



"""simple docstring"""







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     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """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				(							UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       Optional[Any],	UpperCAmelCase_ :       List[Any],	UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Optional[int]     )	-> Dict:






	"""simple docstring"""





	for attribute in key.split("."     ):
		A__											=					getattr(UpperCAmelCase_,	UpperCAmelCase_     )

	if weight_type is not None:
		A__											=					getattr(UpperCAmelCase_,	UpperCAmelCase_     ).shape
	else:
		A__											=					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":
		A__											=					value
	elif weight_type == "weight_g":
		A__											=					value
	elif weight_type == "weight_v":
		A__											=					value
	elif weight_type == "bias":
		A__											=					value
	else:
		A__											=					value

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

def       _lowerCamelCase				(							UpperCAmelCase_ :       List[Any],	UpperCAmelCase_ :       int,	UpperCAmelCase_ :       Union[str, Any]     )	-> str:






	"""simple docstring"""





	A__											=					[]
	A__											=					fairseq_model.state_dict()

	A__											=					hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		A__											=					False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	hf_model.config.feat_extract_norm == "group",	)
			A__											=					True
		else:
			for key, mapped_key in MAPPING.items():
				A__											=					"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):
					A__											=					True
					if "*" in mapped_key:
						A__											=					name.split(UpperCAmelCase_     )[0].split("."     )[-2]
						A__											=					mapped_key.replace("*",	UpperCAmelCase_     )
					if "weight_g" in name:
						A__											=					"weight_g"
					elif "weight_v" in name:
						A__											=					"weight_v"
					elif "weight" in name:
						A__											=					"weight"
					elif "bias" in name:
						A__											=					"bias"
					else:
						A__											=					None
					set_recursively(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_     )

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

def       _lowerCamelCase				(							UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       Optional[Any],	UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Tuple     )	-> Tuple:






	"""simple docstring"""





	A__											=					full_name.split("conv_layers."     )[-1]
	A__											=					name.split("."     )
	A__											=					int(items[0]     )
	A__											=					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."""
			)
			A__											=					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."""
			)
			A__											=					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."
			)
			A__											=					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."""
			)
			A__											=					value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
	else:
		unused_weights.append(UpperCAmelCase_     )

@torch.no_grad()
def       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Optional[int],	UpperCAmelCase_ :       Any=None,	UpperCAmelCase_ :       List[str]=None,	UpperCAmelCase_ :       Tuple=True     )	-> Optional[int]:






	"""simple docstring"""





	if config_path is not None:
		A__											=					HubertConfig.from_pretrained(UpperCAmelCase_     )
	else:
		A__											=					HubertConfig()

	if is_finetuned:
		if dict_path:
			A__											=					Dictionary.load(UpperCAmelCase_     )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			A__											=					target_dict.pad_index
			A__											=					target_dict.bos_index
			A__											=					target_dict.eos_index
			A__											=					len(target_dict.symbols     )
			A__											=					os.path.join(UpperCAmelCase_,	"vocab.json"     )
			if not os.path.isdir(UpperCAmelCase_     ):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase_     )     )
				return
			os.makedirs(UpperCAmelCase_,	exist_ok=UpperCAmelCase_     )
			with open(UpperCAmelCase_,	"w",	encoding="utf-8"     ) as vocab_handle:
				json.dump(target_dict.indices,	UpperCAmelCase_     )
			A__											=					WavaVecaCTCTokenizer(
			    UpperCAmelCase_,	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=UpperCAmelCase_,	)
			A__											=					True if config.feat_extract_norm == "layer" else False
			A__											=					WavaVecaFeatureExtractor(
			    feature_size=1,	sampling_rate=16000,	padding_value=0,	do_normalize=UpperCAmelCase_,	return_attention_mask=UpperCAmelCase_,	)
			A__											=					WavaVecaProcessor(feature_extractor=UpperCAmelCase_,	tokenizer=UpperCAmelCase_     )
			processor.save_pretrained(UpperCAmelCase_     )

		A__											=					HubertForCTC(UpperCAmelCase_     )
	else:
		A__											=					HubertModel(UpperCAmelCase_     )

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

	A__											=					model[0].eval()

	recursively_load_weights(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )

	hf_wavavec.save_pretrained(UpperCAmelCase_     )


if __name__ == "__main__":
			UpperCamelCase     =							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     =							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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A							:			Optional[Any]         =			{}


try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[int]         =			["MLukeTokenizer"]

if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mluke import MLukeTokenizer


else:
 import sys

 __A							:			List[str]         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





