"""simple docstring"""




import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_a   :	Dict 					=     'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
_a   :	str 					=     direct_transformers_import(PATH_TO_TRANSFORMERS)

_a   :	Any 					=     transformers.models.auto.configuration_auto.CONFIG_MAPPING

_a   :	Union[str, Any] 					=     {
    # used to compute the property `self.chunk_length`
    'EncodecConfig': ['overlap'],
    # used as `self.bert_model = BertModel(config, ...)`
    'DPRConfig': True,
    # not used in modeling files, but it's an important information
    'FSMTConfig': ['langs'],
    # used internally in the configuration class file
    'GPTNeoConfig': ['attention_types'],
    # used internally in the configuration class file
    'EsmConfig': ['is_folding_model'],
    # used during training (despite we don't have training script for these models yet)
    'Mask2FormerConfig': ['ignore_value'],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    'OneFormerConfig': ['ignore_value', 'norm'],
    # used during preprocessing and collation, see `collating_graphormer.py`
    'GraphormerConfig': ['spatial_pos_max'],
    # used internally in the configuration class file
    'T5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
    'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
    # used internally in the configuration class file
    'LongT5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    'SwitchTransformersConfig': ['feed_forward_proj'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'BioGptConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'GLPNConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'SegformerConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'CvtConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'PerceiverConfig': ['layer_norm_eps'],
    # used internally to calculate the feature size
    'InformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate `mlp_dim`
    'SamVisionConfig': ['mlp_ratio'],
    # For (head) training, but so far not implemented
    'ClapAudioConfig': ['num_classes'],
    # Not used, but providing useful information to users
    'SpeechT5HifiGanConfig': ['sampling_rate'],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        'CLIPSegConfig': True,
        'DeformableDetrConfig': True,
        'DetaConfig': True,
        'DinatConfig': True,
        'DonutSwinConfig': True,
        'EfficientFormerConfig': True,
        'FSMTConfig': True,
        'JukeboxConfig': True,
        'LayoutLMv2Config': True,
        'MaskFormerSwinConfig': True,
        'MT5Config': True,
        'NatConfig': True,
        'OneFormerConfig': True,
        'PerceiverConfig': True,
        'RagConfig': True,
        'SpeechT5Config': True,
        'SwinConfig': True,
        'Swin2SRConfig': True,
        'Swinv2Config': True,
        'SwitchTransformersConfig': True,
        'TableTransformerConfig': True,
        'TapasConfig': True,
        'TransfoXLConfig': True,
        'UniSpeechConfig': True,
        'UniSpeechSatConfig': True,
        'WavLMConfig': True,
        'WhisperConfig': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        'JukeboxPriorConfig': True,
        # TODO: @Younes (for `is_decoder`)
        'Pix2StructTextConfig': True,
    }
)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: str    ,_lowerCamelCase		: Optional[int]			) ->   int:
  _lowerCAmelCase		: Optional[int]						    =  False
  for attribute in attributes:
    for modeling_source in source_strings:
      # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
      if (
          f"config.{attribute}" in modeling_source
          or f"getattr(config, \"{attribute}\"" in modeling_source
          or f"getattr(self.config, \"{attribute}\"" in modeling_source
      ):
        _lowerCAmelCase		: List[Any]						    =  True
      # Deal with multi-line cases
      elif (
          re.search(
              rf"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""    ,_lowerCamelCase    ,)
          is not None
      ):
        _lowerCAmelCase		: List[str]						    =  True
      # `SequenceSummary` is called with `SequenceSummary(config)`
      elif attribute in [
          "summary_type",
          "summary_use_proj",
          "summary_activation",
          "summary_last_dropout",
          "summary_proj_to_labels",
          "summary_first_dropout",
      ]:
        if "SequenceSummary" in modeling_source:
          _lowerCAmelCase		: Optional[Any]						    =  True
      if attribute_used:
        break
    if attribute_used:
      break

    # common and important attributes, even if they do not always appear in the modeling files
  _lowerCAmelCase		: Tuple						    =  [
      """bos_index""",
      """eos_index""",
      """pad_index""",
      """unk_index""",
      """mask_index""",
      """image_size""",
      """use_cache""",
      """out_features""",
      """out_indices""",
  ]
  _lowerCAmelCase		: int						    =  ["""encoder_no_repeat_ngram_size"""]

  # Special cases to be allowed
  _lowerCAmelCase		: Union[str, Any]						    =  True
  if not attribute_used:
    _lowerCAmelCase		: int						    =  False
    for attribute in attributes:
      # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
      if attribute in ["is_encoder_decoder"] and default_value is True:
        _lowerCAmelCase		: Dict						    =  True
      elif attribute in ["tie_word_embeddings"] and default_value is False:
        _lowerCAmelCase		: Dict						    =  True

      # Allow cases without checking the default value in the configuration class
      elif attribute in attributes_to_allow + attributes_used_in_generation:
        _lowerCAmelCase		: Optional[int]						    =  True
      elif attribute.endswith("""_token_id"""			):
        _lowerCAmelCase		: List[str]						    =  True

