import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for attribute in key.split('''.'''     ):
        __UpperCamelCase					:List[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

    if weight_type is not None:
        __UpperCamelCase					:Dict        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ).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					:Dict        =		value
    elif weight_type == "weight_g":
        __UpperCamelCase					:Optional[Any]        =		value
    elif weight_type == "weight_v":
        __UpperCamelCase					:str        =		value
    elif weight_type == "bias":
        __UpperCamelCase					:List[Any]        =		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  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		[]
    __UpperCamelCase					:Dict        =		fairseq_model.state_dict()

    __UpperCamelCase					:Optional[Any]        =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase					:List[Any]        =		False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      hf_model.config.feat_extract_norm == '''group'''  ,      )
            __UpperCamelCase					:List[Any]        =		True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase					:Tuple        =		'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                if key in name or (key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0] and not is_finetuned):
                    __UpperCamelCase					:Tuple        =		True
                    if "*" in mapped_key:
                        __UpperCamelCase					:str        =		name.split(SCREAMING_SNAKE_CASE     )[0].split('''.'''     )[-2]
                        __UpperCamelCase					:Union[str, Any]        =		mapped_key.replace('''*'''  ,      SCREAMING_SNAKE_CASE     )
                    if "weight_g" in name:
                        __UpperCamelCase					:List[Any]        =		'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase					:Any        =		'''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase					:Optional[int]        =		'''weight'''
                    elif "bias" in name:
                        __UpperCamelCase					:Union[str, Any]        =		'''bias'''
                    else:
                        __UpperCamelCase					:List[Any]        =		None
                    set_recursively(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
                continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE     )

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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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



@torch.no_grad()
def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=True     ):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase					:List[Any]        =		HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:str        =		HubertConfig()

    if is_finetuned:
        if dict_path:
            __UpperCamelCase					:str        =		Dictionary.load(SCREAMING_SNAKE_CASE     )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase					:List[str]        =		target_dict.pad_index
            __UpperCamelCase					:int        =		target_dict.bos_index
            __UpperCamelCase					:List[str]        =		target_dict.eos_index
            __UpperCamelCase					:Any        =		len(target_dict.symbols     )
            __UpperCamelCase					:str        =		os.path.join(SCREAMING_SNAKE_CASE  ,      '''vocab.json'''     )
            if not os.path.isdir(SCREAMING_SNAKE_CASE     ):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE     )     )
                return
            os.makedirs(SCREAMING_SNAKE_CASE  ,      exist_ok=SCREAMING_SNAKE_CASE     )
            with open(SCREAMING_SNAKE_CASE  ,      '''w'''  ,      encoding='''utf-8'''     ) as vocab_handle:
                json.dump(target_dict.indices  ,      SCREAMING_SNAKE_CASE     )
            __UpperCamelCase					:List[Any]        =		WavaVecaCTCTokenizer(
                SCREAMING_SNAKE_CASE  ,      unk_token=target_dict.unk_word  ,      pad_token=target_dict.pad_word  ,      bos_token=target_dict.bos_word  ,      eos_token=target_dict.eos_word  ,      word_delimiter_token='''|'''  ,      do_lower_case=SCREAMING_SNAKE_CASE  ,      )
            __UpperCamelCase					:Optional[int]        =		True if config.feat_extract_norm == '''layer''' else False
            __UpperCamelCase					:Union[str, Any]        =		WavaVecaFeatureExtractor(
                feature_size=1  ,      sampling_rate=16_000  ,      padding_value=0  ,      do_normalize=SCREAMING_SNAKE_CASE  ,      return_attention_mask=SCREAMING_SNAKE_CASE  ,      )
            __UpperCamelCase					:Optional[Any]        =		WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE  ,      tokenizer=SCREAMING_SNAKE_CASE     )
            processor.save_pretrained(SCREAMING_SNAKE_CASE     )

        __UpperCamelCase					:Tuple        =		HubertForCTC(SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:Optional[Any]        =		HubertModel(SCREAMING_SNAKE_CASE     )

    if is_finetuned:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]  ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
    else:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:str        =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

    __UpperCamelCase					:int        =		model[0].eval()

    recursively_load_weights(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= 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_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
    )
    __lowercase										= parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''encoder.embed_positions._float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		emb.weight.shape
    __UpperCamelCase					:str        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Any        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Tuple        =		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
    __UpperCamelCase					:Dict        =		mam_aaa['''model''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Dict        =		state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __UpperCamelCase					:Dict        =		MaMaaaConfig(
        vocab_size=SCREAMING_SNAKE_CASE  ,      max_position_embeddings=1_024  ,      encoder_layers=args.encoder_layers  ,      decoder_layers=args.decoder_layers  ,      encoder_attention_heads=args.encoder_attention_heads  ,      decoder_attention_heads=args.decoder_attention_heads  ,      encoder_ffn_dim=args.encoder_ffn_embed_dim  ,      decoder_ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.encoder_embed_dim  ,      encoder_layerdrop=args.encoder_layerdrop  ,      decoder_layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''relu'''  ,      )

    __UpperCamelCase					:Tuple        =		state_dict['''decoder.embed_tokens.weight''']
    __UpperCamelCase					:int        =		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.shared     )

    return model


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    __lowercase										= parser.parse_args()
    __lowercase										= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





