





'''simple docstring'''




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_SCREAMING_SNAKE_CASE							     =			[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

_SCREAMING_SNAKE_CASE							     =			[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

_SCREAMING_SNAKE_CASE							     =			[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  _SCREAMING_SNAKE_CASE							     =			F"""down_blocks.{i}.resnets.{j}."""
  _SCREAMING_SNAKE_CASE							     =			F"""input_blocks.{3*i + j + 1}.0."""
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   _SCREAMING_SNAKE_CASE							     =			F"""down_blocks.{i}.attentions.{j}."""
   _SCREAMING_SNAKE_CASE							     =			F"""input_blocks.{3*i + j + 1}.1."""
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  _SCREAMING_SNAKE_CASE							     =			F"""up_blocks.{i}.resnets.{j}."""
  _SCREAMING_SNAKE_CASE							     =			F"""output_blocks.{3*i + j}.0."""
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   _SCREAMING_SNAKE_CASE							     =			F"""up_blocks.{i}.attentions.{j}."""
   _SCREAMING_SNAKE_CASE							     =			F"""output_blocks.{3*i + j}.1."""
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  _SCREAMING_SNAKE_CASE							     =			F"""down_blocks.{i}.downsamplers.0.conv."""
  _SCREAMING_SNAKE_CASE							     =			F"""input_blocks.{3*(i+1)}.0.op."""
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  _SCREAMING_SNAKE_CASE							     =			F"""up_blocks.{i}.upsamplers.0."""
  _SCREAMING_SNAKE_CASE							     =			F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_SCREAMING_SNAKE_CASE							     =			"mid_block.attentions.0."
_SCREAMING_SNAKE_CASE							     =			"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 _SCREAMING_SNAKE_CASE							     =			F"""mid_block.resnets.{j}."""
 _SCREAMING_SNAKE_CASE							     =			F"""middle_block.{2*j}."""
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple		)		->  Dict:
   # buyer beware: this is a *brittle* function,
   # and correct output requires that all of these pieces interact in
   # the exact order in which I have arranged them.
   snake_case 						=   {k: k for k in unet_state_dict.keys()}
   for sd_name, hf_name in unet_conversion_map:
      snake_case 						=   sd_name
   for k, v in mapping.items():
      if "resnets" in k:
         for sd_part, hf_part in unet_conversion_map_resnet:
            snake_case 						=   v.replace(__lowerCAmelCase      ,    __lowerCAmelCase		)
         snake_case 						=   v
   for k, v in mapping.items():
      for sd_part, hf_part in unet_conversion_map_layer:
         snake_case 						=   v.replace(__lowerCAmelCase      ,    __lowerCAmelCase		)
      snake_case 						=   v
   snake_case 						=   {v: unet_state_dict[k] for k, v in mapping.items()}
   return new_state_dict


