






'''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
__UpperCamelCase      :       int      			=      """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__UpperCamelCase      :       Optional[Any]      			=      direct_transformers_import(PATH_TO_TRANSFORMERS)

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

__UpperCamelCase      :       Optional[int]      			=      {
    # 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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[Any],					SCREAMING_SNAKE_CASE__:	List[str],					SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	Optional[Any]    )  ->			Tuple:

   """simple docstring"""
   __a				     =    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
         ):
            __a				     =    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}\"""",					SCREAMING_SNAKE_CASE__,					)
             is not None
         ):
            __a				     =    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:
               __a				     =    True
         if attribute_used:
            break
      if attribute_used:
         break

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

   # Special cases to be allowed
   __a				     =    True
   if not attribute_used:
      __a				     =    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:
            __a				     =    True
         elif attribute in ["tie_word_embeddings"] and default_value is False:
            __a				     =    True

         # Allow cases without checking the default value in the configuration class
         elif attribute in attributes_to_allow + attributes_used_in_generation:
            __a				     =    True
         elif attribute.endswith('_token_id'    ):
            __a				     =    True

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

   return attribute_used or case_allowed



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    dict(inspect.signature(config_class.__init__    ).parameters    )
   __a				     =    [x for x in list(signature.keys()    ) if x not in ['self', 'kwargs']]
   __a				     =    [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
   __a				     =    {}
   if len(config_class.attribute_map    ) > 0:
      __a				     =    {v: k for k, v in config_class.attribute_map.items()}

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

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

   __a				     =    []
   for config_param, default_value in zip(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
      # `attributes` here is all the variant names for `config_param`
      __a				     =    [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(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
         unused_attributes.append(attributes[0]    )

   return sorted(SCREAMING_SNAKE_CASE__    )



def    __UpperCAmelCase	(				)  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    {}
   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.)
      __a				     =    [
          cls
          for name, cls in inspect.getmembers(
              inspect.getmodule(_config_class    ),					lambda SCREAMING_SNAKE_CASE__    : inspect.isclass(SCREAMING_SNAKE_CASE__    )
              and issubclass(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
              and inspect.getmodule(SCREAMING_SNAKE_CASE__    ) == inspect.getmodule(_config_class    ),					)
      ]
      for config_class in config_classes_in_module:
         __a				     =    check_config_attributes_being_used(SCREAMING_SNAKE_CASE__    )
         if len(SCREAMING_SNAKE_CASE__    ) > 0:
            __a				     =    unused_attributes

   if len(SCREAMING_SNAKE_CASE__    ) > 0:
      __a				     =    '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(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 check_config_attributes()







'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase      :       List[str]      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Tuple      			=      {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vit_msn"




      def __init__(  self ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=224 ,   lowerCamelCase=16 ,   lowerCamelCase=3 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    image_size
         __a				     =    patch_size
         __a				     =    num_channels
         __a				     =    qkv_bias