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

lowerCAmelCase			:							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",
    "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",
}
lowerCAmelCase			:							Any     =[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 						A__  (					__A   , __A   , __A   , __A   , __A	):







    for attribute in key.split("""."""	):
        _lowerCamelCase     :		str 						=						getattr(__A   , __A	)

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

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







def 						A__  (					__A   , __A	):
    _lowerCamelCase     :		Dict 						=						[]
    _lowerCamelCase     :		int 						=						fairseq_model.state_dict()

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

    for name, value in fairseq_dict.items():
        _lowerCamelCase     :		Optional[int] 						=						False
        if "conv_layers" in name:
            load_conv_layer(
                __A   , __A   , __A   , __A   , hf_model.config.feat_extract_norm == """group"""   , )
            _lowerCamelCase     :		Union[str, Any] 						=						True
        elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]	):
            load_adapter(__A   , __A   , __A   , __A	)
            _lowerCamelCase     :		Dict 						=						True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
                    _lowerCamelCase     :		Dict 						=						True
                    if "*" in mapped_key:
                        _lowerCamelCase     :		Union[str, Any] 						=						name.split(__A	)[0].split("""."""	)[-2]
                        _lowerCamelCase     :		Dict 						=						mapped_key.replace("""*"""   , __A	)
                    if "weight_g" in name:
                        _lowerCamelCase     :		Any 						=						"""weight_g"""
                    elif "weight_v" in name:
                        _lowerCamelCase     :		int 						=						"""weight_v"""
                    elif "bias" in name:
                        _lowerCamelCase     :		Any 						=						"""bias"""
                    elif "weight" in name:
                        _lowerCamelCase     :		int 						=						"""weight"""
                    else:
                        _lowerCamelCase     :		int 						=						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	):
    _lowerCamelCase     :		Union[str, Any] 						=						full_name.split("""conv_layers."""	)[-1]
    _lowerCamelCase     :		Union[str, Any] 						=						name.split("""."""	)
    _lowerCamelCase     :		Union[str, Any] 						=						int(items[0]	)
    _lowerCamelCase     :		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."""
            )
            _lowerCamelCase     :		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."""
            )
            _lowerCamelCase     :		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."
            )
            _lowerCamelCase     :		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."""
            )
            _lowerCamelCase     :		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(__A	)







def 						A__  (					__A   , __A   , __A   , __A	):
    _lowerCamelCase     :		Any 						=						full_name.split("""adaptor."""	)[-1]
    _lowerCamelCase     :		Optional[Any] 						=						name.split("""."""	)

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







def 						A__  (					__A	):
    _lowerCamelCase     :		Any 						=						emb.weight.shape
    _lowerCamelCase     :		Any 						=						nn.Linear(__A   , __A   , bias=__A	)
    _lowerCamelCase     :		Any 						=						emb.weight.data
    return lin_layer







@torch.no_grad()
def 						A__  (					__A   , __A   , __A   , __A   , __A   , __A   , __A   , __A   , __A   , __A   , __A   , ):
    _lowerCamelCase     :		Tuple 						=						WavaVecaConfig.from_pretrained(
        __A   , add_adapter=__A   , adapter_stride=__A   , adapter_kernel_size=__A   , use_auth_token=__A   , output_hidden_size=__A   , )
    _lowerCamelCase     :		int 						=						MBartConfig.from_pretrained(__A	)

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

    # load feature extractor
    _lowerCamelCase     :		int 						=						WavaVecaFeatureExtractor.from_pretrained(__A   , use_auth_token=__A	)

    # set weights for wav2vec2 encoder
    _lowerCamelCase     :		str 						=						WavaVecaModel(__A	)

    recursively_load_weights_wavaveca(model.encoder   , __A	)

    # load decoder weights
    _lowerCamelCase     :		List[str] 						=						MBartForCausalLM(__A	)
    _lowerCamelCase     :		Dict 						=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   , strict=__A	)
    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}"""	)

    _lowerCamelCase     :		Optional[Any] 						=						SpeechEncoderDecoderModel(encoder=__A   , decoder=__A	)
    _lowerCamelCase     :		Optional[Any] 						=						False

    _lowerCamelCase     :		Optional[int] 						=						MBartaaTokenizer(__A	)
    tokenizer.save_pretrained(__A	)

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

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

    _lowerCamelCase     :		Dict 						=						SpeechEncoderDecoderConfig.from_dict(__A	)

    hf_wavavec.save_pretrained(__A	)
    feature_extractor.save_pretrained(__A	)


if __name__ == "__main__":
    lowerCAmelCase			:							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_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=1024, type=int, help="encoder output dim")
    parser.add_argument("--start_token_id", default=250004, type=int, help="`decoder_start_token_id` of model config")

    lowerCAmelCase			:							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,
    )





def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						0
    for ch in input_str:
        _lowerCamelCase     :		Optional[Any] 						=						ord(__A	)
        _lowerCamelCase     :		List[str] 						=						pow(2   , __A	)

        # If we already turned on bit for current character's unicode
        if bitmap >> ch_unicode & 1 == 1:
            return False
        bitmap |= ch_bit_index_on
    return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()




