





'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import RegNetForImageClassification, RegNetModel
    from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class __a			:





   def __init__(       self					:      Any					,lowerCamelCase					:      Any					,lowerCamelCase					:      int=3					,lowerCamelCase					:      Optional[int]=32					,lowerCamelCase					:      Any=3					,lowerCamelCase					:      Dict=10					,lowerCamelCase					:      Union[str, Any]=[10, 20, 30, 40]					,lowerCamelCase					:      Any=[1, 1, 2, 1]					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Optional[Any]=True					,lowerCamelCase					:      Dict="relu"					,lowerCamelCase					:      str=3					,lowerCamelCase					:      Union[str, Any]=None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							image_size
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							embeddings_size
      __SCREAMING_SNAKE_CASE   				=							hidden_sizes
      __SCREAMING_SNAKE_CASE   				=							depths
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							num_labels
      __SCREAMING_SNAKE_CASE   				=							scope
      __SCREAMING_SNAKE_CASE   				=							len(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.num_labels     )

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      return config, pixel_values, labels





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return RegNetConfig(
          num_channels=self.num_channels					,embeddings_size=self.embeddings_size					,hidden_sizes=self.hidden_sizes					,depths=self.depths					,hidden_act=self.hidden_act					,num_labels=self.num_labels					,)





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      int					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape					,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,)





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      __SCREAMING_SNAKE_CASE   				=							RegNetForImageClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							config_and_inputs
      __SCREAMING_SNAKE_CASE   				=							{"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class __a			( _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			List[str]						   =						(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
   __UpperCamelCase :			Optional[int]						   =						(
       {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
       if is_torch_available()
       else {}
   )

   __UpperCamelCase :			Dict						   =						False
   __UpperCamelCase :			str						   =						False
   __UpperCamelCase :			Any						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,has_text_modality=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return





   @unittest.skip(reason="""RegNet does not use inputs_embeds"""     )
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      pass





   @unittest.skip(reason="""RegNet does not support input and output embeddings"""     )
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							inspect.signature(model.forward     )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __SCREAMING_SNAKE_CASE   				=							[*signature.parameters.keys()]

         __SCREAMING_SNAKE_CASE   				=							["""pixel_values"""]
         self.assertListEqual(arg_names[:1]					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class(config=lowerCamelCase     )
         for name, module in model.named_modules():
            if isinstance(lowerCamelCase					,(nn.BatchNormad, nn.GroupNorm)     ):
               self.assertTrue(
                   torch.all(module.weight == 1     )					,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""					,)
               self.assertTrue(
                   torch.all(module.bias == 0     )					,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""					,)





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      def check_hidden_states_output(lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[int]     ):
         __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
         model.to(lowerCamelCase     )
         model.eval()

         with torch.no_grad():
            __SCREAMING_SNAKE_CASE   				=							model(**self._prepare_for_class(lowerCamelCase					,lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         __SCREAMING_SNAKE_CASE   				=							self.model_tester.num_stages
         self.assertEqual(len(lowerCamelCase     )					,expected_num_stages + 1     )

         # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]     )					,[self.model_tester.image_size // 2, self.model_tester.image_size // 2]					,)

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							["""basic""", """bottleneck"""]
      for model_class in self.all_model_classes:
         for layer_type in layers_type:
            __SCREAMING_SNAKE_CASE   				=							layer_type
            __SCREAMING_SNAKE_CASE   				=							True
            check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __SCREAMING_SNAKE_CASE   				=							True

            check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase     )





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE   				=							RegNetModel.from_pretrained(lowerCamelCase     )
         self.assertIsNotNone(lowerCamelCase     )





def     __magic_name__							(				)    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image







@require_torch
@require_vision
class __a			( unittest.TestCase      ):





   @cached_property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return (
          AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
          if is_vision_available()
          else None
      )





   @slow
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.default_image_processor
      __SCREAMING_SNAKE_CASE   				=							prepare_img()
      __SCREAMING_SNAKE_CASE   				=							image_processor(images=lowerCamelCase					,return_tensors="""pt"""     ).to(lowerCamelCase     )

      # forward pass
      with torch.no_grad():
         __SCREAMING_SNAKE_CASE   				=							model(**lowerCamelCase     )

      # verify the logits
      __SCREAMING_SNAKE_CASE   				=							torch.Size((1, 1000)     )
      self.assertEqual(outputs.logits.shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-0.4_180, -1.5_051, -3.4_836]     ).to(lowerCamelCase     )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]					,lowerCamelCase					,atol=1E-4     )     )