# ================#
# VAE Conversion #
# ================#

_SCREAMING_SNAKE_CASE							     =			[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  _SCREAMING_SNAKE_CASE							     =			F"""encoder.down_blocks.{i}.resnets.{j}."""
  _SCREAMING_SNAKE_CASE							     =			F"""encoder.down.{i}.block.{j}."""
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  _SCREAMING_SNAKE_CASE							     =			F"""down_blocks.{i}.downsamplers.0."""
  _SCREAMING_SNAKE_CASE							     =			F"""down.{i}.downsample."""
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  _SCREAMING_SNAKE_CASE							     =			F"""up_blocks.{i}.upsamplers.0."""
  _SCREAMING_SNAKE_CASE							     =			F"""up.{3-i}.upsample."""
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  _SCREAMING_SNAKE_CASE							     =			F"""decoder.up_blocks.{i}.resnets.{j}."""
  _SCREAMING_SNAKE_CASE							     =			F"""decoder.up.{3-i}.block.{j}."""
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 _SCREAMING_SNAKE_CASE							     =			F"""mid_block.resnets.{i}."""
 _SCREAMING_SNAKE_CASE							     =			F"""mid.block_{i+1}."""
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_SCREAMING_SNAKE_CASE							     =			[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int		)		->  List[Any]:
   # convert HF linear weights to SD conv2d weights
   return w.reshape(*w.shape      ,    1      ,    1		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[int]		)		->  List[Any]:
   snake_case 						=   {k: k for k in vae_state_dict.keys()}
   for k, v in mapping.items():
      for sd_part, hf_part in vae_conversion_map:
         snake_case 						=   v.replace(__lowerCAmelCase      ,    __lowerCAmelCase		)
      snake_case 						=   v
   for k, v in mapping.items():
      if "attentions" in k:
         for sd_part, hf_part in vae_conversion_map_attn:
            snake_case 						=   v.replace(__lowerCAmelCase      ,    __lowerCAmelCase		)
         snake_case 						=   v
   snake_case 						=   {v: vae_state_dict[k] for k, v in mapping.items()}
   snake_case 						=   ["""q""", """k""", """v""", """proj_out"""]
   for k, v in new_state_dict.items():
      for weight_name in weights_to_convert:
         if F'''mid.attn_1.{weight_name}.weight''' in k:
            print(F'''Reshaping {k} for SD format'''		)
            snake_case 						=   reshape_weight_for_sd(__lowerCAmelCase		)
   return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_SCREAMING_SNAKE_CASE							     =			[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
_SCREAMING_SNAKE_CASE							     =			{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_SCREAMING_SNAKE_CASE							     =			re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_SCREAMING_SNAKE_CASE							     =			{"q": 0, "k": 1, "v": 2}





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]		)		->  Union[str, Any]:
   snake_case 						=   {}
   snake_case 						=   {}
   snake_case 						=   {}
   for k, v in text_enc_dict.items():
      if (
          k.endswith(""".self_attn.q_proj.weight"""		)
          or k.endswith(""".self_attn.k_proj.weight"""		)
          or k.endswith(""".self_attn.v_proj.weight"""		)
      ):
         snake_case 						=   k[: -len(""".q_proj.weight"""		)]
         snake_case 						=   k[-len("""q_proj.weight"""		)]
         if k_pre not in capture_qkv_weight:
            snake_case 						=   [None, None, None]
         snake_case 						=   v
         continue

      if (
          k.endswith(""".self_attn.q_proj.bias"""		)
          or k.endswith(""".self_attn.k_proj.bias"""		)
          or k.endswith(""".self_attn.v_proj.bias"""		)
      ):
         snake_case 						=   k[: -len(""".q_proj.bias"""		)]
         snake_case 						=   k[-len("""q_proj.bias"""		)]
         if k_pre not in capture_qkv_bias:
            snake_case 						=   [None, None, None]
         snake_case 						=   v
         continue

      snake_case 						=   textenc_pattern.sub(lambda __lowerCAmelCase		: protected[re.escape(m.group(0		)		)]      ,    __lowerCAmelCase		)
      snake_case 						=   v

   for k_pre, tensors in capture_qkv_weight.items():
      if None in tensors:
         raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""		)
      snake_case 						=   textenc_pattern.sub(lambda __lowerCAmelCase		: protected[re.escape(m.group(0		)		)]      ,    __lowerCAmelCase		)
      snake_case 						=   torch.cat(__lowerCAmelCase		)

   for k_pre, tensors in capture_qkv_bias.items():
      if None in tensors:
         raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""		)
      snake_case 						=   textenc_pattern.sub(lambda __lowerCAmelCase		: protected[re.escape(m.group(0		)		)]      ,    __lowerCAmelCase		)
      snake_case 						=   torch.cat(__lowerCAmelCase		)

   return new_state_dict





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[Any]		)		->  Optional[int]:
   return text_enc_dict


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()

 parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
 parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
 parser.add_argument(
     "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 _SCREAMING_SNAKE_CASE							     =			osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
 _SCREAMING_SNAKE_CASE							     =			osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
 _SCREAMING_SNAKE_CASE							     =			osp.join(args.model_path, "text_encoder", "model.safetensors")

