'''simple docstring'''


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
snake_case__	:     Dict      =    logging.get_logger(__name__)

snake_case__	:     int      =    {
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}


def    _lowerCamelCase       ( lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				str  ):



     """simple docstring"""





     for attribute in key.split('.'  ):
          UpperCAmelCase_				:  str  = getattr(lowercase_      ,	lowercase_  )

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

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


def    _lowerCamelCase       ( lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Any  ):



     """simple docstring"""





     UpperCAmelCase_				:  Dict  = []
     UpperCAmelCase_				:  List[Any]  = fairseq_model.state_dict()

     UpperCAmelCase_				:  Tuple  = hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCAmelCase_				:  Optional[Any]  = False
          if "conv_layers" in name:
               load_conv_layer(
                   lowercase_      ,	lowercase_      ,	lowercase_      ,	lowercase_      ,	hf_model.config.feat_extract_norm == 'group'      ,	)
               UpperCAmelCase_				:  int  = True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_				:  int  = mapped_key

                    if key in name:
                         UpperCAmelCase_				:  List[str]  = True
                         if "*" in mapped_key:
                              UpperCAmelCase_				:  Dict  = name.split(lowercase_  )[0].split('.'  )[-2]
                              UpperCAmelCase_				:  Tuple  = mapped_key.replace('*'      ,	lowercase_  )
                         if "weight_g" in name:
                              UpperCAmelCase_				:  Optional[int]  = 'weight_g'
                         elif "weight_v" in name:
                              UpperCAmelCase_				:  Dict  = 'weight_v'
                         elif "weight" in name:
                              UpperCAmelCase_				:  str  = 'weight'
                         elif "bias" in name:
                              UpperCAmelCase_				:  Union[str, Any]  = 'bias'
                         else:
                              UpperCAmelCase_				:  Tuple  = 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    _lowerCamelCase       ( lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				str      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				Optional[Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = full_name.split('conv_layers.'  )[-1]
     UpperCAmelCase_				:  Any  = name.split('.'  )
     UpperCAmelCase_				:  Any  = 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_				:  Optional[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.'''
               )
               UpperCAmelCase_				:  List[str]  = 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_				:  int  = 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_				:  Optional[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    _lowerCamelCase       ( lowerCamelCase_   :				List[str]  ):



     """simple docstring"""





     UpperCAmelCase_				:  str  = HubertConfig()
     UpperCAmelCase_				:  Tuple  = model.config

     UpperCAmelCase_				:  Union[str, Any]  = fs_config.activation_dropout
     UpperCAmelCase_				:  Any  = False
     UpperCAmelCase_				:  List[Any]  = fs_config.attention_dropout
     UpperCAmelCase_				:  Optional[int]  = False
     UpperCAmelCase_				:  str  = eval(fs_config.extractor_conv_feature_layers  )
     UpperCAmelCase_				:  str  = [x[0] for x in conv_layers]
     UpperCAmelCase_				:  Tuple  = [x[1] for x in conv_layers]
     UpperCAmelCase_				:  List[str]  = [x[2] for x in conv_layers]
     UpperCAmelCase_				:  Optional[int]  = 'gelu'
     UpperCAmelCase_				:  Any  = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     UpperCAmelCase_				:  List[str]  = False
     UpperCAmelCase_				:  Union[str, Any]  = 0.0
     UpperCAmelCase_				:  Tuple  = 0.0
     UpperCAmelCase_				:  Tuple  = fs_config.activation_fn
     UpperCAmelCase_				:  Dict  = fs_config.dropout
     UpperCAmelCase_				:  Union[str, Any]  = fs_config.encoder_embed_dim
     UpperCAmelCase_				:  Union[str, Any]  = 0.02
     UpperCAmelCase_				:  Dict  = fs_config.encoder_ffn_embed_dim
     UpperCAmelCase_				:  int  = 1e-5
     UpperCAmelCase_				:  List[Any]  = 0.0
     UpperCAmelCase_				:  List[Any]  = fs_config.encoder_attention_heads
     UpperCAmelCase_				:  str  = fs_config.conv_pos_groups
     UpperCAmelCase_				:  Optional[Any]  = fs_config.conv_pos
     UpperCAmelCase_				:  int  = len(lowercase_  )
     UpperCAmelCase_				:  Optional[int]  = fs_config.encoder_layers

     return config


@torch.no_grad()
def    _lowerCamelCase       ( lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				Tuple=None  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = distilhubert().model.model

     if config_path is not None:
          UpperCAmelCase_				:  Union[str, Any]  = HubertConfig.from_pretrained(lowercase_  )
     else:
          UpperCAmelCase_				:  Optional[Any]  = convert_config(lowercase_  )
     UpperCAmelCase_				:  Any  = model.eval()

     UpperCAmelCase_				:  Any  = WavaVecaFeatureExtractor(
         feature_size=1      ,	sampling_rate=16000      ,	padding_value=0      ,	do_normalize=lowercase_      ,	return_attention_mask=lowercase_      ,	)
     UpperCAmelCase_				:  Tuple  = HubertModel(lowercase_  )

     recursively_load_weights(lowercase_      ,	lowercase_  )

     feature_extractor.save_pretrained(lowercase_  )
     hf_model.save_pretrained(lowercase_  )


if __name__ == "__main__":
 snake_case__	:     Optional[Any]      =    argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 snake_case__	:     Any      =    parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


    if "model" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''model.'''       ,  ''''''   )
    if "norm1" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm1'''       ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm2'''       ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm'''       ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        __UpperCamelCase			=    orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        __UpperCamelCase			=    orig_key.replace(F"transformer_{layer_num}"       ,  F"encoder.layer.{layer_num}"   )
    if "mha.attn" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha.attn'''       ,  '''attention.self'''   )
    if "mha" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha'''       ,  '''attention'''   )
    if "W_q" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_q'''       ,  '''self.query'''   )
    if "W_k" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_k'''       ,  '''self.key'''   )
    if "W_v" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_v'''       ,  '''self.value'''   )
    if "ff.0" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.0'''       ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.2'''       ,  '''output.dense'''   )
    if "ff" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff'''       ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm.mlm_class'''       ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm'''       ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''backbone.backbone.encoders'''       ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        __UpperCamelCase			=    '''mra.''' + orig_key

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase			=    val

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

    print(model.load_state_dict(lowercase_   )   )
    model.eval()
    model.save_pretrained(lowercase_   )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_ 		=  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






