

import copy

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


__UpperCAmelCase								=			logging.get_logger(__name__)

__UpperCAmelCase								=			{
    """SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json""",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										="deformable_detr"
  lowerCamelCase   :      Tuple										={
      "hidden_size": "d_model",
      "num_attention_heads": "encoder_attention_heads",
  }







  def __init__(      self			:   Any	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   int=None	,							lowerCAmelCase			:   int=3	,							lowerCAmelCase			:   List[str]=3_00	,							lowerCAmelCase			:   Union[str, Any]=10_24	,							lowerCAmelCase			:   Optional[Any]=6	,							lowerCAmelCase			:   int=10_24	,							lowerCAmelCase			:   str=8	,							lowerCAmelCase			:   List[Any]=6	,							lowerCAmelCase			:   Any=10_24	,							lowerCAmelCase			:   Union[str, Any]=8	,							lowerCAmelCase			:   List[str]=0.0	,							lowerCAmelCase			:   Union[str, Any]=True	,							lowerCAmelCase			:   str="relu"	,							lowerCAmelCase			:   Any=2_56	,							lowerCAmelCase			:   Optional[int]=0.1	,							lowerCAmelCase			:   Optional[int]=0.0	,							lowerCAmelCase			:   Union[str, Any]=0.0	,							lowerCAmelCase			:   str=0.02	,							lowerCAmelCase			:   Dict=1.0	,							lowerCAmelCase			:   Tuple=True	,							lowerCAmelCase			:   str=False	,							lowerCAmelCase			:   Optional[int]="sine"	,							lowerCAmelCase			:   List[Any]="resnet50"	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   str=False	,							lowerCAmelCase			:   List[str]=4	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Dict=False	,							lowerCAmelCase			:   Optional[Any]=3_00	,							lowerCAmelCase			:   int=False	,							lowerCAmelCase			:   List[Any]=1	,							lowerCAmelCase			:   Union[str, Any]=5	,							lowerCAmelCase			:   List[str]=2	,							lowerCAmelCase			:   Optional[Any]=1	,							lowerCAmelCase			:   List[str]=1	,							lowerCAmelCase			:   int=5	,							lowerCAmelCase			:   List[str]=2	,							lowerCAmelCase			:   int=0.1	,							lowerCAmelCase			:   Any=0.25	,							lowerCAmelCase			:   Union[str, Any]=False	,							**lowerCAmelCase			:   Optional[int]	,							)							->     Optional[Any]:






     """simple docstring"""







     if backbone_config is not None and use_timm_backbone:
        raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""   )

