


"""simple docstring"""







import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
a__							:				List[Any]          =	logging.get_logger(__name__)




def   UpperCAmelCase__			(lowerCAmelCase_  ):




					'''simple docstring'''


					print("Loading config file..."  )

					def flatten_yaml_as_dict(lowerCAmelCase_    ,		lowerCAmelCase_=""    ,		lowerCAmelCase_="."  ):
										__SCREAMING_SNAKE_CASE		  =							[]
										for k, v in d.items():
															__SCREAMING_SNAKE_CASE		  =							parent_key + sep + k if parent_key else k
															if isinstance(__lowerCAmelCase    ,		collections.abc.MutableMapping  ):
																				items.extend(flatten_yaml_as_dict(__lowerCAmelCase    ,		__lowerCAmelCase    ,		sep=__lowerCAmelCase  ).items()  )
															else:
																				items.append((new_key, v)  )
										return dict(__lowerCAmelCase  )

					__SCREAMING_SNAKE_CASE		  =							argparse.Namespace()
					with open(__lowerCAmelCase    ,		"r"  ) as yaml_file:
										try:
															__SCREAMING_SNAKE_CASE		  =							yaml.load(__lowerCAmelCase    ,		Loader=yaml.FullLoader  )

															__SCREAMING_SNAKE_CASE		  =							flatten_yaml_as_dict(__lowerCAmelCase  )
															for k, v in flat_cfg.items():
																				setattr(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase  )
										except yaml.YAMLError as exc:
															logger.error("Error while loading config file: {}. Error message: {}".format(__lowerCAmelCase    ,		str(__lowerCAmelCase  )  )  )
					return config




def   UpperCAmelCase__			(lowerCAmelCase_    ,		lowerCAmelCase_  ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		  =							MobileViTVaConfig()

					__SCREAMING_SNAKE_CASE		  =							False

					# dataset
					if task_name.startswith("imagenet1k_"  ):
										__SCREAMING_SNAKE_CASE		  =							1000
										if int(task_name.strip().split("_"  )[-1]  ) == 384:
															__SCREAMING_SNAKE_CASE		  =							384
										else:
															__SCREAMING_SNAKE_CASE		  =							256
										__SCREAMING_SNAKE_CASE		  =							"imagenet-1k-id2label.json"
					elif task_name.startswith("imagenet21k_to_1k_"  ):
										__SCREAMING_SNAKE_CASE		  =							2_1000
										if int(task_name.strip().split("_"  )[-1]  ) == 384:
															__SCREAMING_SNAKE_CASE		  =							384
										else:
															__SCREAMING_SNAKE_CASE		  =							256
										__SCREAMING_SNAKE_CASE		  =							"imagenet-22k-id2label.json"
					elif task_name.startswith("ade20k_"  ):
										__SCREAMING_SNAKE_CASE		  =							151
										__SCREAMING_SNAKE_CASE		  =							512
										__SCREAMING_SNAKE_CASE		  =							"ade20k-id2label.json"
										__SCREAMING_SNAKE_CASE		  =							True
					elif task_name.startswith("voc_"  ):
										__SCREAMING_SNAKE_CASE		  =							21
										__SCREAMING_SNAKE_CASE		  =							512
										__SCREAMING_SNAKE_CASE		  =							"pascal-voc-id2label.json"
										__SCREAMING_SNAKE_CASE		  =							True

					# orig_config
					__SCREAMING_SNAKE_CASE		  =							load_orig_config_file(__lowerCAmelCase  )
					assert getattr(__lowerCAmelCase    ,		"model.classification.name"    ,		-1  ) == "mobilevit_v2", "Invalid model"
					__SCREAMING_SNAKE_CASE		  =							getattr(__lowerCAmelCase    ,		"model.classification.mitv2.width_multiplier"    ,		1.0  )
					assert (
					    getattr(__lowerCAmelCase    ,		"model.classification.mitv2.attn_norm_layer"    ,		-1  ) == "layer_norm_2d"
					), "Norm layers other than layer_norm_2d is not supported"
					__SCREAMING_SNAKE_CASE		  =							getattr(__lowerCAmelCase    ,		"model.classification.activation.name"    ,		"swish"  )
					# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

					if is_segmentation_model:
										__SCREAMING_SNAKE_CASE		  =							getattr(__lowerCAmelCase    ,		"model.segmentation.output_stride"    ,		16  )
										if "_deeplabv3" in task_name:
															__SCREAMING_SNAKE_CASE		  =							getattr(__lowerCAmelCase    ,		"model.segmentation.deeplabv3.aspp_rates"    ,		[12, 24, 36]  )
															__SCREAMING_SNAKE_CASE		  =							getattr(__lowerCAmelCase    ,		"model.segmentation.deeplabv3.aspp_out_channels"    ,		512  )
															__SCREAMING_SNAKE_CASE		  =							getattr(__lowerCAmelCase    ,		"model.segmentation.deeplabv3.aspp_dropout"    ,		0.1  )

