






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()
SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

SCREAMING_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',
}
SCREAMING_SNAKE_CASE 							=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def      _lowerCamelCase	(						__A    :							Any		,		__A    :							Optional[int]		,		__A    :							List[Any]		,		__A    :							Union[str, Any]		,		__A    :							Optional[int] )      ->					List[str]:
    for attribute in key.split('''.''' ):
        _UpperCAmelCase				:      int			  =				getattr(_lowerCAmelCase		,		_lowerCAmelCase )

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

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

def      _lowerCamelCase	(						__A    :							int		,		__A    :							List[str] )      ->					Union[str, Any]:
    _UpperCAmelCase				:      Optional[Any]			  =				[]
    _UpperCAmelCase				:      Union[str, Any]			  =				fairseq_model.state_dict()

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

    for name, value in fairseq_dict.items():
        _UpperCAmelCase				:      int			  =				False
        if "conv_layers" in name:
            load_conv_layer(
                _lowerCAmelCase		,		_lowerCAmelCase		,		_lowerCAmelCase		,		_lowerCAmelCase		,		hf_model.config.feat_extract_norm == '''group'''		,		)
            _UpperCAmelCase				:      List[Any]			  =				True
        elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
            load_adapter(_lowerCAmelCase		,		_lowerCAmelCase		,		_lowerCAmelCase		,		_lowerCAmelCase )
            _UpperCAmelCase				:      str			  =				True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
                    _UpperCAmelCase				:      Optional[int]			  =				True
                    if "*" in mapped_key:
                        _UpperCAmelCase				:      int			  =				name.split(_lowerCAmelCase )[0].split('''.''' )[-2]
                        _UpperCAmelCase				:      Union[str, Any]			  =				mapped_key.replace('''*'''		,		_lowerCAmelCase )
                    if "weight_g" in name:
                        _UpperCAmelCase				:      List[str]			  =				'''weight_g'''
                    elif "weight_v" in name:
                        _UpperCAmelCase				:      Optional[Any]			  =				'''weight_v'''
                    elif "bias" in name:
                        _UpperCAmelCase				:      List[Any]			  =				'''bias'''
                    elif "weight" in name:
                        _UpperCAmelCase				:      Dict			  =				'''weight'''
                    else:
                        _UpperCAmelCase				:      List[str]			  =				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      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							Optional[Any]		,		__A    :							Optional[int]		,		__A    :							Dict		,		__A    :							str )      ->					Optional[Any]:
    _UpperCAmelCase				:      Optional[int]			  =				full_name.split('''conv_layers.''' )[-1]
    _UpperCAmelCase				:      str			  =				name.split('''.''' )
    _UpperCAmelCase				:      Optional[int]			  =				int(items[0] )
    _UpperCAmelCase				:      str			  =				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				:      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.'''
            )
            _UpperCAmelCase				:      int			  =				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				:      Union[str, Any]			  =				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				:      Dict			  =				value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
    else:
        unused_weights.append(_lowerCAmelCase )

def      _lowerCamelCase	(						__A    :							Optional[Any]		,		__A    :							Any		,		__A    :							Tuple		,		__A    :							str )      ->					str:
    _UpperCAmelCase				:      Optional[int]			  =				full_name.split('''adaptor.''' )[-1]
    _UpperCAmelCase				:      Any			  =				name.split('''.''' )

    if items[1].isdigit():
        _UpperCAmelCase				:      List[Any]			  =				int(items[1] )
    else:
        _UpperCAmelCase				:      Optional[int]			  =				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.'''
                _UpperCAmelCase				:      List[str]			  =				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.'''
                _UpperCAmelCase				:      List[str]			  =				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.'''
                _UpperCAmelCase				:      List[Any]			  =				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.'''
                _UpperCAmelCase				:      str			  =				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.'''
            _UpperCAmelCase				:      Any			  =				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.'''
            _UpperCAmelCase				:      Dict			  =				value
            logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
    else:
        unused_weights.append(_lowerCAmelCase )

def      _lowerCamelCase	(						__A    :							Optional[int] )      ->					Optional[Any]:
    _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				emb.weight.shape
    _UpperCAmelCase				:      Optional[int]			  =				nn.Linear(_lowerCAmelCase		,		_lowerCAmelCase		,		bias=_lowerCAmelCase )
    _UpperCAmelCase				:      Dict			  =				emb.weight.data
    return lin_layer

