





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      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''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
SCREAMING_SNAKE_CASE_   :      Tuple			=   [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    Optional[int]:
		for attribute in key.split('.'							):
				__lowercase											=     getattr(snake_case						,   snake_case							)

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

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



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[Any]:
		__lowercase											=     []
		__lowercase											=     fairseq_model.state_dict()

		__lowercase											=     hf_model.feature_extractor

		# if encoder has different dim to decoder -> use proj_weight
		__lowercase											=     None

		for name, value in fairseq_dict.items():
				__lowercase											=     False
				if "conv_layers" in name:
						load_conv_layer(
						    snake_case						,   snake_case						,   snake_case						,   snake_case						,   hf_model.config.feat_extract_norm == 'group'						,   )
						__lowercase											=     True
				elif name.split('.'							)[0] == "proj":
						__lowercase											=     fairseq_model.proj
						__lowercase											=     True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
										__lowercase											=     True
										if "*" in mapped_key:
												__lowercase											=     name.split(snake_case							)[0].split('.'							)[-2]
												__lowercase											=     mapped_key.replace('*'						,   snake_case							)
										if "weight_g" in name:
												__lowercase											=     'weight_g'
										elif "weight_v" in name:
												__lowercase											=     'weight_v'
										elif "bias" in name:
												__lowercase											=     'bias'
										elif "weight" in name:
												__lowercase											=     'weight'
										else:
												__lowercase											=     None
										set_recursively(snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case							)
								continue
				if not is_used:
						unused_weights.append(snake_case							)

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

		return proj_weight



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



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Any:
		__lowercase						,      __lowercase											=     emb.weight.shape
		__lowercase											=     nn.Linear(snake_case						,   snake_case						,   bias=snake_case							)
		__lowercase											=     emb.weight.data
		return lin_layer



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    List[Any]:
		with open(snake_case						,   'r'						,   encoding='utf-8'							) as f:
				__lowercase											=     f.readlines()
				__lowercase											=     [line.split(' '							)[0] for line in lines]

		__lowercase											=     len(snake_case							)

		__lowercase											=     {
		    '<s>': 0,
		    '<pad>': 1,
		    '</s>': 2,
		    '<unk>': 3,
		}

		vocab_dict.update(dict(zip(snake_case						,   range(4						,   num_words + 4							)							)							)							)
		return vocab_dict



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   )		->    Any:
		__lowercase											=     WavaVecaConfig.from_pretrained(snake_case							)
		__lowercase											=     SpeechaTextaConfig.from_pretrained(
		    snake_case						,   vocab_size=snake_case						,   decoder_layers=snake_case						,   do_stable_layer_norm=snake_case							)

		__lowercase											=     WavaVecaFeatureExtractor(
		    feature_size=1						,   sampling_rate=16_000						,   padding_value=0						,   do_normalize=snake_case						,   return_attention_mask=snake_case						,   )

		__lowercase						,      __lowercase						,      __lowercase											=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]						,   arg_overrides={'data': '/'.join(dict_path.split('/'							)[:-1]							)}							)
		__lowercase											=     model[0].eval()

		# set weights for wav2vec2 encoder
		__lowercase											=     WavaVecaModel(snake_case							)
		__lowercase											=     recursively_load_weights_wavaveca(model.encoder						,   snake_case							)

		__lowercase											=     SpeechaTextaForCausalLM(snake_case							)
		__lowercase						,      __lowercase											=     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,   strict=snake_case							)

		# set output linear layer
		unexpected_keys.remove('embed_out'							)
		__lowercase											=     nn.Parameter(model.decoder.embed_out.detach()							)

		# layer norm is init to identity matrix so leaving it is fine
		logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"							)
		logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"							)

		__lowercase											=     SpeechEncoderDecoderModel(encoder=snake_case						,   decoder=snake_case							)
		__lowercase											=     False

		# add projection layer
		__lowercase											=     nn.Parameter(projection_layer.weight							)
		__lowercase											=     nn.Parameter(projection_layer.bias							)

		__lowercase											=     create_vocab_dict(snake_case							)

		with open(os.path.join(snake_case						,   'vocab.json'							)						,   'w'							) as fp:
				json.dump(snake_case						,   snake_case							)

		__lowercase											=     SpeechaTextaTokenizer(os.path.join(snake_case						,   'vocab.json'							)							)
		tokenizer.save_pretrained(snake_case							)

		__lowercase											=     hf_wavavec.config.to_dict()
		__lowercase											=     tokenizer.pad_token_id
		__lowercase											=     tokenizer.bos_token_id
		__lowercase											=     tokenizer.eos_token_id
		__lowercase											=     'speech_to_text_2'
		__lowercase											=     'wav2vec2'

