


import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase    :						str     =							get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
@require_tokenizers
class 							_UpperCamelCase (						A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							List[Any]    = ReformerTokenizer
   a_				:							Optional[int]    = ReformerTokenizerFast
   a_				:							Optional[int]    = True
   a_				:							List[Any]    = False
   a_				:							Any    = True

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         super().setUp()

         __lowerCamelCase     :      str       						=						ReformerTokenizer(_lowerCamelCase       ,  keep_accents=_lowerCamelCase							)
         tokenizer.save_pretrained(self.tmpdirname							)

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






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						"""<s>"""
         __lowerCamelCase     :      Any       						=						1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase							)       ,  _lowerCamelCase							)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase							)       ,  _lowerCamelCase							)

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






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						list(self.get_tokenizer().get_vocab().keys()							)

         self.assertEqual(vocab_keys[0]       ,  """<unk>"""							)
         self.assertEqual(vocab_keys[1]       ,  """<s>"""							)
         self.assertEqual(vocab_keys[-1]       ,  """j"""							)
         self.assertEqual(len(_lowerCamelCase							)       ,  1_0_0_0							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         self.assertEqual(self.get_tokenizer().vocab_size       ,  1_0_0_0							)

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         if not self.test_rust_tokenizer:
               return

         __lowerCamelCase     :      Dict       						=						self.get_tokenizer()
         __lowerCamelCase     :      Optional[int]       						=						self.get_rust_tokenizer()

         __lowerCamelCase     :      Optional[Any]       						=						"""I was born in 92000, and this is falsé."""

         __lowerCamelCase     :      Union[str, Any]       						=						tokenizer.tokenize(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						rust_tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)

         __lowerCamelCase     :      str       						=						tokenizer.encode(_lowerCamelCase       ,  add_special_tokens=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						rust_tokenizer.encode(_lowerCamelCase       ,  add_special_tokens=_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)

         __lowerCamelCase     :      str       						=						self.get_rust_tokenizer()
         __lowerCamelCase     :      Dict       						=						tokenizer.encode(_lowerCamelCase							)
         __lowerCamelCase     :      Dict       						=						rust_tokenizer.encode(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : int=1_5							):






         '''simple docstring'''



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                     __lowerCamelCase     :      Union[str, Any]       						=						self.rust_tokenizer_class.from_pretrained(_lowerCamelCase       ,  **_lowerCamelCase							)

                     # Simple input
                     __lowerCamelCase     :      Any       						=						"""This is a simple input"""
                     __lowerCamelCase     :      Optional[Any]       						=						["""This is a simple input 1""", """This is a simple input 2"""]
                     __lowerCamelCase     :      Union[str, Any]       						=						("""This is a simple input""", """This is a pair""")
                     __lowerCamelCase     :      Optional[int]       						=						[
                         ("""This is a simple input 1""", """This is a simple input 2"""),
                         ("""This is a simple pair 1""", """This is a simple pair 2"""),
                     ]

                     # Simple input tests
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Simple input
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Simple input
                     self.assertRaises(
                         _lowerCamelCase       ,  tokenizer_r.batch_encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""       ,  )

                     # Pair input
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Pair input
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Pair input
                     self.assertRaises(
                         _lowerCamelCase       ,  tokenizer_r.batch_encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""       ,  )

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         pass

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						ReformerTokenizer(_lowerCamelCase       ,  keep_accents=_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						tokenizer.tokenize("""This is a test"""							)
         self.assertListEqual(_lowerCamelCase       ,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_lowerCamelCase							)       ,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]       ,  )

         __lowerCamelCase     :      List[Any]       						=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
         self.assertListEqual(
             _lowerCamelCase       ,  [
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """9""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """é""",
                 """.""",
             ]       ,  )
         __lowerCamelCase     :      Union[str, Any]       						=						tokenizer.convert_tokens_to_ids(_lowerCamelCase							)
         self.assertListEqual(
             _lowerCamelCase       ,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]       ,  )

         __lowerCamelCase     :      List[str]       						=						tokenizer.convert_ids_to_tokens(_lowerCamelCase							)
         self.assertListEqual(
             _lowerCamelCase       ,  [
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """<unk>""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """<unk>""",
                 """.""",
             ]       ,  )

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






         '''simple docstring'''



         return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""							)

   @slow
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						"""Hello World!"""
         __lowerCamelCase     :      Any       						=						[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

         self.assertListEqual(_lowerCamelCase       ,  self.big_tokenizer.encode(_lowerCamelCase							)							)

   @slow
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						(
             """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
             """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
         )
         __lowerCamelCase     :      List[str]       						=						[
             1_0_8,
             2_6_5,
             2_4,
             1_1_1,
             4,
             2_5_8,
             1_5_6,
             3_5,
             2_8,
             2_7_5,
             3,
             2_5_9,
             2_9_7,
             2_6_0,
             8_4,
             4,
             3_5,
             1_1_0,
             4_4,
             8,
             2_5_9,
             9_1,
             2_6_8,
             2_1,
             1_1,
             2_0_9,
             2_7_4,
             1_0_9,
             2_6_6,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             3_1_5,
             2_5_8,
             2_7_8,
             2_5_8,
             2_7_7,
             2_5_8,
             0,
             2_5_8,
             2_8_8,
             2_5_8,
             3_1_9,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             2_8_7,
             2_5_8,
             3_1_5,
             2_5_8,
             2_8_9,
             2_5_8,
             2_7_8,
             9_9,
             2_6_9,
             2_6_6,
             2_6_2,
             8,
             2_5_9,
             2_4_1,
             4,
             2_1_7,
             2_3_0,
             2_6_8,
             2_6_6,
             5_5,
             1_6_8,
             1_0_6,
             7_5,
             1_9_3,
             2_6_6,
             2_2_3,
             2_7,
             4_9,
             2_6,
             2_8_2,
             2_5,
             2_6_4,
             2_9_9,
             1_9,
             2_6,
             0,
             2_5_8,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             1_7_6,
             1_8_3,
             2_7_0,
             1_1,
             2_6_2,
             4_2,
             6_1,
             2_6_5,
         ]

         self.assertListEqual(_lowerCamelCase       ,  self.big_tokenizer.encode(_lowerCamelCase							)							)

