



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__snake_case						:List[Any]						=					logging.get_logger(__name__)

__snake_case						: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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
__snake_case						:Tuple						=					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    {}
    with open(_UpperCAmelCase       ,			'''r'''    ) as file:
        for line_number, line in enumerate(_UpperCAmelCase    ):
            __a							=    line.strip()
            if line:
                __a							=    line.split()
                __a							=    line_number
                __a							=    words[0]
                __a							=    value
    return result





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    for attribute in key.split('''.'''    ):
        __a							=    getattr(_UpperCAmelCase       ,			_UpperCAmelCase    )

    __a							=    None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(_UpperCAmelCase    ):
            __a							=    PARAM_MAPPING[full_name.split('''.'''    )[-1]]
            __a							=    'param'

    if weight_type is not None and weight_type != "param":
        __a							=    getattr(_UpperCAmelCase       ,			_UpperCAmelCase    ).shape
    elif weight_type is not None and weight_type == "param":
        __a							=    hf_pointer
        for attribute in hf_param_name.split('''.'''    ):
            __a							=    getattr(_UpperCAmelCase       ,			_UpperCAmelCase    )
        __a							=    shape_pointer.shape

        # let's reduce dimension
        __a							=    value[0]
    else:
        __a							=    hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            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							=    value
    elif weight_type == "weight_g":
        __a							=    value
    elif weight_type == "weight_v":
        __a							=    value
    elif weight_type == "bias":
        __a							=    value
    elif weight_type == "param":
        for attribute in hf_param_name.split('''.'''    ):
            __a							=    getattr(_UpperCAmelCase       ,			_UpperCAmelCase    )
        __a							=    value
    else:
        __a							=    value

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





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(_UpperCAmelCase    ):
            __a							=    PARAM_MAPPING[full_name.split('''.'''    )[-1]]
            __a							=    'param'

    if weight_type is not None and weight_type != "param":
        __a							=    '.'.join([key, weight_type]    )
    elif weight_type is not None and weight_type == "param":
        __a							=    '.'.join([key, hf_param_name]    )
    else:
        __a							=    key

    __a							=    value if 'lm_head' in full_key else value[0]


__snake_case						:List[Any]						=					{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):
    __a							=    False
    for key, mapped_key in MAPPING.items():
        __a							=    'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('''w2v_model.'''    )[-1] == name.split('''.'''    )[0]:
            __a							=    True
            if "*" in mapped_key:
                __a							=    name.split(_UpperCAmelCase    )[0].split('''.'''    )[-2]
                __a							=    mapped_key.replace('''*'''       ,			_UpperCAmelCase    )
            if "weight_g" in name:
                __a							=    'weight_g'
            elif "weight_v" in name:
                __a							=    'weight_v'
            elif "bias" in name:
                __a							=    'bias'
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                __a							=    'weight'
            else:
                __a							=    None
            if hf_dict is not None:
                rename_dict(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
            else:
                set_recursively(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
            return is_used
    return is_used





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    []
    __a							=    fairseq_model.state_dict()

    __a							=    hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        __a							=    False
        if "conv_layers" in name:
            load_conv_layer(
                _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			hf_model.config.feat_extract_norm == '''group'''       ,			)
            __a							=    True
        else:
            __a							=    load_wavaveca_layer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
        if not is_used:
            unused_weights.append(_UpperCAmelCase    )

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





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    full_name.split('''conv_layers.'''    )[-1]
    __a							=    name.split('''.'''    )
    __a							=    int(items[0]    )
    __a							=    int(items[1]    )

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'    )
            __a							=    value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'    )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'    )
            __a							=    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:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'    )
            __a							=    value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'    )
            __a							=    value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
    else:
        unused_weights.append(_UpperCAmelCase    )





@torch.no_grad()
def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=True       ,			_UpperCAmelCase=False    ):
    if config_path is not None:
        __a							=    WavaVecaConfig.from_pretrained(_UpperCAmelCase    )
    else:
        __a							=    WavaVecaConfig()

    if is_seq_class:
        __a							=    read_txt_into_dict(_UpperCAmelCase    )
        __a							=    idalabel
        __a							=    WavaVecaForSequenceClassification(_UpperCAmelCase    )
        __a							=    WavaVecaFeatureExtractor(
            feature_size=1       ,			sampling_rate=16000       ,			padding_value=0       ,			do_normalize=_UpperCAmelCase       ,			return_attention_mask=_UpperCAmelCase       ,			)
        feature_extractor.save_pretrained(_UpperCAmelCase    )

    elif is_finetuned:
        if dict_path:
            __a							=    Dictionary.load(_UpperCAmelCase    )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __a							=    target_dict.pad_index
            __a							=    target_dict.bos_index
            __a							=    target_dict.eos_index
            __a							=    len(target_dict.symbols    )
            __a							=    os.path.join(_UpperCAmelCase       ,			'''vocab.json'''    )
            if not os.path.isdir(_UpperCAmelCase    ):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCAmelCase    )    )
                return
            os.makedirs(_UpperCAmelCase       ,			exist_ok=_UpperCAmelCase    )
            __a							=    target_dict.indices

            # fairseq has the <pad> and <s> switched
            __a							=    0
            __a							=    1
            with open(_UpperCAmelCase       ,			'''w'''       ,			encoding='''utf-8'''    ) as vocab_handle:
                json.dump(_UpperCAmelCase       ,			_UpperCAmelCase    )
            __a							=    WavaVecaCTCTokenizer(
                _UpperCAmelCase       ,			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=_UpperCAmelCase       ,			)
            __a							=    True if config.feat_extract_norm == 'layer' else False
            __a							=    WavaVecaFeatureExtractor(
                feature_size=1       ,			sampling_rate=16000       ,			padding_value=0       ,			do_normalize=_UpperCAmelCase       ,			return_attention_mask=_UpperCAmelCase       ,			)
            __a							=    WavaVecaProcessor(feature_extractor=_UpperCAmelCase       ,			tokenizer=_UpperCAmelCase    )
            processor.save_pretrained(_UpperCAmelCase    )

        __a							=    WavaVecaForCTC(_UpperCAmelCase    )
    else:
        __a							=    WavaVecaForPreTraining(_UpperCAmelCase    )

    if is_finetuned or is_seq_class:
        __a							=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]       ,			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''    )[:-1]    )}    )
    else:
        __a							=    argparse.Namespace(task='''audio_pretraining'''    )
        __a							=    fairseq.tasks.setup_task(_UpperCAmelCase    )

        __a							=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,			task=_UpperCAmelCase    )

    __a							=    model[0].eval()

    recursively_load_weights(_UpperCAmelCase       ,			_UpperCAmelCase       ,			not is_finetuned    )

    hf_wavavec.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
 __snake_case						:List[Any]						=					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'''
 )
 parser.add_argument(
     '''--is_seq_class''',
     action='''store_true''',
     help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
 )
 __snake_case						:Optional[int]						=					parser.parse_args()

 __snake_case						:Optional[int]						=					not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )










def 					__snake_case     (      _UpperCAmelCase    ):
    if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ):
        raise TypeError('''Input value must be an \'int\' type'''    )

    __a							=    0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
 import doctest

 doctest.testmod()





