

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

__UpperCAmelCase			=					{
    '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			=					[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





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

 if weight_type is not None:
  UpperCAmelCase__       :	Optional[int]        =       getattr(__UpperCamelCase				,     __UpperCamelCase   ).shape
 else:
  UpperCAmelCase__       :	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__       :	str        =       value
 elif weight_type == "weight_g":
  UpperCAmelCase__       :	Dict        =       value
 elif weight_type == "weight_v":
  UpperCAmelCase__       :	List[str]        =       value
 elif weight_type == "bias":
  UpperCAmelCase__       :	Optional[Any]        =       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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	List[Any]        =       []
 UpperCAmelCase__       :	int        =       fairseq_model.state_dict()

 UpperCAmelCase__       :	Any        =       hf_model.feature_extractor

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





 UpperCAmelCase__       :	Dict        =       full_name.split("""conv_layers."""   )[-1]
 UpperCAmelCase__       :	Tuple        =       name.split("""."""   )
 UpperCAmelCase__       :	Optional[Any]        =       int(items[0]   )
 UpperCAmelCase__       :	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__       :	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__       :	Tuple        =       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__       :	Optional[int]        =       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__       :	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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=None   ):
 '''simple docstring'''





 UpperCAmelCase__       :	str        =       torch.load(__UpperCamelCase   )
 UpperCAmelCase__       :	List[str]        =       WavLMConfigOrig(checkpoint["""cfg"""]   )
 UpperCAmelCase__       :	Optional[Any]        =       WavLMOrig(__UpperCamelCase   )
 model.load_state_dict(checkpoint["""model"""]   )
 model.eval()

 if config_path is not None:
  UpperCAmelCase__       :	Tuple        =       WavLMConfig.from_pretrained(__UpperCamelCase   )
 else:
  UpperCAmelCase__       :	List[Any]        =       WavLMConfig()

 UpperCAmelCase__       :	Optional[int]        =       WavLMModel(__UpperCamelCase   )

 recursively_load_weights(__UpperCamelCase				,     __UpperCamelCase   )

 hf_wavlm.save_pretrained(__UpperCamelCase   )


if __name__ == "__main__":
      __UpperCAmelCase			=					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			=					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


lowerCAmelCase							:       str					 =	{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       Optional[Any]					 =	[
							    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PegasusXForConditionalGeneration""",
							    """PegasusXModel""",
							    """PegasusXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pegasus_x import (
														    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PegasusXForConditionalGeneration,
														    PegasusXModel,
														    PegasusXPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase							:       str					 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



