
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()
__lowerCAmelCase    :	Union[str, Any]						=						logging.get_logger(__name__)

__lowerCAmelCase    :	Tuple						=						{
    "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 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  List[str]:
    for attribute in key.split('''.'''		):
        __lowercase						:	List[str]					=				getattr(__lowerCamelCase		,    __lowerCamelCase		)

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

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


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  Tuple:
    __lowercase						:	str					=				[]
    __lowercase						:	int					=				fairseq_model.state_dict()

    __lowercase						:	int					=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  List[Any]:
    __lowercase						:	Optional[Any]					=				full_name.split('''conv_layers.'''		)[-1]
    __lowercase						:	Tuple					=				name.split('''.'''		)
    __lowercase						:	int					=				int(items[0]		)
    __lowercase						:	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.'
            )
            __lowercase						:	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.'
            )
            __lowercase						:	Optional[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."
            )
            __lowercase						:	Optional[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.'
            )
            __lowercase						:	Any					=				value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
    else:
        unused_weights.append(__lowerCamelCase		)


@torch.no_grad()
def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=True		)       ->  List[Any]:
    if config_path is not None:
        __lowercase						:	Any					=				HubertConfig.from_pretrained(__lowerCamelCase		)
    else:
        __lowercase						:	Dict					=				HubertConfig()

    if is_finetuned:
        if dict_path:
            __lowercase						:	str					=				Dictionary.load(__lowerCamelCase		)

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

        __lowercase						:	List[Any]					=				HubertForCTC(__lowerCamelCase		)
    else:
        __lowercase						:	str					=				HubertModel(__lowerCamelCase		)

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

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

    recursively_load_weights(__lowerCamelCase		,    __lowerCamelCase		,    __lowerCamelCase		)

    hf_wavavec.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 __lowerCAmelCase    :	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(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 __lowerCAmelCase    :	Optional[Any]						=						parser.parse_args()
 convert_hubert_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )




"""simple docstring"""





def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   return round(float(moles / volume  ) * nfactor  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   return round(float((moles * 0.0821 * temperature) / (volume)  )  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   return round(float((moles * 0.0821 * temperature) / (pressure)  )  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   return round(float((pressure * volume) / (0.0821 * moles)  )  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


