



'''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
lowercase							       =			'''src/transformers'''


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

lowercase							       =			transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase							       =			{
    # 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_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''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}\""""       ,	lowercase__       ,	)
                   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_       (		lowercase__       ):


     '''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(lowercase__       )
     a_       		=os.path.dirname(lowercase__       )
     # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
     a_       		=[os.path.join(lowercase__       ,	lowercase__       ) for fn in os.listdir(lowercase__       ) if fn.startswith("modeling_"       )]

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

     a_       		=[]
     for config_param, default_value in zip(lowercase__       ,	lowercase__       ):
          # `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(lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):
               unused_attributes.append(attributes[0]       )

     return sorted(lowercase__       )




def  UpperCAmelCase_       (		):


     '''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 lowercase__       : inspect.isclass(lowercase__       )
                  and issubclass(lowercase__       ,	lowercase__       )
                  and inspect.getmodule(lowercase__       ) == inspect.getmodule(_config_class       )       ,	)
          ]
          for config_class in config_classes_in_module:
               a_       		=check_config_attributes_being_used(lowercase__       )
               if len(lowercase__       ) > 0:
                    a_       		=unused_attributes

     if len(lowercase__       ) > 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(lowercase__       )


if __name__ == "__main__":
  check_config_attributes()







'''simple docstring'''


import os
from math import logaa






def  UpperCAmelCase_       (		lowercase__ = "base_exp.txt"       ):


     '''simple docstring'''







     a_       		=0
     a_       		=0
     for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase__       )       ,	lowercase__       )       )       ):
          a_		,						a_       		=list(map(lowercase__       ,	line.split(","       )       )       )
          if x * logaa(lowercase__       ) > largest:
               a_       		=x * logaa(lowercase__       )
               a_       		=i + 1
     return result


if __name__ == "__main__":
  print(solution())


