




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

_snake_case   :   str     							=	{
    '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 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						Any				,		UpperCamelCase :						Union[str, Any]   ):





							'''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 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						Dict   ):





							'''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 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						Dict				,		UpperCamelCase :						str   ):





							'''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 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Union[str, Any]=None				,		UpperCamelCase :						List[str]=None				,		UpperCamelCase :						Optional[Any]=True   ):





							'''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=1_6000				,		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__":
				_snake_case   :   str     							=	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'
				)
				_snake_case   :   Dict     							=	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'''


from ..utils import DummyObject, requires_backends

class     A					(	metaclass=_a					):
							lowercase_           =       ['torch', 'torchsde']







							def __init__(						self :						Optional[Any]							,    *lowerCAmelCase_ :						Any							,    **lowerCAmelCase_ :						Tuple      )    ->							Dict:


														"""simple docstring"""


														requires_backends(self							,    ['''torch''', '''torchsde''']      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						Dict							,    *lowerCAmelCase_ :						Optional[int]							,    **lowerCAmelCase_ :						Optional[Any]      )    ->							int:


														"""simple docstring"""


														requires_backends(cls							,    ['''torch''', '''torchsde''']      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						List[str]							,    *lowerCAmelCase_ :						str							,    **lowerCAmelCase_ :						Optional[Any]      )    ->							Any:


														"""simple docstring"""


														requires_backends(cls							,    ['''torch''', '''torchsde''']      )

