





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.distilbert.modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertModel,
    )







class __a			:





   def __init__(       self					:      str					,lowerCamelCase					:      List[Any]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							13
      __SCREAMING_SNAKE_CASE   				=							7
      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							False
      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							99
      __SCREAMING_SNAKE_CASE   				=							32
      __SCREAMING_SNAKE_CASE   				=							2
      __SCREAMING_SNAKE_CASE   				=							4
      __SCREAMING_SNAKE_CASE   				=							37
      __SCREAMING_SNAKE_CASE   				=							"""gelu"""
      __SCREAMING_SNAKE_CASE   				=							0.1
      __SCREAMING_SNAKE_CASE   				=							0.1
      __SCREAMING_SNAKE_CASE   				=							512
      __SCREAMING_SNAKE_CASE   				=							16
      __SCREAMING_SNAKE_CASE   				=							2
      __SCREAMING_SNAKE_CASE   				=							0.02
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							4
      __SCREAMING_SNAKE_CASE   				=							None





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_input_mask:
         __SCREAMING_SNAKE_CASE   				=							random_attention_mask([self.batch_size, self.seq_length]     )

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

      __SCREAMING_SNAKE_CASE   				=							DistilBertConfig(
          vocab_size=self.vocab_size					,dim=self.hidden_size					,n_layers=self.num_hidden_layers					,n_heads=self.num_attention_heads					,hidden_dim=self.intermediate_size					,hidden_act=self.hidden_act					,dropout=self.hidden_dropout_prob					,attention_dropout=self.attention_probs_dropout_prob					,max_position_embeddings=self.max_position_embeddings					,initializer_range=self.initializer_range					,)

      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TFDistilBertModel(config=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """attention_mask""": input_mask}

      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							[input_ids, input_mask]

      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )

      self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)     )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TFDistilBertForMaskedLM(config=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)     )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TFDistilBertForQuestionAnswering(config=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							{
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
      }
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      self.parent.assertEqual(result.start_logits.shape					,(self.batch_size, self.seq_length)     )
      self.parent.assertEqual(result.end_logits.shape					,(self.batch_size, self.seq_length)     )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      __SCREAMING_SNAKE_CASE   				=							TFDistilBertForSequenceClassification(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)     )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_choices
      __SCREAMING_SNAKE_CASE   				=							TFDistilBertForMultipleChoice(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							tf.tile(tf.expand_dims(lowerCamelCase					,1     )					,(1, self.num_choices, 1)     )
      __SCREAMING_SNAKE_CASE   				=							tf.tile(tf.expand_dims(lowerCamelCase					,1     )					,(1, self.num_choices, 1)     )
      __SCREAMING_SNAKE_CASE   				=							{
          """input_ids""": multiple_choice_inputs_ids,
          """attention_mask""": multiple_choice_input_mask,
      }
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_choices)     )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      __SCREAMING_SNAKE_CASE   				=							TFDistilBertForTokenClassification(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.num_labels)     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

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







@require_tf
class __a			( _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			int						   =						(
       (
           TFDistilBertModel,
           TFDistilBertForMaskedLM,
           TFDistilBertForQuestionAnswering,
           TFDistilBertForSequenceClassification,
           TFDistilBertForTokenClassification,
           TFDistilBertForMultipleChoice,
       )
       if is_tf_available()
       else None
   )
   __UpperCamelCase :			Any						   =						(
       {
           'feature-extraction': TFDistilBertModel,
           'fill-mask': TFDistilBertForMaskedLM,
           'question-answering': TFDistilBertForQuestionAnswering,
           'text-classification': TFDistilBertForSequenceClassification,
           'token-classification': TFDistilBertForTokenClassification,
           'zero-shot': TFDistilBertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCamelCase :			Optional[int]						   =						False
   __UpperCamelCase :			Union[str, Any]						   =						False





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TFDistilBertModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,dim=37     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

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





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

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

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





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

      '''simple docstring'''

      for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]     ):
         __SCREAMING_SNAKE_CASE   				=							TFDistilBertModel.from_pretrained(lowerCamelCase     )
         self.assertIsNotNone(lowerCamelCase     )







@require_tf
class __a			( unittest.TestCase      ):





   @slow
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TFDistilBertModel.from_pretrained("""distilbert-base-uncased"""     )
      __SCREAMING_SNAKE_CASE   				=							tf.constant([[0, 1, 2, 3, 4, 5]]     )
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )[0]

      __SCREAMING_SNAKE_CASE   				=							[1, 6, 768]
      self.assertEqual(output.shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							tf.constant(
          [
              [
                  [0.19_261_885, -0.13_732_955, 0.4_119_799],
                  [0.22_150_156, -0.07_422_661, 0.39_037_204],
                  [0.22_756_018, -0.0_896_414, 0.3_701_467],
              ]
          ]     )
      tf.debugging.assert_near(output[:, :3, :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
    )