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  _SCREAMING_SNAKE_CASE							     =			load_file(unet_path, device="cpu")
 else:
  _SCREAMING_SNAKE_CASE							     =			osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
  _SCREAMING_SNAKE_CASE							     =			torch.load(unet_path, map_location="cpu")

 if osp.exists(vae_path):
  _SCREAMING_SNAKE_CASE							     =			load_file(vae_path, device="cpu")
 else:
  _SCREAMING_SNAKE_CASE							     =			osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
  _SCREAMING_SNAKE_CASE							     =			torch.load(vae_path, map_location="cpu")

 if osp.exists(text_enc_path):
  _SCREAMING_SNAKE_CASE							     =			load_file(text_enc_path, device="cpu")
 else:
  _SCREAMING_SNAKE_CASE							     =			osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
  _SCREAMING_SNAKE_CASE							     =			torch.load(text_enc_path, map_location="cpu")

 # Convert the UNet model
 _SCREAMING_SNAKE_CASE							     =			convert_unet_state_dict(unet_state_dict)
 _SCREAMING_SNAKE_CASE							     =			{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 _SCREAMING_SNAKE_CASE							     =			convert_vae_state_dict(vae_state_dict)
 _SCREAMING_SNAKE_CASE							     =			{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 _SCREAMING_SNAKE_CASE							     =			"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  _SCREAMING_SNAKE_CASE							     =			{"transformer." + k: v for k, v in text_enc_dict.items()}
  _SCREAMING_SNAKE_CASE							     =			convert_text_enc_state_dict_vaa(text_enc_dict)
  _SCREAMING_SNAKE_CASE							     =			{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
 else:
  _SCREAMING_SNAKE_CASE							     =			convert_text_enc_state_dict(text_enc_dict)
  _SCREAMING_SNAKE_CASE							     =			{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 _SCREAMING_SNAKE_CASE							     =			{**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  _SCREAMING_SNAKE_CASE							     =			{k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  _SCREAMING_SNAKE_CASE							     =			{"state_dict": state_dict}
  torch.save(state_dict, args.checkpoint_path)








'''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
_SCREAMING_SNAKE_CASE							     =			"src/transformers"


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

_SCREAMING_SNAKE_CASE							     =			transformers.models.auto.configuration_auto.CONFIG_MAPPING

_SCREAMING_SNAKE_CASE							     =			{
    # 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			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Union[str, Any]		)		->  List[Any]:
   snake_case 						=   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 						=   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}"'''      ,    __lowerCAmelCase      ,    )
             is not None
         ):
            snake_case 						=   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 						=   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 						=   [
       """bos_index""",
       """eos_index""",
       """pad_index""",
       """unk_index""",
       """mask_index""",
       """image_size""",
       """use_cache""",
       """out_features""",
       """out_indices""",
   ]
   snake_case 						=   ["""encoder_no_repeat_ngram_size"""]

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

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

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

   return attribute_used or case_allowed





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]		)		->  List[Any]:
   snake_case 						=   dict(inspect.signature(config_class.__init__		).parameters		)
   snake_case 						=   [x for x in list(signature.keys()		) if x not in ["""self""", """kwargs"""]]
   snake_case 						=   [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 						=   {}
   if len(config_class.attribute_map		) > 0:
      snake_case 						=   {v: k for k, v in config_class.attribute_map.items()}

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

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

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

   return sorted(__lowerCAmelCase		)





def 	__lowerCamelCase			(      )		->  Optional[Any]:
   snake_case 						=   {}
   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 						=   [
          cls
          for name, cls in inspect.getmembers(
              inspect.getmodule(_config_class		)      ,    lambda __lowerCAmelCase		: inspect.isclass(__lowerCAmelCase		)
              and issubclass(__lowerCAmelCase      ,    __lowerCAmelCase		)
              and inspect.getmodule(__lowerCAmelCase		) == inspect.getmodule(_config_class		)      ,    )
      ]
      for config_class in config_classes_in_module:
         snake_case 						=   check_config_attributes_being_used(__lowerCAmelCase		)
         if len(__lowerCAmelCase		) > 0:
            snake_case 						=   unused_attributes

   if len(__lowerCAmelCase		) > 0:
      snake_case 						=   """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(__lowerCAmelCase		)


if __name__ == "__main__":
 check_config_attributes()

