

"""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()
_a							=							logging.get_logger(__name__)

_a							=							{
    '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',
}
_a							=							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def   _A  (  UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	int,      UpperCamelCase_					:	List[Any])     ->						List[str]:
   '''simple docstring'''


   for attribute in key.split("."):
      __lowercase								=						getattr(UpperCamelCase_,      UpperCamelCase_)

   if weight_type is not None:
      __lowercase								=						getattr(UpperCamelCase_,      UpperCamelCase_).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  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	str)     ->						Dict:
   '''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(
             UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      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(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)
         __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(UpperCamelCase_)[0].split(".")[-2]
                  __lowercase								=						mapped_key.replace("*",      UpperCamelCase_)
               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(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)
            continue
      if not is_used:
         unused_weights.append(UpperCamelCase_)

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




def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	str,      UpperCamelCase_					:	str)     ->						Optional[Any]:
   '''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(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	List[str],      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	int)     ->						str:
   '''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(UpperCamelCase_,      UpperCamelCase_):
      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(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Tuple)     ->						Optional[int]:
   '''simple docstring'''


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




@torch.no_grad()
def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Any,      UpperCamelCase_					:	str,      UpperCamelCase_					:	int,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	int,      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	str,      )     ->						Any:
   '''simple docstring'''


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

   # load model
   __lowercase		,__lowercase		,__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(UpperCamelCase_,      use_auth_token=UpperCamelCase_)

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

   recursively_load_weights_wavaveca(model.encoder,      UpperCamelCase_)

   # load decoder weights
   __lowercase								=						MBartForCausalLM(UpperCamelCase_)
   __lowercase		,__lowercase								=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),      strict=UpperCamelCase_)
   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=UpperCamelCase_,      decoder=UpperCamelCase_)
   __lowercase								=						False

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

   __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(UpperCamelCase_)

   hf_wavavec.save_pretrained(UpperCamelCase_)
   feature_extractor.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
  _a							=							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')

  _a							=							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 fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      **UpperCamelCase_					:	Optional[int])     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						AutoConfig.from_pretrained(UpperCamelCase_,      **UpperCamelCase_)
   __lowercase								=						AutoModelForSeqaSeqLM.from_config(UpperCamelCase_)
   model.save_pretrained(UpperCamelCase_)
   AutoTokenizer.from_pretrained(UpperCamelCase_).save_pretrained(UpperCamelCase_)
   return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)