    # id2label
					__SCREAMING_SNAKE_CASE		  =							"huggingface/label-files"
					__SCREAMING_SNAKE_CASE		  =							json.load(open(hf_hub_download(__lowerCAmelCase    ,		__lowerCAmelCase    ,		repo_type="dataset"  )    ,		"r"  )  )
					__SCREAMING_SNAKE_CASE		  =							{int(__lowerCAmelCase  ): v for k, v in idalabel.items()}
					__SCREAMING_SNAKE_CASE		  =							idalabel
					__SCREAMING_SNAKE_CASE		  =							{v: k for k, v in idalabel.items()}

					return config




def   UpperCAmelCase__			(lowerCAmelCase_    ,		lowerCAmelCase_    ,		lowerCAmelCase_  ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		  =							dct.pop(__lowerCAmelCase  )
					__SCREAMING_SNAKE_CASE		  =							val




def   UpperCAmelCase__			(lowerCAmelCase_    ,		lowerCAmelCase_=False  ):




					'''simple docstring'''


					if base_model:
										__SCREAMING_SNAKE_CASE		  =							""
					else:
										__SCREAMING_SNAKE_CASE		  =							"mobilevitv2."

					__SCREAMING_SNAKE_CASE		  =							[]
					for k in state_dict.keys():
										if k[:8] == "encoder.":
															__SCREAMING_SNAKE_CASE		  =							k[8:]
										else:
															__SCREAMING_SNAKE_CASE		  =							k

										if ".block." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace(".block."    ,		"."  )
										if ".conv." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace(".conv."    ,		".convolution."  )
										if ".norm." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace(".norm."    ,		".normalization."  )

										if "conv_1." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace("conv_1."    ,		f"""{model_prefix}conv_stem."""  )
										for i in [1, 2]:
															if f"""layer_{i}.""" in k:
																				__SCREAMING_SNAKE_CASE		  =							k_new.replace(f"""layer_{i}."""    ,		f"""{model_prefix}encoder.layer.{i-1}.layer."""  )
										if ".exp_1x1." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace(".exp_1x1."    ,		".expand_1x1."  )
										if ".red_1x1." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace(".red_1x1."    ,		".reduce_1x1."  )

										for i in [3, 4, 5]:
															if f"""layer_{i}.0.""" in k:
																				__SCREAMING_SNAKE_CASE		  =							k_new.replace(f"""layer_{i}.0."""    ,		f"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""  )
															if f"""layer_{i}.1.local_rep.0.""" in k:
																				__SCREAMING_SNAKE_CASE		  =							k_new.replace(f"""layer_{i}.1.local_rep.0."""    ,		f"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""  )
															if f"""layer_{i}.1.local_rep.1.""" in k:
																				__SCREAMING_SNAKE_CASE		  =							k_new.replace(f"""layer_{i}.1.local_rep.1."""    ,		f"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""  )

										for i in [3, 4, 5]:
															if i == 3:
																				__SCREAMING_SNAKE_CASE		  =							[0, 1]
															elif i == 4:
																				__SCREAMING_SNAKE_CASE		  =							[0, 1, 2, 3]
															elif i == 5:
																				__SCREAMING_SNAKE_CASE		  =							[0, 1, 2]

															for j in j_in:
																				if f"""layer_{i}.1.global_rep.{j}.""" in k:
																									__SCREAMING_SNAKE_CASE		  =							k_new.replace(
																									    f"""layer_{i}.1.global_rep.{j}."""    ,		f"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""  )
															if f"""layer_{i}.1.global_rep.{j+1}.""" in k:
																				__SCREAMING_SNAKE_CASE		  =							k_new.replace(
																				    f"""layer_{i}.1.global_rep.{j+1}."""    ,		f"""{model_prefix}encoder.layer.{i-1}.layernorm."""  )

															if f"""layer_{i}.1.conv_proj.""" in k:
																				__SCREAMING_SNAKE_CASE		  =							k_new.replace(f"""layer_{i}.1.conv_proj."""    ,		f"""{model_prefix}encoder.layer.{i-1}.conv_projection."""  )

										if "pre_norm_attn.0." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace("pre_norm_attn.0."    ,		"layernorm_before."  )
										if "pre_norm_attn.1." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace("pre_norm_attn.1."    ,		"attention."  )
										if "pre_norm_ffn.0." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace("pre_norm_ffn.0."    ,		"layernorm_after."  )
										if "pre_norm_ffn.1." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace("pre_norm_ffn.1."    ,		"ffn.conv1."  )
										if "pre_norm_ffn.3." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace("pre_norm_ffn.3."    ,		"ffn.conv2."  )

