

'''simple docstring'''






import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''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_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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCAmelCase__											=	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 for attribute in key.split('''.'''      ):
  __lowercase							  = getattr(__snake_case     ,				__snake_case      )

 if weight_type is not None:
  __lowercase							  = getattr(__snake_case     ,				__snake_case      ).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 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = []
 __lowercase							  = fairseq_model.state_dict()

 __lowercase							  = hf_model.feature_extractor
 __lowercase							  = hf_model.adapter

 for name, value in fairseq_dict.items():
  __lowercase							  = False
  if "conv_layers" in name:
   load_conv_layer(
       __snake_case     ,				__snake_case     ,				__snake_case     ,				__snake_case     ,				hf_model.config.feat_extract_norm == '''group'''     ,				)
   __lowercase							  = True
  elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']      ):
   load_adapter(__snake_case     ,				__snake_case     ,				__snake_case     ,				__snake_case      )
   __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(__snake_case      )[0].split('''.'''      )[-2]
      __lowercase							  = mapped_key.replace('''*'''     ,				__snake_case      )
     if "weight_g" in name:
      __lowercase							  = '''weight_g'''
     elif "weight_v" in name:
      __lowercase							  = '''weight_v'''
     elif "bias" in name:
      __lowercase							  = '''bias'''
     elif "weight" in name:
      __lowercase							  = '''weight'''
     else:
      __lowercase							  = None
     set_recursively(__snake_case     ,				__snake_case     ,				__snake_case     ,				__snake_case     ,				__snake_case      )
    continue
  if not is_used:
   unused_weights.append(__snake_case      )

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


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """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(__snake_case      )


def 		_A     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = full_name.split('''adaptor.'''      )[-1]
 __lowercase							  = name.split('''.'''      )

 if items[1].isdigit():
  __lowercase							  = int(items[1]      )
 else:
  __lowercase							  = None

 if "adaptor" not in full_name:
  if "proj_ln" in full_name:
   # has to be layer norm
   if "bias" in name:
    assert (
        value.shape == adapter.proj_layer_norm.bias.data.shape
    ), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
    __lowercase							  = value
    logger.info(F"Adapter proj layer norm bias was initialized from {full_name}."      )
   if "weight" in name:
    assert (
        value.shape == adapter.proj_layer_norm.weight.data.shape
    ), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
    __lowercase							  = value
  else:
   # has to be projection layer
   if "bias" in name:
    assert (
        value.shape == adapter.proj.bias.data.shape
    ), F"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
    __lowercase							  = value
    logger.info(F"Adapter proj layer bias was initialized from {full_name}."      )
   if "weight" in name:
    assert (
        value.shape == adapter.proj.weight.data.shape
    ), F"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
    __lowercase							  = value
    logger.info(F"Adapter proj layer weight was initialized from {full_name}."      )
 elif isinstance(__snake_case     ,				__snake_case      ):
  if "bias" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.bias.data.shape
   ), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
   __lowercase							  = value
   logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."      )
  elif "weight" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.weight.data.shape
   ), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
   __lowercase							  = value
   logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."      )
 else:
  unused_weights.append(__snake_case      )


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = emb.weight.shape
 __lowercase							  = nn.Linear(__snake_case     ,				__snake_case     ,				bias=__snake_case      )
 __lowercase							  = emb.weight.data
 return lin_layer


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				):






 """simple docstring"""



 __lowercase							  = WavaVecaConfig.from_pretrained(
     __snake_case     ,				add_adapter=__snake_case     ,				adapter_stride=__snake_case     ,				adapter_kernel_size=__snake_case     ,				use_auth_token=__snake_case     ,				output_hidden_size=__snake_case     ,				)
 __lowercase							  = MBartConfig.from_pretrained(__snake_case      )

 # load model
 __lowercase							  = fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]     ,				arg_overrides={
         '''config_yaml''': config_yaml_path,
         '''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      ),
         '''w2v_path''': checkpoint_path,
         '''load_pretrained_decoder_from''': None,
     }     ,				)
 __lowercase							  = model[0].eval()

 # load feature extractor
 __lowercase							  = WavaVecaFeatureExtractor.from_pretrained(__snake_case     ,				use_auth_token=__snake_case      )

 # set weights for wav2vec2 encoder
 __lowercase							  = WavaVecaModel(__snake_case      )

 recursively_load_weights_wavaveca(model.encoder     ,				__snake_case      )

 # load decoder weights
 __lowercase							  = MBartForCausalLM(__snake_case      )
 __lowercase							  = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,				strict=__snake_case      )
 logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"      )
 logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"      )

 __lowercase							  = SpeechEncoderDecoderModel(encoder=__snake_case     ,				decoder=__snake_case      )
 __lowercase							  = False

 __lowercase							  = MBartaaTokenizer(__snake_case      )
 tokenizer.save_pretrained(__snake_case      )

 __lowercase							  = hf_wavavec.config.to_dict()
 __lowercase							  = tokenizer.pad_token_id
 __lowercase							  = tokenizer.bos_token_id
 __lowercase							  = tokenizer.eos_token_id
 __lowercase							  = '''mbart50'''
 __lowercase							  = '''wav2vec2'''

 __lowercase							  = tokenizer.eos_token_id
 __lowercase							  = 250004
 __lowercase							  = tokenizer.eos_token_id

 __lowercase							  = SpeechEncoderDecoderConfig.from_dict(__snake_case      )

 hf_wavavec.save_pretrained(__snake_case      )
 feature_extractor.save_pretrained(__snake_case      )


if __name__ == "__main__":
 lowerCAmelCase__											=	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('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
 parser.add_argument(
     '''--encoder_config_path''',
     default='''facebook/wav2vec2-xls-r-1b''',
     type=str,
     help='''Path to hf encoder wav2vec2 checkpoint config''',
 )
 parser.add_argument(
     '''--decoder_config_path''',
     default='''facebook/mbart-large-50-one-to-many-mmt''',
     type=str,
     help='''Path to hf decoder checkpoint config''',
 )
 parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
 parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
 parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
 parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
 parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')

 lowerCAmelCase__											=	parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )









"""simple docstring"""



__A		:       Any  				=		{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}



