





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

lowercase_		:				Optional[int]				=						{
    '''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       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				for attribute in key.split('.' ):
								_snake_case :			Any  						=   getattr(__lowerCAmelCase	,			__lowerCAmelCase )

				if weight_type is not None:
								_snake_case :			List[str]  						=   getattr(__lowerCAmelCase	,			__lowerCAmelCase ).shape
				else:
								_snake_case :			Dict  						=   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":
								_snake_case :			Tuple  						=   value
				elif weight_type == "weight_g":
								_snake_case :			List[str]  						=   value
				elif weight_type == "weight_v":
								_snake_case :			List[str]  						=   value
				elif weight_type == "bias":
								_snake_case :			int  						=   value
				else:
								_snake_case :			Dict  						=   value

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

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[str]  						=   []
				_snake_case :			Tuple  						=   fairseq_model.state_dict()

				_snake_case :			Any  						=   hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

				if is_finetuned:
								if dict_path:
												_snake_case :			Dict  						=   Dictionary.load(__lowerCAmelCase )

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

								_snake_case :			List[str]  						=   HubertForCTC(__lowerCAmelCase )
				else:
								_snake_case :			Union[str, Any]  						=   HubertModel(__lowerCAmelCase )

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

				_snake_case :			List[Any]  						=   model[0].eval()

				recursively_load_weights(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

				hf_wavavec.save_pretrained(__lowerCAmelCase )


if __name__ == "__main__":
		lowercase_		:				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'''
		)
		lowercase_		:				List[str]				=						parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)













def       A__(		__lowerCAmelCase ):
				assert column_title.isupper()
				_snake_case :			List[Any]  						=   0
				_snake_case :			List[str]  						=   len(__lowerCAmelCase ) - 1
				_snake_case :			Dict  						=   0

				while index >= 0:
								_snake_case :			List[str]  						=   (ord(column_title[index] ) - 64) * pow(26	,			__lowerCAmelCase )
								answer += value
								power += 1
								index -= 1

				return answer


if __name__ == "__main__":
		from doctest import testmod

		testmod()






