'''simple docstring'''




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()
__SCREAMING_SNAKE_CASE       :     Any     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """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""",
}
__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      Optional[Any]			,	_UpperCAmelCase  :      List[str]			,	_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      List[Any]    )    ->    Optional[Any]:


       """simple docstring"""







       for attribute in key.split("."    ):
              _UpperCAmelCase     :  List[Any]							      =    getattr(_UpperCAmelCase			,	_UpperCAmelCase    )

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

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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Dict    )    ->    Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    []
       _UpperCAmelCase     :  Optional[Any]							      =    fairseq_model.state_dict()

       _UpperCAmelCase     :  Dict							      =    hf_model.feature_extractor

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


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    full_name.split("conv_layers."    )[-1]
       _UpperCAmelCase     :  str							      =    name.split("."    )
       _UpperCAmelCase     :  Any							      =    int(items[0]    )
       _UpperCAmelCase     :  Optional[int]							      =    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."""
                     )
                     _UpperCAmelCase     :  Optional[int]							      =    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."""
                     )
                     _UpperCAmelCase     :  Optional[Any]							      =    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."
                     )
                     _UpperCAmelCase     :  List[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."""
                     )
                     _UpperCAmelCase     :  List[str]							      =    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[Any]			,	_UpperCAmelCase  :      Optional[Any]			,	_UpperCAmelCase  :      List[str]=None    )    ->    Any:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    torch.load(_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    WavLMConfigOrig(checkpoint["cfg"]    )
       _UpperCAmelCase     :  Tuple							      =    WavLMOrig(_UpperCAmelCase    )
       model.load_state_dict(checkpoint["model"]    )
       model.eval()

       if config_path is not None:
              _UpperCAmelCase     :  List[str]							      =    WavLMConfig.from_pretrained(_UpperCAmelCase    )
       else:
              _UpperCAmelCase     :  List[Any]							      =    WavLMConfig()

       _UpperCAmelCase     :  Optional[Any]							      =    WavLMModel(_UpperCAmelCase    )

       recursively_load_weights(_UpperCAmelCase			,	_UpperCAmelCase    )

       hf_wavlm.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     int     =  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""")
  __SCREAMING_SNAKE_CASE       :     Tuple     =  parser.parse_args()
  convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching






def lowerCamelCase__    (							)      ->				Any:
   import os as original_os
   from os import path as original_path
   from os import rename as original_rename
   from os.path import dirname as original_dirname
   from os.path import join as original_join

   assert _test_patching.os is original_os
   assert _test_patching.path is original_path
   assert _test_patching.join is original_join

   assert _test_patching.renamed_os is original_os
   assert _test_patching.renamed_path is original_path
   assert _test_patching.renamed_join is original_join

   __snake_case							       =   '''__test_patch_submodule_mock__'''
   with patch_submodule(_test_patching       ,      '''os.path.join'''       ,      snake_case_       ):
      # Every way to access os.path.join must be patched, and the rest must stay untouched

      # check os.path.join
      assert isinstance(_test_patching.os       ,      _PatchedModuleObj       )
      assert isinstance(_test_patching.os.path       ,      _PatchedModuleObj       )
      assert _test_patching.os.path.join is mock

      # check path.join
      assert isinstance(_test_patching.path       ,      _PatchedModuleObj       )
      assert _test_patching.path.join is mock

      # check join
      assert _test_patching.join is mock

      # check that the other attributes are untouched
      assert _test_patching.os.rename is original_rename
      assert _test_patching.path.dirname is original_dirname
      assert _test_patching.os.path.dirname is original_dirname

      # Even renamed modules or objects must be patched

      # check renamed_os.path.join
      assert isinstance(_test_patching.renamed_os       ,      _PatchedModuleObj       )
      assert isinstance(_test_patching.renamed_os.path       ,      _PatchedModuleObj       )
      assert _test_patching.renamed_os.path.join is mock

      # check renamed_path.join
      assert isinstance(_test_patching.renamed_path       ,      _PatchedModuleObj       )
      assert _test_patching.renamed_path.join is mock

      # check renamed_join
      assert _test_patching.renamed_join is mock

      # check that the other attributes are untouched
      assert _test_patching.renamed_os.rename is original_rename
      assert _test_patching.renamed_path.dirname is original_dirname
      assert _test_patching.renamed_os.path.dirname is original_dirname

