



'''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()
lowerCAmelCase	:		List[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   __lowerCAmelCase					(							lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						Optional[Any]					):






	'''simple docstring'''




	for attribute in key.split("."					):
		__lowerCAmelCase												=  getattr(lowerCamelCase				,		lowerCamelCase					)

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

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



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						str				,		lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  []
	__lowerCAmelCase												=  fairseq_model.state_dict()

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

	for name, value in fairseq_dict.items():
		__lowerCAmelCase												=  False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		hf_model.config.feat_extract_norm == "group"				,		)
			__lowerCAmelCase												=  True
		else:
			for key, mapped_key in MAPPING.items():
				__lowerCAmelCase												=  "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):
					__lowerCAmelCase												=  True
					if "*" in mapped_key:
						__lowerCAmelCase												=  name.split(lowerCamelCase					)[0].split("."					)[-2]
						__lowerCAmelCase												=  mapped_key.replace("*"				,		lowerCamelCase					)
					if "weight_g" in name:
						__lowerCAmelCase												=  "weight_g"
					elif "weight_v" in name:
						__lowerCAmelCase												=  "weight_v"
					elif "weight" in name:
						__lowerCAmelCase												=  "weight"
					elif "bias" in name:
						__lowerCAmelCase												=  "bias"
					else:
						__lowerCAmelCase												=  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   :						Dict				,		lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Tuple				,		lowerCamelCase   :						Dict				,		lowerCamelCase   :						str					):






	'''simple docstring'''




	__lowerCAmelCase												=  full_name.split("conv_layers."					)[-1]
	__lowerCAmelCase												=  name.split("."					)
	__lowerCAmelCase												=  int(items[0]					)
	__lowerCAmelCase												=  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												=  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												=  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												=  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												=  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   :						Optional[int]				,		lowerCamelCase   :						str				,		lowerCamelCase   :						int=None				,		lowerCamelCase   :						str=None				,		lowerCamelCase   :						Any=True					):






	'''simple docstring'''




	if config_path is not None:
		__lowerCAmelCase												=  HubertConfig.from_pretrained(lowerCamelCase					)
	else:
		__lowerCAmelCase												=  HubertConfig()

	if is_finetuned:
		if dict_path:
			__lowerCAmelCase												=  Dictionary.load(lowerCamelCase					)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__lowerCAmelCase												=  target_dict.pad_index
			__lowerCAmelCase												=  target_dict.bos_index
			__lowerCAmelCase												=  target_dict.eos_index
			__lowerCAmelCase												=  len(target_dict.symbols					)
			__lowerCAmelCase												=  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					)
			__lowerCAmelCase												=  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				,		)
			__lowerCAmelCase												=  True if config.feat_extract_norm == "layer" else False
			__lowerCAmelCase												=  WavaVecaFeatureExtractor(
			    feature_size=1				,		sampling_rate=1_60_00				,		padding_value=0				,		do_normalize=lowerCamelCase				,		return_attention_mask=lowerCamelCase				,		)
			__lowerCAmelCase												=  WavaVecaProcessor(feature_extractor=lowerCamelCase				,		tokenizer=lowerCamelCase					)
			processor.save_pretrained(lowerCamelCase					)

		__lowerCAmelCase												=  HubertForCTC(lowerCamelCase					)
	else:
		__lowerCAmelCase												=  HubertModel(lowerCamelCase					)

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

	__lowerCAmelCase												=  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	:		int				=   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'''



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


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

lowerCAmelCase	:		Dict[Optional[str], Type[Formatter]]				=   {}
lowerCAmelCase	:		Dict[Optional[str], str]				=   {}
lowerCAmelCase	:		Dict[Optional[str], Exception]				=   {}



def   __lowerCAmelCase					(							lowerCamelCase   :						type				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''					)
	__lowerCAmelCase												=  formatter_cls
	for alias in set(aliases + [format_type]					):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''					)
		__lowerCAmelCase												=  format_type



def   __lowerCAmelCase					(							lowerCamelCase   :						Exception				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None					):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	for alias in set(aliases + [format_type]					):
		__lowerCAmelCase												=  unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	lowerCAmelCase	:		Optional[int]				=   ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	lowerCAmelCase	:		str				=   ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	lowerCAmelCase	:		Any				=   ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]					):






	'''simple docstring'''




	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type




def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]				,		**lowerCamelCase   :						Tuple					):






	'''simple docstring'''




	__lowerCAmelCase												=  get_format_type_from_alias(lowerCamelCase					)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**lowerCamelCase					)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None					)}, but got \'{format_type}\''''					)