import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowerCAmelCase			:							Union[str, 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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
lowerCAmelCase			:							Optional[int]     =[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]







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







    '''simple docstring'''
    for attribute in key.split("""."""	):
        _lowerCamelCase     :		Dict 						=						getattr(__A   , __A	)

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

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







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						[]
    _lowerCamelCase     :		Optional[Any] 						=						fairseq_model.state_dict()

    _lowerCamelCase     :		Any 						=						hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        _lowerCamelCase     :		List[Any] 						=						False
        if "conv_layers" in name:
            load_conv_layer(
                __A   , __A   , __A   , __A   , hf_model.config.feat_extract_norm == """group"""   , )
            _lowerCamelCase     :		Any 						=						True
        else:
            for key, mapped_key in MAPPING.items():
                _lowerCamelCase     :		str 						=						"""unispeech_sat.""" + 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]:
                    if "layer_norm_for_extract" in name and (".".join(name.split("""."""	)[:-1]	) != key):
                        # special case since naming is very similar
                        continue
                    _lowerCamelCase     :		Optional[int] 						=						True
                    if "*" in mapped_key:
                        _lowerCamelCase     :		Dict 						=						name.split(__A	)[0].split("""."""	)[-2]
                        _lowerCamelCase     :		List[str] 						=						mapped_key.replace("""*"""   , __A	)
                    if "weight_g" in name:
                        _lowerCamelCase     :		Optional[Any] 						=						"""weight_g"""
                    elif "weight_v" in name:
                        _lowerCamelCase     :		List[Any] 						=						"""weight_v"""
                    elif "bias" in name:
                        _lowerCamelCase     :		List[Any] 						=						"""bias"""
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _lowerCamelCase     :		Optional[Any] 						=						"""weight"""
                    else:
                        _lowerCamelCase     :		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	):







    '''simple docstring'''
    _lowerCamelCase     :		Any 						=						full_name.split("""conv_layers."""	)[-1]
    _lowerCamelCase     :		Dict 						=						name.split("""."""	)
    _lowerCamelCase     :		int 						=						int(items[0]	)
    _lowerCamelCase     :		int 						=						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."""	)
            _lowerCamelCase     :		Union[str, Any] 						=						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."""	)
            _lowerCamelCase     :		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:
            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[layer_id].layer_norm.bias.data.shape} was found."""	)
            _lowerCamelCase     :		Dict 						=						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[layer_id].layer_norm.weight.data.shape} was found."""	)
            _lowerCamelCase     :		List[str] 						=						value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
    else:
        unused_weights.append(__A	)







@torch.no_grad()
def 						A__  (					__A   , __A   , __A=None   , __A=None   , __A=True	):







    '''simple docstring'''
    if config_path is not None:
        _lowerCamelCase     :		int 						=						UniSpeechSatConfig.from_pretrained(__A	)
    else:
        _lowerCamelCase     :		int 						=						UniSpeechSatConfig()

    _lowerCamelCase     :		Tuple 						=						""""""

    if is_finetuned:
        _lowerCamelCase     :		Dict 						=						UniSpeechSatForCTC(__A	)
    else:
        _lowerCamelCase     :		Union[str, Any] 						=						UniSpeechSatForPreTraining(__A	)

    _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Union[str, Any] 						=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]   , arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
    _lowerCamelCase     :		List[Any] 						=						model[0].eval()

    recursively_load_weights(__A   , __A	)

    hf_wavavec.save_pretrained(__A	)


if __name__ == "__main__":
    lowerCAmelCase			:							Tuple     =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"
    )
    lowerCAmelCase			:							Any     =parser.parse_args()
    convert_unispeech_sat_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[int] 						=						ArgumentParser(
        description=(
            """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
        )	)

    # Optional arguments for the launch helper
    parser.add_argument("""--num_cores"""   , type=__A   , default=1   , help="""Number of TPU cores to use (1 or 8)."""	)

    # positional
    parser.add_argument(
        """training_script"""   , type=__A   , help=(
            """The full path to the single TPU training """
            """program/script to be launched in parallel, """
            """followed by all the arguments for the """
            """training script"""
        )   , )

    # rest from the training program
    parser.add_argument("""training_script_args"""   , nargs=__A	)

    return parser.parse_args()







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						parse_args()

    # Import training_script as a module.
    _lowerCamelCase     :		List[Any] 						=						Path(args.training_script	)
    sys.path.append(str(script_fpath.parent.resolve()	)	)
    _lowerCamelCase     :		Optional[Any] 						=						script_fpath.stem
    _lowerCamelCase     :		Dict 						=						importlib.import_module(__A	)

    # Patch sys.argv
    _lowerCamelCase     :		Union[str, Any] 						=						[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

    xmp.spawn(mod._mp_fn   , args=()   , nprocs=args.num_cores	)


if __name__ == "__main__":
    main()