      # configuration class specific cases
      if not case_allowed:
        _lowerCAmelCase		: List[Any]						    =  SPECIAL_CASES_TO_ALLOW.get(config_class.__name__    ,[]			)
        _lowerCAmelCase		: Union[str, Any]						    =  allowed_cases is True or attribute in allowed_cases

  return attribute_used or case_allowed





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int			) ->   Tuple:
  _lowerCAmelCase		: Dict						    =  dict(inspect.signature(config_class.__init__			).parameters			)
  _lowerCAmelCase		: List[str]						    =  [x for x in list(signature.keys()			) if x not in ["""self""", """kwargs"""]]
  _lowerCAmelCase		: Union[str, Any]						    =  [signature[param].default for param in parameter_names]

  # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
  # as one variant is used, the test should pass
  _lowerCAmelCase		: str						    =  {}
  if len(config_class.attribute_map			) > 0:
    _lowerCAmelCase		: int						    =  {v: k for k, v in config_class.attribute_map.items()}

  # Get the path to modeling source files
  _lowerCAmelCase		: int						    =  inspect.getsourcefile(_lowerCamelCase			)
  _lowerCAmelCase		: Dict						    =  os.path.dirname(_lowerCamelCase			)
  # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
  _lowerCAmelCase		: Union[str, Any]						    =  [os.path.join(_lowerCamelCase    ,_lowerCamelCase			) for fn in os.listdir(_lowerCamelCase			) if fn.startswith("""modeling_"""			)]

  # Get the source code strings
  _lowerCAmelCase		: int						    =  []
  for path in modeling_paths:
    if os.path.isfile(_lowerCamelCase			):
      with open(_lowerCamelCase			) as fp:
        modeling_sources.append(fp.read()			)

  _lowerCAmelCase		: Dict						    =  []
  for config_param, default_value in zip(_lowerCamelCase    ,_lowerCamelCase			):
    # `attributes` here is all the variant names for `config_param`
    _lowerCAmelCase		: Union[str, Any]						    =  [config_param]
    # some configuration classes have non-empty `attribute_map`, and both names could be used in the
    # corresponding modeling files. As long as one of them appears, it is fine.
    if config_param in reversed_attribute_map:
      attributes.append(reversed_attribute_map[config_param]			)

    if not check_attribute_being_used(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			):
      unused_attributes.append(attributes[0]			)

  return sorted(_lowerCamelCase			)





def 							SCREAMING_SNAKE_CASE  (    ) ->   List[str]:
  _lowerCAmelCase		: List[str]						    =  {}
  for _config_class in list(CONFIG_MAPPING.values()			):
    # Skip deprecated models
    if "models.deprecated" in _config_class.__module__:
      continue
    # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
    _lowerCAmelCase		: Optional[Any]						    =  [
        cls
        for name, cls in inspect.getmembers(
            inspect.getmodule(_config_class			)    ,lambda _lowerCamelCase			: inspect.isclass(_lowerCamelCase			)
            and issubclass(_lowerCamelCase    ,_lowerCamelCase			)
            and inspect.getmodule(_lowerCamelCase			) == inspect.getmodule(_config_class			)    ,)
    ]
    for config_class in config_classes_in_module:
      _lowerCAmelCase		: Union[str, Any]						    =  check_config_attributes_being_used(_lowerCamelCase			)
      if len(_lowerCamelCase			) > 0:
        _lowerCAmelCase		: List[Any]						    =  unused_attributes

  if len(_lowerCamelCase			) > 0:
    _lowerCAmelCase		: Tuple						    =  """The following configuration classes contain unused attributes in the corresponding modeling files:\n"""
    for name, attributes in configs_with_unused_attributes.items():
      error += f"{name}: {attributes}\n"

    raise ValueError(_lowerCamelCase			)


if __name__ == "__main__":
     check_config_attributes()





"""simple docstring"""




import argparse
import json
import subprocess





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Any			) ->   List[Any]:
  _lowerCAmelCase		: Tuple						    =  []

  _lowerCAmelCase		: Optional[int]						    =  (
      f"curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""
      """ https://api.github.com/repos/huggingface/transformers/actions/runners"""
  )
  _lowerCAmelCase		: List[str]						    =  subprocess.run(_lowerCamelCase    ,shell=_lowerCamelCase    ,stdout=subprocess.PIPE			)
  _lowerCAmelCase		: int						    =  output.stdout.decode("""utf-8"""			)
  _lowerCAmelCase		: Tuple						    =  json.loads(_lowerCamelCase			)

  _lowerCAmelCase		: int						    =  status["""runners"""]
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_lowerCamelCase			)

    # save the result so we can report them on Slack
  with open("""offline_runners.txt"""    ,"""w"""			) as fp:
    fp.write(json.dumps(_lowerCamelCase			)			)

  if len(_lowerCamelCase			) > 0:
    _lowerCAmelCase		: int						    =  """\n""".join([x["""name"""] for x in offline_runners]			)
    raise ValueError(f"The following runners are offline:\n{failed}"			)


if __name__ == "__main__":





     def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   Optional[int]:
       return values.split(""","""			)

     _a   :	int 					=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--target_runners',
         default=None,
         type=list_str,
         required=True,
         help='Comma-separated list of runners to check status.',
     )

     parser.add_argument(
         '--token', default=None, type=str, required=True, help='A token that has actions:read permission.'
     )
     _a   :	Tuple 					=     parser.parse_args()






     get_runner_status(args.target_runners, args.token)




