





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  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> Optional[Any]:

							"""simple docstring"""



							for attribute in key.split("""."""				):
														lowercase_	:   List[str]  				=     getattr(lowercase				,       lowercase				)

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

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





def  __magic_name__  (				lowercase				,       lowercase				)				-> Dict:

							"""simple docstring"""



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

							lowercase_	:   int  				=     hf_model.feature_extractor

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

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

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





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> int:

							"""simple docstring"""



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





@torch.no_grad()
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase=None				)				-> Dict:

							"""simple docstring"""



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

							if config_path is not None:
														lowercase_	:   Tuple  				=     WavLMConfig.from_pretrained(lowercase				)
							else:
														lowercase_	:   Dict  				=     WavLMConfig()

							lowercase_	:   Dict  				=     WavLMModel(lowercase				)

							recursively_load_weights(lowercase				,       lowercase				)

							hf_wavlm.save_pretrained(lowercase				)


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)






UpperCAmelCase_							=				8.31_44_62  # Unit - J mol-1 K-1





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or volume < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or pressure < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
							from doctest import testmod

							testmod()