										if "classifier.1." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace("classifier.1."    ,		"classifier."  )

										if "seg_head." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace("seg_head."    ,		"segmentation_head."  )
										if ".aspp_layer." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace(".aspp_layer."    ,		"."  )
										if ".aspp_pool." in k:
															__SCREAMING_SNAKE_CASE		  =							k_new.replace(".aspp_pool."    ,		"."  )

										rename_keys.append((k, k_new)  )
					return rename_keys




def   UpperCAmelCase__			(lowerCAmelCase_  ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		  =							[]
					for k in state_dict.keys():
										if k.startswith("seg_head.aux_head."  ):
															keys_to_ignore.append(__lowerCAmelCase  )
					for k in keys_to_ignore:
										state_dict.pop(__lowerCAmelCase    ,		__lowerCAmelCase  )




def   UpperCAmelCase__			():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		  =							"http://images.cocodataset.org/val2017/000000039769.jpg"
					# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
					__SCREAMING_SNAKE_CASE		  =							Image.open(requests.get(__lowerCAmelCase    ,		stream=__lowerCAmelCase  ).raw  )
					return im




@torch.no_grad()
def   UpperCAmelCase__			(lowerCAmelCase_    ,		lowerCAmelCase_    ,		lowerCAmelCase_    ,		lowerCAmelCase_  ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		  =							get_mobilevitva_config(__lowerCAmelCase    ,		__lowerCAmelCase  )

					# load original state_dict
					__SCREAMING_SNAKE_CASE		  =							torch.load(__lowerCAmelCase    ,		map_location="cpu"  )

					# load huggingface model
					if task_name.startswith("ade20k_"  ) or task_name.startswith("voc_"  ):
										__SCREAMING_SNAKE_CASE		  =							MobileViTVaForSemanticSegmentation(__lowerCAmelCase  ).eval()
										__SCREAMING_SNAKE_CASE		  =							False
					else:
										__SCREAMING_SNAKE_CASE		  =							MobileViTVaForImageClassification(__lowerCAmelCase  ).eval()
										__SCREAMING_SNAKE_CASE		  =							False

					# remove and rename some keys of load the original model
					__SCREAMING_SNAKE_CASE		  =							checkpoint
					remove_unused_keys(__lowerCAmelCase  )
					__SCREAMING_SNAKE_CASE		  =							create_rename_keys(__lowerCAmelCase    ,		base_model=__lowerCAmelCase  )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase  )

					# load modified state_dict
					model.load_state_dict(__lowerCAmelCase  )

					# Check outputs on an image, prepared by MobileViTImageProcessor
					__SCREAMING_SNAKE_CASE		  =							MobileViTImageProcessor(crop_size=config.image_size    ,		size=config.image_size + 32  )
					__SCREAMING_SNAKE_CASE		  =							image_processor(images=prepare_img()    ,		return_tensors="pt"  )
					__SCREAMING_SNAKE_CASE		  =							model(**__lowerCAmelCase  )

					# verify classification model
					if task_name.startswith("imagenet"  ):
										__SCREAMING_SNAKE_CASE		  =							outputs.logits
										__SCREAMING_SNAKE_CASE		  =							logits.argmax(-1  ).item()
										print("Predicted class:"    ,		model.config.idalabel[predicted_class_idx]  )
										if task_name.startswith("imagenet1k_256"  ) and config.width_multiplier == 1.0:
															# expected_logits for base variant
															__SCREAMING_SNAKE_CASE		  =							torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]  )
															assert torch.allclose(logits[0, :3]    ,		__lowerCAmelCase    ,		atol=1E-4  )

					Path(__lowerCAmelCase  ).mkdir(exist_ok=__lowerCAmelCase  )
					print(f"""Saving model {task_name} to {pytorch_dump_folder_path}"""  )
					model.save_pretrained(__lowerCAmelCase  )
					print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
					image_processor.save_pretrained(__lowerCAmelCase  )


if __name__ == "__main__":
		a__							:				Any          =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--task''',
		    default='''imagenet1k_256''',
		    type=str,
		    help=(
		        '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
		        '''\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '''
		    ),
		    choices=[
		        '''imagenet1k_256''',
		        '''imagenet1k_384''',
		        '''imagenet21k_to_1k_256''',
		        '''imagenet21k_to_1k_384''',
		        '''ade20k_deeplabv3''',
		        '''voc_deeplabv3''',
		    ],
		)

		parser.add_argument(
		    '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
		)
		parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
		)

		a__							:				Tuple          =	parser.parse_args()
		convert_mobilevitva_checkpoint(
		    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_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()

