =ignore_value
        a__     : str  =num_queries
        a__     : Optional[Any]  =no_object_weight
        a__     : Union[str, Any]  =class_weight
        a__     : Tuple  =mask_weight
        a__     : Optional[Any]  =dice_weight
        a__     : Tuple  =train_num_points
        a__     : Tuple  =oversample_ratio
        a__     : List[str]  =importance_sample_ratio
        a__     : Union[str, Any]  =init_std
        a__     : Tuple  =init_xavier_std
        a__     : Dict  =use_auxiliary_loss
        a__     : Optional[int]  =feature_strides
        a__     : Optional[int]  =output_auxiliary_logits
        a__     : Dict  =decoder_layers

        super().__init__(**lowerCAmelCase__							)







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        return cls(
            backbone_config=lowerCAmelCase__		,				**lowerCAmelCase__		,				)







      def        _lowercase  (   self							)       ->						Dict[str, any]:



        '''simple docstring'''




        a__     : List[Any]  =copy.deepcopy(self.__dict__							)
        a__     : Optional[Any]  =self.backbone_config.to_dict()
        a__     : int  =self.__class__.model_type
        return output













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


UpperCAmelCase     :     Union[str, Any]							=	logging.get_logger(__name__)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  a__     : Tuple  =set()
  a__     : Optional[Any]  =[]

  def parse_line(SCREAMING_SNAKE_CASE					:       Optional[int]						):
    for line in fp:
      if isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
        a__     : str  =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(SCREAMING_SNAKE_CASE						) > 0:
          a__     : Union[str, Any]  ="\n".join(SCREAMING_SNAKE_CASE						)
          # Only keep the warnings specified in `targets`
          if any(f''': {x}: ''' in warning for x in targets						):
            selected_warnings.add(SCREAMING_SNAKE_CASE						)
          buffer.clear()
        continue
      else:
        a__     : Optional[Any]  =line.strip()
        buffer.append(SCREAMING_SNAKE_CASE						)

  if from_gh:
    for filename in os.listdir(SCREAMING_SNAKE_CASE						):
      a__     : str  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      if not os.path.isdir(SCREAMING_SNAKE_CASE						):
        # read the file
        if filename != "warnings.txt":
          continue
        with open(SCREAMING_SNAKE_CASE						) as fp:
          parse_line(SCREAMING_SNAKE_CASE						)
  else:
    try:
      with zipfile.ZipFile(SCREAMING_SNAKE_CASE						) as z:
        for filename in z.namelist():
          if not os.path.isdir(SCREAMING_SNAKE_CASE						):
            # read the file
            if filename != "warnings.txt":
              continue
            with z.open(SCREAMING_SNAKE_CASE						) as fp:
              parse_line(SCREAMING_SNAKE_CASE						)
    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 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  a__     : Optional[int]  =set()

  a__     : Any  =[os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) for p in os.listdir(SCREAMING_SNAKE_CASE						) if (p.endswith(".zip"						) or from_gh)]
  for p in paths:
    selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)						)

  return selected_warnings


if __name__ == "__main__":
     def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]						):





       """simple docstring"""



       return values.split(","						)

     UpperCAmelCase     :     Any							=	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.""",
     )

     UpperCAmelCase     :     List[Any]							=	parser.parse_args()

     UpperCAmelCase     :     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
          UpperCAmelCase     :     Dict							=	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
     UpperCAmelCase     :     Tuple							=	extract_warnings(args.output_dir, args.targets)
     UpperCAmelCase     :     Optional[Any]							=	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)






