




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						:   List[Any]		=   logging.get_logger(__name__)

_lowerCAmelCase						:   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",
}
_lowerCAmelCase						:   Tuple		=   [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







 for attribute in key.split("."	):
  UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							_lowerCAmelCase	)

 if weight_type is not None:
  UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							_lowerCAmelCase	).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		(		_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   Optional[int]	):




 """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(
       _lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							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(_lowerCAmelCase	)[0].split("."	)[-2]
      UpperCAmelCase__							 =						mapped_key.replace("*"						,							_lowerCAmelCase	)
     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(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)
    continue
  if not is_used:
   unused_weights.append(_lowerCAmelCase	)

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   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(_lowerCAmelCase	)



@torch.no_grad()
def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   Optional[Any]=None	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						WavLMConfigOrig(checkpoint["cfg"]	)
 UpperCAmelCase__							 =						WavLMOrig(_lowerCAmelCase	)
 model.load_state_dict(checkpoint["model"]	)
 model.eval()

 if config_path is not None:
  UpperCAmelCase__							 =						WavLMConfig.from_pretrained(_lowerCAmelCase	)
 else:
  UpperCAmelCase__							 =						WavLMConfig()

 UpperCAmelCase__							 =						WavLMModel(_lowerCAmelCase	)

 recursively_load_weights(_lowerCAmelCase						,							_lowerCAmelCase	)

 hf_wavlm.save_pretrained(_lowerCAmelCase	)


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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[int]		=   ["MaskFormerFeatureExtractor"]
  _lowerCAmelCase						:   Dict		=   ["MaskFormerImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MaskFormerForInstanceSegmentation",
      "MaskFormerModel",
      "MaskFormerPreTrainedModel",
  ]
  _lowerCAmelCase						:   List[str]		=   [
      "MaskFormerSwinBackbone",
      "MaskFormerSwinModel",
      "MaskFormerSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase						:   str		=   _LazyModule(__name__, globals()["__file__"], _import_structure)





