



"""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()
lowerCAmelCase__              =							logging.get_logger(__name__)

lowerCAmelCase__              =							{
    '''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''',
}
lowerCAmelCase__              =							[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 			snake_case_    ( A_   :						List[str],							A_   :						str,							A_   :						Optional[int],							A_   :						Tuple,							A_   :						Dict		):


       '''simple docstring'''





       for attribute in key.split('''.'''		):
              _lowerCamelCase		:							Optional[Any]            =					getattr(A_,							A_		)

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

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






def 			snake_case_    ( A_   :						List[str],							A_   :						str		):


       '''simple docstring'''





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

       _lowerCamelCase		:							Tuple            =					hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _lowerCamelCase		:							str            =					False
              if "conv_layers" in name:
                     load_conv_layer(
                         A_,							A_,							A_,							A_,							hf_model.config.feat_extract_norm == '''group''',							)
                     _lowerCamelCase		:							List[Any]            =					True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
                                   _lowerCamelCase		:							Tuple            =					True
                                   if "*" in mapped_key:
                                          _lowerCamelCase		:							int            =					name.split(A_		)[0].split('''.'''		)[-2]
                                          _lowerCamelCase		:							List[str]            =					mapped_key.replace('''*''',							A_		)
                                   if "weight_g" in name:
                                          _lowerCamelCase		:							int            =					'''weight_g'''
                                   elif "weight_v" in name:
                                          _lowerCamelCase		:							List[Any]            =					'''weight_v'''
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          _lowerCamelCase		:							Dict            =					'''bias'''
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          _lowerCamelCase		:							int            =					'''weight'''
                                   else:
                                          _lowerCamelCase		:							Tuple            =					None

                                   set_recursively(A_,							A_,							A_,							A_,							A_		)
                            continue
              if not is_used:
                     unused_weights.append(A_		)

       logger.warning(F'''Unused weights: {unused_weights}'''		)






def 			snake_case_    ( A_   :						List[Any],							A_   :						int,							A_   :						Optional[int],							A_   :						int,							A_   :						Optional[Any]		):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[Any]            =					full_name.split('''conv_layers.'''		)[-1]
       _lowerCamelCase		:							str            =					name.split('''.'''		)
       _lowerCamelCase		:							str            =					int(items[0]		)
       _lowerCamelCase		:							Union[str, 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		:							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.'''
                     )
                     _lowerCamelCase		:							List[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."
                     )
                     _lowerCamelCase		:							Union[str, 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		:							Optional[int]            =					value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
       else:
              unused_weights.append(A_		)






@torch.no_grad()
def 			snake_case_    ( A_   :						str,							A_   :						Any,							A_   :						Optional[int]=None		):


       '''simple docstring'''





       _lowerCamelCase		:							Any            =					torch.load(A_		)
       _lowerCamelCase		:							Any            =					WavLMConfigOrig(checkpoint['''cfg''']		)
       _lowerCamelCase		:							List[str]            =					WavLMOrig(A_		)
       model.load_state_dict(checkpoint['''model''']		)
       model.eval()

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

       _lowerCamelCase		:							int            =					WavLMModel(A_		)

       recursively_load_weights(A_,							A_		)

       hf_wavlm.save_pretrained(A_		)


if __name__ == "__main__":
   lowerCAmelCase__              =							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''')
   lowerCAmelCase__              =							parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import torch




class       snake_case__ (				TensorFormatter[Mapping, "torch.Tensor", Mapping]):

       '''simple docstring'''



       def __init__(				self      ,    a__=None      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            super().__init__(features=a__     )
            __snake_case      :int 							=    torch_tensor_kwargs
            import torch  # noqa import torch at initialization



       def        __lowercase    (				self      ,    a__     )			->   List[str]:






            '''simple docstring'''





            import torch

            if isinstance(a__      ,    a__     ) and column:
                 if all(
                     isinstance(a__      ,    torch.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                     for x in column     ):
                      return torch.stack(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            import torch

            if isinstance(a__      ,    (str, bytes, type(a__     ))     ):
                 return value
            elif isinstance(a__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
                 return value.tolist()

            __snake_case      :Optional[int] 							=    {}

            if isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.integer     ):
                 __snake_case      :List[Any] 							=    {"""dtype""": torch.intaa}
            elif isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.floating     ):
                 __snake_case      :Tuple 							=    {"""dtype""": torch.floataa}
            elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(a__      ,    PIL.Image.Image     ):
                      __snake_case      :Union[str, Any] 							=    np.asarray(a__     )
            return torch.tensor(a__      ,    **{**default_dtype, **self.torch_tensor_kwargs}     )



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            import torch

            # support for torch, tf, jax etc.
            if hasattr(a__      ,    """__array__"""     ) and not isinstance(a__      ,    torch.Tensor     ):
                 __snake_case      :Optional[Any] 							=    data_struct.__array__()
            # support for nested types like struct of list of struct
            if isinstance(a__      ,    np.ndarray     ):
                 if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                      return self._consolidate([self.recursive_tensorize(a__     ) for substruct in data_struct]     )
            elif isinstance(a__      ,    (list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(a__     ) for substruct in data_struct]     )
            return self._tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            return map_nested(self._recursive_tensorize      ,    a__      ,    map_list=a__     )



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.numpy_arrow_extractor().extract_row(a__     )
            __snake_case      :Any 							=    self.python_features_decoder.decode_row(a__     )
            return self.recursive_tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   "torch.Tensor":






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.numpy_arrow_extractor().extract_column(a__     )
            __snake_case      :List[Any] 							=    self.python_features_decoder.decode_column(a__      ,    pa_table.column_names[0]     )
            __snake_case      :Optional[Any] 							=    self.recursive_tensorize(a__     )
            __snake_case      :Any 							=    self._consolidate(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.numpy_arrow_extractor().extract_batch(a__     )
            __snake_case      :Tuple 							=    self.python_features_decoder.decode_batch(a__     )
            __snake_case      :Tuple 							=    self.recursive_tensorize(a__     )
            for column_name in batch:
                 __snake_case      :str 							=    self._consolidate(batch[column_name]     )
            return batch


