



"""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__    :  Optional[int]							    =	'''src/transformers'''


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

lowercase__    :  Optional[int]							    =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase__    :  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 __lowercase			(							_a		,			_a		,			_a		,			_a    ):
    snake_case_ :     Any				=	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
            ):
                snake_case_ :     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}\""		,			_a		,			)
                is not None
            ):
                snake_case_ :     List[Any]				=	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:
                    snake_case_ :     Dict				=	True
            if attribute_used:
                break
        if attribute_used:
            break

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

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

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

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

    return attribute_used or case_allowed




def __lowercase			(							_a    ):
    snake_case_ :     int				=	dict(inspect.signature(config_class.__init__    ).parameters    )
    snake_case_ :     List[str]				=	[x for x in list(signature.keys()    ) if x not in ['''self''', '''kwargs''']]
    snake_case_ :     List[str]				=	[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
    snake_case_ :     Optional[Any]				=	{}
    if len(config_class.attribute_map    ) > 0:
        snake_case_ :     Dict				=	{v: k for k, v in config_class.attribute_map.items()}

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

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

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

    return sorted(_a    )




def __lowercase			(							):
    snake_case_ :     int				=	{}
    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.)
        snake_case_ :     Union[str, Any]				=	[
            cls
            for name, cls in inspect.getmembers(
                inspect.getmodule(_config_class    )		,			lambda _a    : inspect.isclass(_a    )
                and issubclass(_a		,			_a    )
                and inspect.getmodule(_a    ) == inspect.getmodule(_config_class    )		,			)
        ]
        for config_class in config_classes_in_module:
            snake_case_ :     Dict				=	check_config_attributes_being_used(_a    )
            if len(_a    ) > 0:
                snake_case_ :     int				=	unused_attributes

    if len(_a    ) > 0:
        snake_case_ :     Dict				=	'''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 . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