@torch.no_grad()
def      _lowerCamelCase	(						__A    :							Optional[Any]		,		__A    :							Any		,		__A    :							Union[str, Any]		,		__A    :							Optional[Any]		,		__A    :							int		,		__A    :							List[str]		,		__A    :							Optional[int]		,		__A    :							Tuple		,		__A    :							Optional[Any]		,		__A    :							List[Any]		,		__A    :							List[str]		,		)      ->					Optional[int]:
    _UpperCAmelCase				:      Union[str, Any]			  =				WavaVecaConfig.from_pretrained(
        _lowerCAmelCase		,		add_adapter=_lowerCAmelCase		,		adapter_stride=_lowerCAmelCase		,		adapter_kernel_size=_lowerCAmelCase		,		use_auth_token=_lowerCAmelCase		,		output_hidden_size=_lowerCAmelCase		,		)
    _UpperCAmelCase				:      List[Any]			  =				MBartConfig.from_pretrained(_lowerCAmelCase )

    # load model
    _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				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,
        }		,		)
    _UpperCAmelCase				:      Optional[int]			  =				model[0].eval()

    # load feature extractor
    _UpperCAmelCase				:      str			  =				WavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase		,		use_auth_token=_lowerCAmelCase )

    # set weights for wav2vec2 encoder
    _UpperCAmelCase				:      Optional[Any]			  =				WavaVecaModel(_lowerCAmelCase )

    recursively_load_weights_wavaveca(model.encoder		,		_lowerCAmelCase )

    # load decoder weights
    _UpperCAmelCase				:      List[str]			  =				MBartForCausalLM(_lowerCAmelCase )
    _UpperCAmelCase  , _UpperCAmelCase				:      Tuple			  =				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}''' )

    _UpperCAmelCase				:      Optional[Any]			  =				SpeechEncoderDecoderModel(encoder=_lowerCAmelCase		,		decoder=_lowerCAmelCase )
    _UpperCAmelCase				:      Tuple			  =				False

    _UpperCAmelCase				:      Union[str, Any]			  =				MBartaaTokenizer(_lowerCAmelCase )
    tokenizer.save_pretrained(_lowerCAmelCase )

    _UpperCAmelCase				:      Union[str, Any]			  =				hf_wavavec.config.to_dict()
    _UpperCAmelCase				:      List[str]			  =				tokenizer.pad_token_id
    _UpperCAmelCase				:      List[Any]			  =				tokenizer.bos_token_id
    _UpperCAmelCase				:      Optional[int]			  =				tokenizer.eos_token_id
    _UpperCAmelCase				:      str			  =				'''mbart50'''
    _UpperCAmelCase				:      Optional[Any]			  =				'''wav2vec2'''

    _UpperCAmelCase				:      int			  =				tokenizer.eos_token_id
    _UpperCAmelCase				:      Any			  =				250_004
    _UpperCAmelCase				:      Optional[int]			  =				tokenizer.eos_token_id

    _UpperCAmelCase				:      int			  =				SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase )

    hf_wavavec.save_pretrained(_lowerCAmelCase )
    feature_extractor.save_pretrained(_lowerCAmelCase )


if __name__ == "__main__":
  SCREAMING_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=1024, type=int, help='encoder output dim')
  parser.add_argument('--start_token_id', default=250004, type=int, help='`decoder_start_token_id` of model config')

  SCREAMING_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,
  )










import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(__A		,		__A		,		__A )

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__A ) )
    torch.save(model.state_dict()		,		__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--rembert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained RemBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