'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a   	=    logging.get_logger(__name__)

a   	=    {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   for attribute in key.split("""."""   ):
      __SCREAMING_SNAKE_CASE   				=							getattr(__UpperCAmelCase				,						__UpperCAmelCase   )

   if weight_type is not None:
      __SCREAMING_SNAKE_CASE   				=							getattr(__UpperCAmelCase				,						__UpperCAmelCase   ).shape
   else:
      __SCREAMING_SNAKE_CASE   				=							hf_pointer.shape

   assert hf_shape == value.shape, (
       f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
       f""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      __SCREAMING_SNAKE_CASE   				=							value
   elif weight_type == "weight_g":
      __SCREAMING_SNAKE_CASE   				=							value
   elif weight_type == "weight_v":
      __SCREAMING_SNAKE_CASE   				=							value
   elif weight_type == "bias":
      __SCREAMING_SNAKE_CASE   				=							value
   else:
      __SCREAMING_SNAKE_CASE   				=							value

   logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							fairseq_model.state_dict()

   __SCREAMING_SNAKE_CASE   				=							hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __SCREAMING_SNAKE_CASE   				=							False
      if "conv_layers" in name:
         load_conv_layer(
             __UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						hf_model.config.feat_extract_norm == """group"""				,						)
         __SCREAMING_SNAKE_CASE   				=							True
      else:
         for key, mapped_key in MAPPING.items():
            __SCREAMING_SNAKE_CASE   				=							"""hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

            if key in name or (key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0] and not is_finetuned):
               __SCREAMING_SNAKE_CASE   				=							True
               if "*" in mapped_key:
                  __SCREAMING_SNAKE_CASE   				=							name.split(__UpperCAmelCase   )[0].split("""."""   )[-2]
                  __SCREAMING_SNAKE_CASE   				=							mapped_key.replace("""*"""				,						__UpperCAmelCase   )
               if "weight_g" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_g"""
               elif "weight_v" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_v"""
               elif "weight" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight"""
               elif "bias" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""bias"""
               else:
                  __SCREAMING_SNAKE_CASE   				=							None
               set_recursively(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
            continue
      if not is_used:
         unused_weights.append(__UpperCAmelCase   )

   logger.warning(f"""Unused weights: {unused_weights}"""   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							full_name.split("""conv_layers."""   )[-1]
   __SCREAMING_SNAKE_CASE   				=							name.split("""."""   )
   __SCREAMING_SNAKE_CASE   				=							int(items[0]   )
   __SCREAMING_SNAKE_CASE   				=							int(items[1]   )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(__UpperCAmelCase   )

@torch.no_grad()
def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=True   )    ->       Tuple:

   '''simple docstring'''







   if config_path is not None:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig.from_pretrained(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig()

   if is_finetuned:
      if dict_path:
         __SCREAMING_SNAKE_CASE   				=							Dictionary.load(__UpperCAmelCase   )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __SCREAMING_SNAKE_CASE   				=							target_dict.pad_index
         __SCREAMING_SNAKE_CASE   				=							target_dict.bos_index
         __SCREAMING_SNAKE_CASE   				=							target_dict.eos_index
         __SCREAMING_SNAKE_CASE   				=							len(target_dict.symbols   )
         __SCREAMING_SNAKE_CASE   				=							os.path.join(__UpperCAmelCase				,						"""vocab.json"""   )
         if not os.path.isdir(__UpperCAmelCase   ):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__UpperCAmelCase   )   )
            return
         os.makedirs(__UpperCAmelCase				,						exist_ok=__UpperCAmelCase   )
         with open(__UpperCAmelCase				,						"""w"""				,						encoding="""utf-8"""   ) as vocab_handle:
            json.dump(target_dict.indices				,						__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE   				=							WavaVecaCTCTokenizer(
             __UpperCAmelCase				,						unk_token=target_dict.unk_word				,						pad_token=target_dict.pad_word				,						bos_token=target_dict.bos_word				,						eos_token=target_dict.eos_word				,						word_delimiter_token="""|"""				,						do_lower_case=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							True if config.feat_extract_norm == """layer""" else False
         __SCREAMING_SNAKE_CASE   				=							WavaVecaFeatureExtractor(
             feature_size=1				,						sampling_rate=16000				,						padding_value=0				,						do_normalize=__UpperCAmelCase				,						return_attention_mask=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							WavaVecaProcessor(feature_extractor=__UpperCAmelCase				,						tokenizer=__UpperCAmelCase   )
         processor.save_pretrained(__UpperCAmelCase   )

      __SCREAMING_SNAKE_CASE   				=							HubertForCTC(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertModel(__UpperCAmelCase   )

   if is_finetuned:
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]				,						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
   else:
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

   __SCREAMING_SNAKE_CASE   				=							model[0].eval()

   recursively_load_weights(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )

   hf_wavavec.save_pretrained(__UpperCAmelCase   )


if __name__ == "__main__":
    a   	=    argparse.ArgumentParser()
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
    parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument(
        "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    a   	=    parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





