
"""simple docstring"""


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_lowercase							      = {
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_lowercase							      = logging.get_logger(__name__)







class  lowerCAmelCase_ (					__snake_case			):



						'''simple docstring'''


						_lowerCamelCase:			Any					=			"""maskformer"""
						_lowerCamelCase:			Tuple					=			{"""hidden_size""": """mask_feature_size"""}
						_lowerCamelCase:			Union[str, Any]					=			["""resnet""", """swin"""]
						_lowerCamelCase:			List[Any]					=			["""detr"""]


						def __init__(       self   :					List[Any]     ,A_   :					List[str] = 256     ,A_   :					Optional[int] = 256     ,A_   :					Optional[Any] = 0.1     ,A_   :					str = False     ,A_   :					Union[str, Any] = None     ,A_   :					int = None     ,A_   :					List[str] = 0.02     ,A_   :					List[str] = 1.0     ,A_   :					Dict = 1.0     ,A_   :					Optional[int] = 1.0     ,A_   :					Union[str, Any] = 20.0     ,A_   :					str = None     ,**A_   :					Dict     ,)   ->      str:
							if backbone_config is None:
								# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
								A        =      SwinConfig(
								    image_size=384     ,in_channels=3     ,patch_size=4     ,embed_dim=128     ,depths=[2, 2, 18, 2]     ,num_heads=[4, 8, 16, 32]     ,window_size=12     ,drop_path_rate=0.3     ,out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,)

							if isinstance(A_     ,A_					):
								A        =      backbone_config.pop('model_type'					)
								A        =      CONFIG_MAPPING[backbone_model_type]
								A        =      config_class.from_dict(A_					)

							# 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 MaskFormer. '
								    F'Supported model types: {",".join(self.backbones_supported					)}'					)

							if decoder_config is None:
								# fall back to https://huggingface.co/facebook/detr-resnet-50
								A        =      DetrConfig()
							else:
								# verify that the decoder is supported
								A        =      (
								    decoder_config.pop('model_type'					) if isinstance(A_     ,A_					) else decoder_config.model_type
								)
								if decoder_type not in self.decoders_supported:
									raise ValueError(
									    F'Transformer Decoder {decoder_type} not supported, please use one of'
									    F' {",".join(self.decoders_supported					)}'					)
								if isinstance(A_     ,A_					):
									A        =      CONFIG_MAPPING[decoder_type]
									A        =      config_class.from_dict(A_					)

							A        =      backbone_config
							A        =      decoder_config
							# main feature dimension for the model
							A        =      fpn_feature_size
							A        =      mask_feature_size
							# initializer
							A        =      init_std
							A        =      init_xavier_std
							# Hungarian matcher && loss
							A        =      cross_entropy_weight
							A        =      dice_weight
							A        =      mask_weight
							A        =      use_auxiliary_loss
							A        =      no_object_weight
							A        =      output_auxiliary_logits

							A        =      self.decoder_config.encoder_attention_heads
							A        =      self.decoder_config.num_hidden_layers
							super().__init__(**A_					)


						@classmethod
						def   _SCREAMING_SNAKE_CASE					(       cls   :					List[Any]     ,A_   :					Dict     ,A_   :					str     ,**A_   :					Optional[Any]					)   ->      str:
							return cls(
							    backbone_config=A_     ,decoder_config=A_     ,**A_     ,)




						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Any:
							A        =      copy.deepcopy(self.__dict__					)
							A        =      self.backbone_config.to_dict()
							A        =      self.decoder_config.to_dict()
							A        =      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


snake_case_			  = logging.get_logger(__name__)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       set()
     lowercase__    :   List[str]      =       []

     def parse_line(SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):
          for line in fp:
               if isinstance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[int]      =       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:
                         lowercase__    :   Optional[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:
                    lowercase__    :   Optional[Any]      =       line.strip()
                    buffer.append(SCREAMING_SNAKE_CASE_			)

     if from_gh:
          for filename in os.listdir(SCREAMING_SNAKE_CASE_			):
               lowercase__    :   int      =       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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       set()

     lowercase__    :   List[str]      =       [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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any			):



            '''simple docstring'''





            return values.split(','			)

       snake_case_			  = 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.''',
       )

       snake_case_			  = parser.parse_args()

       snake_case_			  = 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
              snake_case_			  = 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
       snake_case_			  = extract_warnings(args.output_dir, args.targets)
       snake_case_			  = 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)

