


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[str]       =				logging.get_logger(__name__)

lowerCAmelCase__    :int       =				{
    '''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__    :Union[str, Any]       =				[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def 			lowerCAmelCase__					(     a__:						Tuple     ,    a__:						Any     ,    a__:						Any     ,    a__:						Optional[Any]     ,    a__:						str			)    ->						Dict:


							'''simple docstring'''





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

							if weight_type is not None:
														_UpperCAmelCase											=     getattr(a__     ,    a__			).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__					(     a__:						Optional[Any]     ,    a__:						Optional[Any]			)    ->						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(
																					    a__     ,    a__     ,    a__     ,    a__     ,    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(a__			)[0].split('.'			)[-2]
																																										_UpperCAmelCase											=     mapped_key.replace('*'     ,    a__			)
																																			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(a__     ,    a__     ,    a__     ,    a__     ,    a__			)
																												continue
														if not is_used:
																					unused_weights.append(a__			)

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

def 			lowerCAmelCase__					(     a__:						Tuple     ,    a__:						Optional[Any]     ,    a__:						int     ,    a__:						Optional[int]     ,    a__:						Tuple			)    ->						Union[str, Any]:


							'''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(a__			)

@torch.no_grad()
def 			lowerCAmelCase__					(     a__:						List[str]     ,    a__:						Optional[int]     ,    a__:						Tuple=None			)    ->						Optional[Any]:


							'''simple docstring'''





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

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

							_UpperCAmelCase											=     WavLMModel(a__			)

							recursively_load_weights(a__     ,    a__			)

							hf_wavlm.save_pretrained(a__			)


if __name__ == "__main__":
		lowerCAmelCase__    :List[str]       =				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__    :str       =				parser.parse_args()
		convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCAmelCase__    :int       =				logging.get_logger(__name__)

def 			lowerCAmelCase__					(     a__:						Dict			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     R'\w+[.]\d+'
							_UpperCAmelCase											=     re.findall(a__     ,    a__			)
							for pat in pats:
														_UpperCAmelCase											=     key.replace(a__     ,    '_'.join(pat.split('.'			)			)			)
							return key

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						Dict     ,    a__:						List[Any]			)    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
							if (
							    any('norm' in str_ for str_ in pt_tuple_key			)
							    and (pt_tuple_key[-1] == "bias")
							    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
							    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
							):
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
														return renamed_pt_tuple_key, pt_tensor
							elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('embedding',)
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('kernel',)
							if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
														_UpperCAmelCase											=     pt_tensor.transpose(2     ,    3     ,    1     ,    0			)
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('kernel',)
							if pt_tuple_key[-1] == "weight":
														_UpperCAmelCase											=     pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm weight
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('weight',)
							if pt_tuple_key[-1] == "gamma":
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm bias
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('bias',)
							if pt_tuple_key[-1] == "beta":
														return renamed_pt_tuple_key, pt_tensor

							return pt_tuple_key, pt_tensor







def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Optional[Any]     ,    a__:						int=4_2			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     {k: v.numpy() for k, v in pt_state_dict.items()}

							# Step 2: Since the model is stateless, get random Flax params
							_UpperCAmelCase											=     flax_model.init_weights(PRNGKey(a__			)			)

							_UpperCAmelCase											=     flatten_dict(a__			)
							_UpperCAmelCase											=     {}

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														_UpperCAmelCase											=     rename_key(a__			)
														_UpperCAmelCase											=     tuple(renamed_pt_key.split('.'			)			)

														# Correctly rename weight parameters
														_UpperCAmelCase						,			_UpperCAmelCase											=     rename_key_and_reshape_tensor(a__     ,    a__     ,    a__			)

														if flax_key in random_flax_state_dict:
																					if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																												raise ValueError(
																												    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																												    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''			)

        # also add unexpected weight so that warning is thrown
														_UpperCAmelCase											=     jnp.asarray(a__			)

							return unflatten_dict(a__			)