     if not use_timm_backbone:
        if backbone_config is None:
           logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""   )
           __lowerCAmelCase  :      List[Any]    =						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]   )
        elif isinstance(lowerCAmelCase	,							lowerCAmelCase   ):
           __lowerCAmelCase  :      str    =						backbone_config.get("""model_type"""   )
           __lowerCAmelCase  :      Tuple    =						CONFIG_MAPPING[backbone_model_type]
           __lowerCAmelCase  :      str    =						config_class.from_dict(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						use_timm_backbone
     __lowerCAmelCase  :      List[str]    =						backbone_config
     __lowerCAmelCase  :      Optional[Any]    =						num_channels
     __lowerCAmelCase  :      Any    =						num_queries
     __lowerCAmelCase  :      int    =						max_position_embeddings
     __lowerCAmelCase  :      Optional[int]    =						d_model
     __lowerCAmelCase  :      Dict    =						encoder_ffn_dim
     __lowerCAmelCase  :      List[str]    =						encoder_layers
     __lowerCAmelCase  :      Dict    =						encoder_attention_heads
     __lowerCAmelCase  :      List[str]    =						decoder_ffn_dim
     __lowerCAmelCase  :      Optional[int]    =						decoder_layers
     __lowerCAmelCase  :      Optional[int]    =						decoder_attention_heads
     __lowerCAmelCase  :      List[str]    =						dropout
     __lowerCAmelCase  :      Tuple    =						attention_dropout
     __lowerCAmelCase  :      Optional[Any]    =						activation_dropout
     __lowerCAmelCase  :      Union[str, Any]    =						activation_function
     __lowerCAmelCase  :      int    =						init_std
     __lowerCAmelCase  :      Dict    =						init_xavier_std
     __lowerCAmelCase  :      Union[str, Any]    =						encoder_layerdrop
     __lowerCAmelCase  :      str    =						auxiliary_loss
     __lowerCAmelCase  :      Tuple    =						position_embedding_type
     __lowerCAmelCase  :      List[Any]    =						backbone
     __lowerCAmelCase  :      Union[str, Any]    =						use_pretrained_backbone
     __lowerCAmelCase  :      Tuple    =						dilation
     # deformable attributes
     __lowerCAmelCase  :      List[Any]    =						num_feature_levels
     __lowerCAmelCase  :      Tuple    =						encoder_n_points
     __lowerCAmelCase  :      Any    =						decoder_n_points
     __lowerCAmelCase  :      List[Any]    =						two_stage
     __lowerCAmelCase  :      Union[str, Any]    =						two_stage_num_proposals
     __lowerCAmelCase  :      Optional[Any]    =						with_box_refine
     if two_stage is True and with_box_refine is False:
        raise ValueError("""If two_stage is True, with_box_refine must be True."""   )
     # Hungarian matcher
     __lowerCAmelCase  :      str    =						class_cost
     __lowerCAmelCase  :      Any    =						bbox_cost
     __lowerCAmelCase  :      Dict    =						giou_cost
     # Loss coefficients
     __lowerCAmelCase  :      List[str]    =						mask_loss_coefficient
     __lowerCAmelCase  :      List[str]    =						dice_loss_coefficient
     __lowerCAmelCase  :      List[Any]    =						bbox_loss_coefficient
     __lowerCAmelCase  :      Any    =						giou_loss_coefficient
     __lowerCAmelCase  :      int    =						eos_coefficient
     __lowerCAmelCase  :      Dict    =						focal_alpha
     __lowerCAmelCase  :      Optional[Any]    =						disable_custom_kernels
     super().__init__(is_encoder_decoder=lowerCAmelCase	,							**lowerCAmelCase   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     int:






     """simple docstring"""







     return self.encoder_attention_heads







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     int:






     """simple docstring"""







     return self.d_model







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						copy.deepcopy(self.__dict__   )
     if self.backbone_config is not None:
        __lowerCAmelCase  :      List[Any]    =						self.backbone_config.to_dict()
     __lowerCAmelCase  :      Tuple    =						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								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						List[str] ,       __A					:						str					)      ->     int:
   __lowerCAmelCase  :      str    =						set()
   __lowerCAmelCase  :      int    =						[]

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

   if from_gh:
      for filename in os.listdir(__A					):
         __lowerCAmelCase  :      Optional[Any]    =						os.path.join(__A ,       __A					)
         if not os.path.isdir(__A					):
            # read the file
            if filename != "warnings.txt":
               continue
            with open(__A					) as fp:
               parse_line(__A					)
   else:
      try:
         with zipfile.ZipFile(__A					) as z:
            for filename in z.namelist():
               if not os.path.isdir(__A					):
                  # read the file
                  if filename != "warnings.txt":
                     continue
                  with z.open(__A					) as fp:
                     parse_line(__A					)
      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_				(__A					:						Dict ,       __A					:						Union[str, Any]					)      ->     Dict:
   __lowerCAmelCase  :      Any    =						set()

   __lowerCAmelCase  :      Optional[int]    =						[os.path.join(__A ,       __A					) for p in os.listdir(__A					) if (p.endswith(""".zip"""					) or from_gh)]
   for p in paths:
      selected_warnings.update(extract_warnings_from_single_artifact(__A ,       __A					)					)

   return selected_warnings


if __name__ == "__main__":





   def      snake_case_				(__A					:						int					)      ->     Tuple:
      return values.split(""","""					)

   __UpperCAmelCase								=			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								=			parser.parse_args()

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


