
'''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()
snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """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""",
}
snake_case									=    [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 for attribute in key.split("."			):
  lowerCAmelCase__     :   Any     		=	getattr(lowerCamelCase_      ,			lowerCamelCase_			)

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

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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	[]
 lowerCAmelCase__     :   Tuple     		=	fairseq_model.state_dict()

 lowerCAmelCase__     :   Tuple     		=	hf_model.feature_extractor
 lowerCAmelCase__     :   Dict     		=	hf_model.adapter

 for name, value in fairseq_dict.items():
  lowerCAmelCase__     :   List[str]     		=	False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			hf_model.config.feat_extract_norm == "group"      ,			)
   lowerCAmelCase__     :   Union[str, Any]     		=	True
  elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]			):
   load_adapter(lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)
   lowerCAmelCase__     :   List[Any]     		=	True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("w2v_model."			)[-1] == name.split("."			)[0]:
     lowerCAmelCase__     :   Any     		=	True
     if "*" in mapped_key:
      lowerCAmelCase__     :   Tuple     		=	name.split(lowerCamelCase_			)[0].split("."			)[-2]
      lowerCAmelCase__     :   Tuple     		=	mapped_key.replace("*"      ,			lowerCamelCase_			)
     if "weight_g" in name:
      lowerCAmelCase__     :   Tuple     		=	"weight_g"
     elif "weight_v" in name:
      lowerCAmelCase__     :   Dict     		=	"weight_v"
     elif "bias" in name:
      lowerCAmelCase__     :   Optional[int]     		=	"bias"
     elif "weight" in name:
      lowerCAmelCase__     :   Any     		=	"weight"
     else:
      lowerCAmelCase__     :   Dict     		=	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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	full_name.split("adaptor."			)[-1]
 lowerCAmelCase__     :   Optional[Any]     		=	name.split("."			)

 if items[1].isdigit():
  lowerCAmelCase__     :   List[Any]     		=	int(items[1]			)
 else:
  lowerCAmelCase__     :   Dict     		=	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.'''
    lowerCAmelCase__     :   Tuple     		=	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.'''
    lowerCAmelCase__     :   Optional[Any]     		=	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.'''
    lowerCAmelCase__     :   Tuple     		=	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.'''
    lowerCAmelCase__     :   int     		=	value
    logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''			)
 elif isinstance(lowerCamelCase_      ,			lowerCamelCase_			):
  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.'''
   lowerCAmelCase__     :   Optional[int]     		=	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.'''
   lowerCAmelCase__     :   int     		=	value
   logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''			)
 else:
  unused_weights.append(lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	emb.weight.shape
 lowerCAmelCase__     :   Dict     		=	nn.Linear(lowerCamelCase_      ,			lowerCamelCase_      ,			bias=lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[int]     		=	emb.weight.data
 return lin_layer


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	WavaVecaConfig.from_pretrained(
     lowerCamelCase_      ,			add_adapter=lowerCamelCase_      ,			adapter_stride=lowerCamelCase_      ,			adapter_kernel_size=lowerCamelCase_      ,			use_auth_token=lowerCamelCase_      ,			output_hidden_size=lowerCamelCase_      ,			)
 lowerCAmelCase__     :   Optional[int]     		=	MBartConfig.from_pretrained(lowerCamelCase_			)

 # load model
 lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__     :   Dict     		=	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,
     }      ,			)
 lowerCAmelCase__     :   Any     		=	model[0].eval()

 # load feature extractor
 lowerCAmelCase__     :   int     		=	WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_      ,			use_auth_token=lowerCamelCase_			)

 # set weights for wav2vec2 encoder
 lowerCAmelCase__     :   List[Any]     		=	WavaVecaModel(lowerCamelCase_			)

 recursively_load_weights_wavaveca(model.encoder      ,			lowerCamelCase_			)

 # load decoder weights
 lowerCAmelCase__     :   Any     		=	MBartForCausalLM(lowerCamelCase_			)
 lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,			strict=lowerCamelCase_			)
 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}'''			)

 lowerCAmelCase__     :   List[Any]     		=	SpeechEncoderDecoderModel(encoder=lowerCamelCase_      ,			decoder=lowerCamelCase_			)
 lowerCAmelCase__     :   int     		=	False

 lowerCAmelCase__     :   Optional[Any]     		=	MBartaaTokenizer(lowerCamelCase_			)
 tokenizer.save_pretrained(lowerCamelCase_			)

 lowerCAmelCase__     :   Optional[Any]     		=	hf_wavavec.config.to_dict()
 lowerCAmelCase__     :   Dict     		=	tokenizer.pad_token_id
 lowerCAmelCase__     :   Any     		=	tokenizer.bos_token_id
 lowerCAmelCase__     :   List[Any]     		=	tokenizer.eos_token_id
 lowerCAmelCase__     :   Union[str, Any]     		=	"mbart50"
 lowerCAmelCase__     :   Optional[Any]     		=	"wav2vec2"

 lowerCAmelCase__     :   int     		=	tokenizer.eos_token_id
 lowerCAmelCase__     :   Optional[Any]     		=	2_5_0_0_0_4
 lowerCAmelCase__     :   List[str]     		=	tokenizer.eos_token_id

 lowerCAmelCase__     :   List[Any]     		=	SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_			)

 hf_wavavec.save_pretrained(lowerCamelCase_			)
 feature_extractor.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
     snake_case									=    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=10_24, type=int, help="""encoder output dim""")
     parser.add_argument("""--start_token_id""", default=25_00_04, type=int, help="""`decoder_start_token_id` of model config""")

     snake_case									=    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'''

import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
snake_case									=    """."""


if __name__ == "__main__":
     snake_case									=    os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
     snake_case									=    []
     snake_case									=    []
     with open(doctest_file_path) as fp:
          for line in fp:
               snake_case									=    line.strip()
               snake_case									=    os.path.join(REPO_PATH, line)
               if not (os.path.isfile(path) or os.path.isdir(path)):
                    non_existent_paths.append(line)
               all_paths.append(path)
     if len(non_existent_paths) > 0:
          snake_case									=    """\n""".join(non_existent_paths)
          raise ValueError(f'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
     if all_paths != sorted(all_paths):
          raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")





