

from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)








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 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''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 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''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 							_lowerCamelCase   (			):
      '''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()