   # check that everthing is back to normal when the patch is over

   assert _test_patching.os is original_os
   assert _test_patching.path is original_path
   assert _test_patching.join is original_join

   assert _test_patching.renamed_os is original_os
   assert _test_patching.renamed_path is original_path
   assert _test_patching.renamed_join is original_join






def lowerCamelCase__    (							)      ->				Any:
   assert _test_patching.open is open

   __snake_case							       =   '''__test_patch_submodule_builtin_mock__'''
   # _test_patching has "open" in its globals
   assert _test_patching.open is open
   with patch_submodule(_test_patching       ,      '''open'''       ,      snake_case_       ):
      assert _test_patching.open is mock

   # check that everthing is back to normal when the patch is over

   assert _test_patching.open is open






def lowerCamelCase__    (							)      ->				List[str]:
   # pandas.read_csv is not present in _test_patching
   __snake_case							       =   '''__test_patch_submodule_missing_mock__'''
   with patch_submodule(_test_patching       ,      '''pandas.read_csv'''       ,      snake_case_       ):
      pass






def lowerCamelCase__    (							)      ->				Union[str, Any]:
   # builtin should always be mocked even if they're not in the globals
   # in case they're loaded at one point
   __snake_case							       =   '''__test_patch_submodule_missing_builtin_mock__'''
   # _test_patching doesn't have "len" in its globals
   assert getattr(_test_patching       ,      '''len'''       ,      snake_case_       ) is None
   with patch_submodule(_test_patching       ,      '''len'''       ,      snake_case_       ):
      assert _test_patching.len is mock
   assert _test_patching.len is len






def lowerCamelCase__    (							)      ->				Union[str, Any]:
   __snake_case							       =   '''__test_patch_submodule_start_and_stop_mock__'''
   __snake_case							       =   patch_submodule(_test_patching       ,      '''open'''       ,      snake_case_       )
   assert _test_patching.open is open
   patch.start()
   assert _test_patching.open is mock
   patch.stop()
   assert _test_patching.open is open






def lowerCamelCase__    (							)      ->				Optional[int]:
   from os import rename as original_rename
   from os.path import dirname as original_dirname
   from os.path import join as original_join

   __snake_case							       =   '''__test_patch_submodule_successive_join__'''
   __snake_case							       =   '''__test_patch_submodule_successive_dirname__'''
   __snake_case							       =   '''__test_patch_submodule_successive_rename__'''
   assert _test_patching.os.path.join is original_join
   assert _test_patching.os.path.dirname is original_dirname
   assert _test_patching.os.rename is original_rename

   with patch_submodule(_test_patching       ,      '''os.path.join'''       ,      snake_case_       ):
      with patch_submodule(_test_patching       ,      '''os.rename'''       ,      snake_case_       ):
         with patch_submodule(_test_patching       ,      '''os.path.dirname'''       ,      snake_case_       ):
            assert _test_patching.os.path.join is mock_join
            assert _test_patching.os.path.dirname is mock_dirname
            assert _test_patching.os.rename is mock_rename

    # try another order
   with patch_submodule(_test_patching       ,      '''os.rename'''       ,      snake_case_       ):
      with patch_submodule(_test_patching       ,      '''os.path.join'''       ,      snake_case_       ):
         with patch_submodule(_test_patching       ,      '''os.path.dirname'''       ,      snake_case_       ):
            assert _test_patching.os.path.join is mock_join
            assert _test_patching.os.path.dirname is mock_dirname
            assert _test_patching.os.rename is mock_rename

   assert _test_patching.os.path.join is original_join
   assert _test_patching.os.path.dirname is original_dirname
   assert _test_patching.os.rename is original_rename







def lowerCamelCase__    (							)      ->				Tuple:
   __snake_case							       =   '''__test_patch_submodule_doesnt_exist_mock__'''
   with patch_submodule(_test_patching       ,      '''__module_that_doesn_exist__.__attribute_that_doesn_exist__'''       ,      snake_case_       ):
      pass
   with patch_submodule(_test_patching       ,      '''os.__attribute_that_doesn_exist__'''       ,      snake_case_       ):
      pass

