





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()
_lowercase							:      Optional[int]      							=logging.get_logger(__name__)

_lowercase							:      List[Any]      							={
    '''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''',
}
_lowercase							:      int      							=[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def A__  (			lowercase:     int,		lowercase:     Any,		lowercase:     Optional[Any],		lowercase:     List[Any],		lowercase:     Dict					)				->		int:







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

       if weight_type is not None:
              A						:     List[str]         =getattr(lowercase,		lowercase					).shape
       else:
              A						:     Tuple         =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":
              A						:     Any         =value
       elif weight_type == "weight_g":
              A						:     List[str]         =value
       elif weight_type == "weight_v":
              A						:     str         =value
       elif weight_type == "bias":
              A						:     Union[str, Any]         =value
       else:
              A						:     Dict         =value

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



def A__  (			lowercase:     str,		lowercase:     Any					)				->		Any:
       A						:     Dict         =[]
       A						:     int         =fairseq_model.state_dict()

       A						:     Any         =hf_model.feature_extractor
       A						:     Dict         =hf_model.adapter

       for name, value in fairseq_dict.items():
              A						:     Optional[int]         =False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowercase,		lowercase,		lowercase,		lowercase,		hf_model.config.feat_extract_norm == 'group',		)
                     A						:     Union[str, Any]         =True
              elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']					):
                     load_adapter(lowercase,		lowercase,		lowercase,		lowercase					)
                     A						:     Dict         =True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
                                   A						:     Dict         =True
                                   if "*" in mapped_key:
                                          A						:     Union[str, Any]         =name.split(lowercase					)[0].split('.'					)[-2]
                                          A						:     Dict         =mapped_key.replace('*',		lowercase					)
                                   if "weight_g" in name:
                                          A						:     Any         ="""weight_g"""
                                   elif "weight_v" in name:
                                          A						:     int         ="""weight_v"""
                                   elif "bias" in name:
                                          A						:     Any         ="""bias"""
                                   elif "weight" in name:
                                          A						:     int         ="""weight"""
                                   else:
                                          A						:     int         =None
                                   set_recursively(lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)
                            continue
              if not is_used:
                     unused_weights.append(lowercase					)

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



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



def A__  (			lowercase:     Optional[int],		lowercase:     Optional[int],		lowercase:     Tuple,		lowercase:     Tuple					)				->		List[str]:
       A						:     Any         =full_name.split('adaptor.'					)[-1]
       A						:     Optional[Any]         =name.split('.'					)

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



def A__  (			lowercase:     Union[str, Any]					)				->		Optional[Any]:
       A						:     Any         =emb.weight.shape
       A						:     Any         =nn.Linear(lowercase,		lowercase,		bias=lowercase					)
       A						:     Any         =emb.weight.data
       return lin_layer



@torch.no_grad()
def A__  (			lowercase:     List[Any],		lowercase:     Optional[int],		lowercase:     int,		lowercase:     List[Any],		lowercase:     Dict,		lowercase:     int,		lowercase:     Optional[int],		lowercase:     List[Any],		lowercase:     Optional[Any],		lowercase:     Tuple,		lowercase:     int,		)				->		str:
       A						:     Tuple         =WavaVecaConfig.from_pretrained(
           lowercase,		add_adapter=lowercase,		adapter_stride=lowercase,		adapter_kernel_size=lowercase,		use_auth_token=lowercase,		output_hidden_size=lowercase,		)
       A						:     int         =MBartConfig.from_pretrained(lowercase					)

       # load model
       A						:     Optional[int]         =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,
           },		)
       A						:     Optional[Any]         =model[0].eval()

       # load feature extractor
       A						:     int         =WavaVecaFeatureExtractor.from_pretrained(lowercase,		use_auth_token=lowercase					)

       # set weights for wav2vec2 encoder
       A						:     str         =WavaVecaModel(lowercase					)

       recursively_load_weights_wavaveca(model.encoder,		lowercase					)

       # load decoder weights
       A						:     List[str]         =MBartForCausalLM(lowercase					)
       A						:     Dict         =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),		strict=lowercase					)
       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}'					)

       A						:     Optional[Any]         =SpeechEncoderDecoderModel(encoder=lowercase,		decoder=lowercase					)
       A						:     Optional[Any]         =False

       A						:     Optional[int]         =MBartaaTokenizer(lowercase					)
       tokenizer.save_pretrained(lowercase					)

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

       A						:     Optional[int]         =tokenizer.eos_token_id
       A						:     int         =250_004
       A						:     List[str]         =tokenizer.eos_token_id

       A						:     Dict         =SpeechEncoderDecoderConfig.from_dict(lowercase					)

       hf_wavavec.save_pretrained(lowercase					)
       feature_extractor.save_pretrained(lowercase					)


if __name__ == "__main__":
    _lowercase							:      str      							=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=1_0_2_4, type=int, help='''encoder output dim''')
    parser.add_argument('''--start_token_id''', default=2_5_0_0_0_4, type=int, help='''`decoder_start_token_id` of model config''')

    _lowercase							:      Tuple      							=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,
    )







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






