




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()
UpperCAmelCase_		:				str									=     logging.get_logger(__name__)

UpperCAmelCase_		:				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",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				for attribute in key.split("""."""   ):
								__magic_name__	:	List[str]								=getattr(lowerCamelCase	,     lowerCamelCase   )

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

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




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Any								=[]
				__magic_name__	:	Optional[int]								=fairseq_model.state_dict()

				__magic_name__	:	Optional[Any]								=hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								__magic_name__	:	int								=False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     hf_model.config.feat_extract_norm == """group"""	,     )
												__magic_name__	:	Optional[int]								=True
								else:
												for key, mapped_key in MAPPING.items():
																__magic_name__	:	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):
																				__magic_name__	:	Optional[int]								=True
																				if "*" in mapped_key:
																								__magic_name__	:	Any								=name.split(lowerCamelCase   )[0].split("""."""   )[-2]
																								__magic_name__	:	Optional[int]								=mapped_key.replace("""*"""	,     lowerCamelCase   )
																				if "weight_g" in name:
																								__magic_name__	:	Optional[int]								="""weight_g"""
																				elif "weight_v" in name:
																								__magic_name__	:	int								="""weight_v"""
																				elif "weight" in name:
																								__magic_name__	:	Optional[Any]								="""weight"""
																				elif "bias" in name:
																								__magic_name__	:	List[Any]								="""bias"""
																				else:
																								__magic_name__	:	Union[str, Any]								=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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Any								=full_name.split("""conv_layers."""   )[-1]
				__magic_name__	:	Any								=name.split("""."""   )
				__magic_name__	:	Any								=int(items[0]   )
				__magic_name__	:	Union[str, 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."
												)
												__magic_name__	:	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."
												)
												__magic_name__	:	Union[str, 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."
												)
												__magic_name__	:	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."
												)
												__magic_name__	:	Dict								=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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=None	,     lowerCamelCase=None	,     lowerCamelCase=True   ):
				if config_path is not None:
								__magic_name__	:	List[Any]								=HubertConfig.from_pretrained(lowerCamelCase   )
				else:
								__magic_name__	:	int								=HubertConfig()

				if is_finetuned:
								if dict_path:
												__magic_name__	:	List[str]								=Dictionary.load(lowerCamelCase   )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												__magic_name__	:	Union[str, Any]								=target_dict.pad_index
												__magic_name__	:	int								=target_dict.bos_index
												__magic_name__	:	Dict								=target_dict.eos_index
												__magic_name__	:	str								=len(target_dict.symbols   )
												__magic_name__	:	List[Any]								=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   )
												__magic_name__	:	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	,     )
												__magic_name__	:	List[Any]								=True if config.feat_extract_norm == """layer""" else False
												__magic_name__	:	Optional[Any]								=WavaVecaFeatureExtractor(
												    feature_size=1	,     sampling_rate=16000	,     padding_value=0	,     do_normalize=lowerCamelCase	,     return_attention_mask=lowerCamelCase	,     )
												__magic_name__	:	Optional[Any]								=WavaVecaProcessor(feature_extractor=lowerCamelCase	,     tokenizer=lowerCamelCase   )
												processor.save_pretrained(lowerCamelCase   )

								__magic_name__	:	Tuple								=HubertForCTC(lowerCamelCase   )
				else:
								__magic_name__	:	Union[str, Any]								=HubertModel(lowerCamelCase   )

				if is_finetuned:
								__magic_name__	:	Dict								=fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]	,     arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
				else:
								__magic_name__	:	Any								=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

				__magic_name__	:	str								=model[0].eval()

				recursively_load_weights(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )

				hf_wavavec.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
							UpperCAmelCase_		:				Dict									=     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"
							)
							UpperCAmelCase_		:				Union[str, 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
							)










from math import pow, sqrt




def 		lowerCAmelCase_   (	*lowerCamelCase   ):
				__magic_name__	:	Tuple								=len(lowerCamelCase   ) > 0 and all(value > 0.0 for value in values   )
				return result




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(sqrt(molar_mass_a / molar_mass_a   )	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase   )
				    else ValueError("""Input Error: Molar mass values must greater than 0."""   )
				)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a   )	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				    else ValueError(
				        """Input Error: Molar mass and effusion rate values must greater than 0."""   )
				)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a   )	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				    else ValueError(
				        """Input Error: Molar mass and effusion rate values must greater than 0."""   )
				)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(molar_mass / pow(effusion_rate_a / effusion_rate_a	,     2   )	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				    else ValueError(
				        """Input Error: Molar mass and effusion rate values must greater than 0."""   )
				)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(pow(effusion_rate_a / effusion_rate_a	,     2   ) / molar_mass	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				    else ValueError(
				        """Input Error: Molar mass and effusion rate values must greater than 0."""   )
				)




