


'''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()
lowerCamelCase							:   str  						=	logging.get_logger(__name__)

lowerCamelCase							:   List[str]  						=	{
    '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							:   Dict  						=	[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A  , A       )       ->       List[str]:






       """simple docstring"""







       for attribute in key.split('''.'''       ):
              lowercase__      			= getattr(A  , A       )

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

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

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= []
       lowercase__      			= fairseq_model.state_dict()

       lowercase__      			= hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__      			= False
              if "conv_layers" in name:
                     load_conv_layer(
                         A  , A  , A  , A  , hf_model.config.feat_extract_norm == '''group'''  , )
                     lowercase__      			= True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
                                   lowercase__      			= True
                                   if "*" in mapped_key:
                                          lowercase__      			= name.split(A       )[0].split('''.'''       )[-2]
                                          lowercase__      			= mapped_key.replace('''*'''  , A       )
                                   if "weight_g" in name:
                                          lowercase__      			= '''weight_g'''
                                   elif "weight_v" in name:
                                          lowercase__      			= '''weight_v'''
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          lowercase__      			= '''bias'''
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase__      			= '''weight'''
                                   else:
                                          lowercase__      			= 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 _SCREAMING_SNAKE_CASE      (A  , A  , A  , A  , A       )       ->       List[str]:






       """simple docstring"""







       lowercase__      			= full_name.split('''conv_layers.'''       )[-1]
       lowercase__      			= name.split('''.'''       )
       lowercase__      			= int(items[0]       )
       lowercase__      			= 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__      			= 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__      			= 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__      			= 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__      			= 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 _SCREAMING_SNAKE_CASE      (A  , A  , A=None       )       ->       Union[str, Any]:






       """simple docstring"""







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

       if config_path is not None:
              lowercase__      			= WavLMConfig.from_pretrained(A       )
       else:
              lowercase__      			= WavLMConfig()

       lowercase__      			= WavLMModel(A       )

       recursively_load_weights(A  , A       )

       hf_wavlm.save_pretrained(A       )


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	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)








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config',
     default='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




