






"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_   : Optional[Any]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Optional[int]   			= {
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}



def 					_A			(__a    ,				__a    ,				__a    ,				__a    ,				__a							) ->       Union[str, Any]:
    """simple docstring"""






    for attribute in key.split('''.'''							):
        SCREAMING_SNAKE_CASE_      :  str    						=		getattr(__a    ,				__a							)

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

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



def 					_A			(__a    ,				__a    ,				__a							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		[]
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		fairseq_model.state_dict()

    SCREAMING_SNAKE_CASE_      :  Any    						=		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        SCREAMING_SNAKE_CASE_      :  Dict    						=		False
        if "conv_layers" in name:
            load_conv_layer(
                __a    ,				__a    ,				__a    ,				__a    ,				hf_model.config.feat_extract_norm == '''group'''    ,				)
            SCREAMING_SNAKE_CASE_      :  Any    						=		True
        else:
            for key, mapped_key in MAPPING.items():
                SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		'''sew.''' + 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]:
                    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		True
                    if "*" in mapped_key:
                        SCREAMING_SNAKE_CASE_      :  int    						=		name.split(__a							)[0].split('''.'''							)[-2]
                        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		mapped_key.replace('''*'''    ,				__a							)
                    if "weight_g" in name:
                        SCREAMING_SNAKE_CASE_      :  Dict    						=		'''weight_g'''
                    elif "weight_v" in name:
                        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		'''weight_v'''
                    elif "weight" in name:
                        SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''weight'''
                    elif "bias" in name:
                        SCREAMING_SNAKE_CASE_      :  Dict    						=		'''bias'''
                    else:
                        SCREAMING_SNAKE_CASE_      :  List[str]    						=		None
                    set_recursively(__a    ,				__a    ,				__a    ,				__a    ,				__a							)
                continue
        if not is_used:
            unused_weights.append(__a							)

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



def 					_A			(__a    ,				__a    ,				__a    ,				__a    ,				__a							) ->       str:
    """simple docstring"""






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



def 					_A			(__a    ,				__a							) ->       Any:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		SEWConfig()
    if is_finetuned:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model.wav_encoder.wav_model.cfg
    else:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model.cfg

    SCREAMING_SNAKE_CASE_      :  Tuple    						=		fs_config.conv_bias
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		eval(fs_config.conv_feature_layers							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		[x[0] for x in conv_layers]
    SCREAMING_SNAKE_CASE_      :  int    						=		[x[1] for x in conv_layers]
    SCREAMING_SNAKE_CASE_      :  int    						=		[x[2] for x in conv_layers]
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''gelu'''
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		0.0
    SCREAMING_SNAKE_CASE_      :  Dict    						=		fs_config.activation_fn.name
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		fs_config.encoder_embed_dim
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		0.02
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		fs_config.encoder_ffn_embed_dim
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		1e-5
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		fs_config.encoder_layerdrop
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		fs_config.encoder_attention_heads
    SCREAMING_SNAKE_CASE_      :  Any    						=		fs_config.conv_pos_groups
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		fs_config.conv_pos
    SCREAMING_SNAKE_CASE_      :  Dict    						=		len(__a							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		fs_config.encoder_layers
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model.cfg
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		fs_config.final_dropout
        SCREAMING_SNAKE_CASE_      :  Dict    						=		fs_config.layerdrop
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		fs_config.activation_dropout
    SCREAMING_SNAKE_CASE_      :  str    						=		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    SCREAMING_SNAKE_CASE_      :  int    						=		fs_config.attention_dropout
    SCREAMING_SNAKE_CASE_      :  Dict    						=		fs_config.dropout_input
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		fs_config.dropout
    SCREAMING_SNAKE_CASE_      :  Any    						=		fs_config.mask_channel_length
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		fs_config.mask_channel_prob
    SCREAMING_SNAKE_CASE_      :  str    						=		fs_config.mask_length
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		fs_config.mask_prob

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		'''Wav2Vec2FeatureExtractor'''
    SCREAMING_SNAKE_CASE_      :  Any    						=		'''Wav2Vec2CTCTokenizer'''

    return config



@torch.no_grad()
def 					_A			(__a    ,				__a    ,				__a=None    ,				__a=None    ,				__a=True							) ->       str:
    """simple docstring"""






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

    if config_path is not None:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		SEWConfig.from_pretrained(__a							)
    else:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		convert_config(model[0]    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model[0].eval()

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		True if config.feat_extract_norm == '''layer''' else False
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		WavaVecaFeatureExtractor(
        feature_size=1    ,				sampling_rate=1_60_00    ,				padding_value=0    ,				do_normalize=__a    ,				return_attention_mask=__a    ,				)

    if is_finetuned:
        if dict_path:
            SCREAMING_SNAKE_CASE_      :  str    						=		Dictionary.load(__a							)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		target_dict.pad_index
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		target_dict.bos_index
            SCREAMING_SNAKE_CASE_      :  Any    						=		target_dict.pad_index
            SCREAMING_SNAKE_CASE_      :  Dict    						=		target_dict.bos_index
            SCREAMING_SNAKE_CASE_      :  str    						=		target_dict.eos_index
            SCREAMING_SNAKE_CASE_      :  str    						=		len(target_dict.symbols							)
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		os.path.join(__a    ,				'''vocab.json'''							)
            if not os.path.isdir(__a							):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__a							)							)
                return
            os.makedirs(__a    ,				exist_ok=__a							)
            with open(__a    ,				'''w'''    ,				encoding='''utf-8'''							) as vocab_handle:
                json.dump(target_dict.indices    ,				__a							)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		WavaVecaCTCTokenizer(
                __a    ,				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=__a    ,				)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		WavaVecaProcessor(feature_extractor=__a    ,				tokenizer=__a							)
            processor.save_pretrained(__a							)

        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		SEWForCTC(__a							)
    else:
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		SEWModel(__a							)
        feature_extractor.save_pretrained(__a							)

    recursively_load_weights(__a    ,				__a    ,				__a							)

    hf_model.save_pretrained(__a							)


if __name__ == "__main__":
  UpperCAmelCase_   : int   			= 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(
      """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
  )
  UpperCAmelCase_   : int   			= parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )










"""simple docstring"""






from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)


