





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

_A					:				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""",
}
_A					:				Any				=			[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    for attribute in key.split('''.'''	):
        SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	)

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

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


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						int:
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			fairseq_model.state_dict()

    SCREAMING_SNAKE_CASE__											=			hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        SCREAMING_SNAKE_CASE__											=			False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							hf_model.config.feat_extract_norm == '''group''' ,							)
            SCREAMING_SNAKE_CASE__											=			True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
                    SCREAMING_SNAKE_CASE__											=			True
                    if "*" in mapped_key:
                        SCREAMING_SNAKE_CASE__											=			name.split(lowerCAmelCase_	)[0].split('''.'''	)[-2]
                        SCREAMING_SNAKE_CASE__											=			mapped_key.replace('''*''' ,							lowerCAmelCase_	)
                    if "weight_g" in name:
                        SCREAMING_SNAKE_CASE__											=			'''weight_g'''
                    elif "weight_v" in name:
                        SCREAMING_SNAKE_CASE__											=			'''weight_v'''
                    elif "bias" in name and "relative_attention_bias" not in name:
                        SCREAMING_SNAKE_CASE__											=			'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        SCREAMING_SNAKE_CASE__											=			'''weight'''
                    else:
                        SCREAMING_SNAKE_CASE__											=			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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			full_name.split('''conv_layers.'''	)[-1]
    SCREAMING_SNAKE_CASE__											=			name.split('''.'''	)
    SCREAMING_SNAKE_CASE__											=			int(items[0]	)
    SCREAMING_SNAKE_CASE__											=			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.'''
            )
            SCREAMING_SNAKE_CASE__											=			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.'''
            )
            SCREAMING_SNAKE_CASE__											=			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."
            )
            SCREAMING_SNAKE_CASE__											=			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.'''
            )
            SCREAMING_SNAKE_CASE__											=			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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			WavLMConfigOrig(checkpoint['''cfg''']	)
    SCREAMING_SNAKE_CASE__											=			WavLMOrig(lowerCAmelCase_	)
    model.load_state_dict(checkpoint['''model''']	)
    model.eval()

    if config_path is not None:
        SCREAMING_SNAKE_CASE__											=			WavLMConfig.from_pretrained(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			WavLMConfig()

    SCREAMING_SNAKE_CASE__											=			WavLMModel(lowerCAmelCase_	)

    recursively_load_weights(lowerCAmelCase_ ,							lowerCAmelCase_	)

    hf_wavlm.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
  _A					:				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""")
  _A					:				str				=			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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_           =				{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

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

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''TFEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_encoder_decoder import EncoderDecoderConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_encoder_decoder import EncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
  import sys

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