   @require_torch
   @slow
   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         import torch

         from transformers import ReformerConfig, ReformerModel

         # Build sequence
         __lowerCamelCase     :      int       						=						list(self.big_tokenizer.get_vocab().keys()							)[:1_0]
         __lowerCamelCase     :      Union[str, Any]       						=						""" """.join(_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						self.big_tokenizer.encode_plus(_lowerCamelCase       ,  return_tensors="""pt"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						self.big_tokenizer.batch_encode_plus([sequence, sequence]       ,  return_tensors="""pt"""							)

         __lowerCamelCase     :      List[Any]       						=						ReformerConfig()
         # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
         __lowerCamelCase     :      List[str]       						=						encoded_sequence["""input_ids"""].shape
         __lowerCamelCase     :      Optional[Any]       						=						ReformerModel(_lowerCamelCase							)

         # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
               model(**_lowerCamelCase							)
               model(**_lowerCamelCase							)


   @slow
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						{"""input_ids""": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
         # fmt: on

         # This tokenizer does not know some characters like ")".
         # That is the reason why we use very simple texts here.
         # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
         __lowerCamelCase     :      int       						=						[
             """This is a very simple sentence.""",
             """The quick brown fox jumps over the lazy dog.""",
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCamelCase       ,  model_name="""google/reformer-crime-and-punishment"""       ,  revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""       ,  padding=_lowerCamelCase       ,  sequences=_lowerCamelCase       ,  )







import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
__UpperCamelCase    :						Any     =							logging.get_logger(__name__)

__UpperCamelCase    :						str     =							{
    '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.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    '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',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
__UpperCamelCase    :						Optional[int]     =							[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




      for attribute in key.split("""."""      ):
            __lowerCamelCase     :      Union[str, Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

      if weight_type is not None:
            __lowerCamelCase     :      Union[str, Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      ).shape
      else:
            __lowerCamelCase     :      Any       						=						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":
            __lowerCamelCase     :      List[str]       						=						value
      elif weight_type == "weight_g":
            __lowerCamelCase     :      Dict       						=						value
      elif weight_type == "weight_v":
            __lowerCamelCase     :      str       						=						value
      elif weight_type == "bias":
            __lowerCamelCase     :      Optional[Any]       						=						value
      else:
            __lowerCamelCase     :      str       						=						value

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						[]
      __lowerCamelCase     :      int       						=						fairseq_model.state_dict()

      __lowerCamelCase     :      List[str]       						=						hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            __lowerCamelCase     :      str       						=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	hf_model.config.feat_extract_norm == """group"""						,	)
                  __lowerCamelCase     :      Optional[int]       						=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
                              __lowerCamelCase     :      int       						=						True
                              if "*" in mapped_key:
                                    __lowerCamelCase     :      Optional[int]       						=						name.split(UpperCAmelCase      )[0].split("""."""      )[-2]
                                    __lowerCamelCase     :      Optional[int]       						=						mapped_key.replace("""*"""						,	UpperCAmelCase      )
                              if "weight_g" in name:
                                    __lowerCamelCase     :      Optional[Any]       						=						"""weight_g"""
                              elif "weight_v" in name:
                                    __lowerCamelCase     :      Dict       						=						"""weight_v"""
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    __lowerCamelCase     :      List[Any]       						=						"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    __lowerCamelCase     :      List[str]       						=						"""weight"""
                              else:
                                    __lowerCamelCase     :      List[str]       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						full_name.split("""conv_layers."""      )[-1]
      __lowerCamelCase     :      List[Any]       						=						name.split("""."""      )
      __lowerCamelCase     :      Tuple       						=						int(items[0]      )
      __lowerCamelCase     :      List[Any]       						=						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."""
                  )
                  __lowerCamelCase     :      List[str]       						=						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."""
                  )
                  __lowerCamelCase     :      str       						=						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."
                  )
                  __lowerCamelCase     :      Any       						=						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."""
                  )
                  __lowerCamelCase     :      Tuple       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]=None      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						torch.load(UpperCAmelCase      )
      __lowerCamelCase     :      Optional[int]       						=						WavLMConfigOrig(checkpoint["""cfg"""]      )
      __lowerCamelCase     :      Any       						=						WavLMOrig(UpperCAmelCase      )
      model.load_state_dict(checkpoint["""model"""]      )
      model.eval()

      if config_path is not None:
            __lowerCamelCase     :      Union[str, Any]       						=						WavLMConfig.from_pretrained(UpperCAmelCase      )
      else:
            __lowerCamelCase     :      str       						=						WavLMConfig()

      __lowerCamelCase     :      Dict       						=						WavLMModel(UpperCAmelCase      )

      recursively_load_weights(UpperCAmelCase						,	UpperCAmelCase      )

      hf_wavlm.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						List[str]     =							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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    __UpperCamelCase    :						Tuple     =							parser.parse_args()
    convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



