


'''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_   :      Tuple				    =      '''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_   :      str				    =      {
    # 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 		_A			(lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:str			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:List[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}"'			,  _a			,  )
                   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 		_A			(lowerCAmelCase__					:int						)				->					int:




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

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

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

     return sorted(_a						)






def 		_A			()				->					Tuple:




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

     if len(_a						) > 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(_a						)


if __name__ == "__main__":
     check_config_attributes()





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      int				    =      {
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      [
     "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXJapaneseForCausalLM",
     "GPTNeoXJapaneseLayer",
     "GPTNeoXJapaneseModel",
     "GPTNeoXJapanesePreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 a_   :      int				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

