





"""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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




       for attribute in key.split('.'							):
              _UpperCAmelCase     =     getattr(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       if weight_type is not None:
              _UpperCAmelCase     =     getattr(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							).shape
       else:
              _UpperCAmelCase     =     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     =     value
       elif weight_type == "weight_g":
              _UpperCAmelCase     =     value
       elif weight_type == "weight_v":
              _UpperCAmelCase     =     value
       elif weight_type == "bias":
              _UpperCAmelCase     =     value
       else:
              _UpperCAmelCase     =     value

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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     fairseq_model.state_dict()

       _UpperCAmelCase     =     hf_model.feature_extractor

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

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

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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[int]:






       """simple docstring"""




       _UpperCAmelCase     =     full_name.split('conv_layers.'							)[-1]
       _UpperCAmelCase     =     name.split('.'							)
       _UpperCAmelCase     =     int(items[0]							)
       _UpperCAmelCase     =     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     =     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     =     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     =     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     =     value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE							)

@torch.no_grad()
def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     WavLMConfigOrig(checkpoint['cfg']							)
       _UpperCAmelCase     =     WavLMOrig(SCREAMING_SNAKE_CASE							)
       model.load_state_dict(checkpoint['model']							)
       model.eval()

       if config_path is not None:
              _UpperCAmelCase     =     WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE							)
       else:
              _UpperCAmelCase     =     WavLMConfig()

       _UpperCAmelCase     =     WavLMModel(SCREAMING_SNAKE_CASE							)

       recursively_load_weights(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       hf_wavlm.save_pretrained(SCREAMING_SNAKE_CASE							)


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)













"""simple docstring"""






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_			=							{'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_			=							['''TimmBackbone''']


if TYPE_CHECKING:
    from .configuration_timm_backbone import TimmBackboneConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timm_backbone import TimmBackbone

else:
    import sys

    lowerCAmelCase_			=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