		__lowercase											=     SpeechEncoderDecoderConfig.from_dict(snake_case							)

		hf_wavavec.save_pretrained(snake_case							)
		feature_extractor.save_pretrained(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      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(
		    '''--encoder_config_path''',
		    default='''facebook/wav2vec2-large-lv60''',
		    type=str,
		    help='''Path to hf encoder wav2vec2 checkpoint config''',
		)
		parser.add_argument(
		    '''--decoder_config_path''',
		    default='''facebook/s2t-small-mustc-en-fr-st''',
		    type=str,
		    help='''Path to hf decoder s2t checkpoint config''',
		)
		parser.add_argument('''--vocab_size''', default=1_0224, type=int, help='''Vocab size of decoder''')
		parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.dict_path,
		    encoder_config_path=args.encoder_config_path,
		    decoder_config_path=args.decoder_config_path,
		    vocab_size=args.vocab_size,
		    num_decoder_layers=args.num_decoder_layers,
		)











import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

set_seed(770)


SCREAMING_SNAKE_CASE_   :      List[Any]			=   {
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   {
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

SCREAMING_SNAKE_CASE_   :      List[str]			=   os.path.dirname(os.path.abspath(__file__))
SCREAMING_SNAKE_CASE_   :      List[Any]			=   os.path.join(os.path.expanduser('''~'''), '''.cache''')
SCREAMING_SNAKE_CASE_   :      Any			=   os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     model_type
		if use_small:
				key += "_small"
		return os.path.join(snake_case						,   REMOTE_MODEL_PATHS[key]['file_name']							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[str]:
		os.makedirs(snake_case						,   exist_ok=snake_case							)
		hf_hub_download(repo_id=snake_case						,   filename=snake_case						,   local_dir=snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=False						,   snake_case="text"							)		->    List[Any]:
		if model_type == "text":
				__lowercase											=     BarkSemanticModel
				__lowercase											=     BarkSemanticConfig
				__lowercase											=     BarkSemanticGenerationConfig
		elif model_type == "coarse":
				__lowercase											=     BarkCoarseModel
				__lowercase											=     BarkCoarseConfig
				__lowercase											=     BarkCoarseGenerationConfig
		elif model_type == "fine":
				__lowercase											=     BarkFineModel
				__lowercase											=     BarkFineConfig
				__lowercase											=     BarkFineGenerationConfig
		else:
				raise NotImplementedError()
		__lowercase											=     F"{model_type}_small" if use_small else model_type
		__lowercase											=     REMOTE_MODEL_PATHS[model_key]
		if not os.path.exists(snake_case							):
				logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."							)
				_download(model_info['repo_id']						,   model_info['file_name']							)
		__lowercase											=     torch.load(snake_case						,   map_location=snake_case							)
		# this is a hack
		__lowercase											=     checkpoint['model_args']
		if "input_vocab_size" not in model_args:
				__lowercase											=     model_args['vocab_size']
				__lowercase											=     model_args['vocab_size']
				del model_args["vocab_size"]

		# convert Bark model arguments to HF Bark model arguments
		__lowercase											=     model_args.pop('n_head'							)
		__lowercase											=     model_args.pop('n_embd'							)
		__lowercase											=     model_args.pop('n_layer'							)

		__lowercase											=     ConfigClass(**checkpoint['model_args']							)
		__lowercase											=     ModelClass(config=snake_case							)
		__lowercase											=     GenerationConfigClass()

		__lowercase											=     model_generation_config
		__lowercase											=     checkpoint['model']
		# fixup checkpoint
		__lowercase											=     '_orig_mod.'
		for k, v in list(state_dict.items()							):
				if k.startswith(snake_case							):
						# replace part of the key with corresponding layer name in HF implementation
						__lowercase											=     k[len(snake_case							) :]
						for old_layer_name in new_layer_name_dict:
								__lowercase											=     new_k.replace(snake_case						,   new_layer_name_dict[old_layer_name]							)

						__lowercase											=     state_dict.pop(snake_case							)

		__lowercase											=     set(state_dict.keys()							) - set(model.state_dict().keys()							)
		__lowercase											=     {k for k in extra_keys if not k.endswith('.attn.bias'							)}
		__lowercase											=     set(model.state_dict().keys()							) - set(state_dict.keys()							)
		__lowercase											=     {k for k in missing_keys if not k.endswith('.attn.bias'							)}
		if len(snake_case							) != 0:
				raise ValueError(F"extra keys found: {extra_keys}"							)
		if len(snake_case							) != 0:
				raise ValueError(F"missing keys: {missing_keys}"							)
		model.load_state_dict(snake_case						,   strict=snake_case							)
		__lowercase											=     model.num_parameters(exclude_embeddings=snake_case							)
		__lowercase											=     checkpoint['best_val_loss'].item()
		logger.info(F"model loaded: {round(n_params/1E6						,   1							)}M params, {round(snake_case						,   3							)} loss"							)
		model.eval()
		model.to(snake_case							)
		del checkpoint, state_dict

		return model



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=False						,   snake_case="text"							)		->    Tuple:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		__lowercase											=     'cpu'  # do conversion on cpu

		__lowercase											=     _get_ckpt_path(snake_case						,   use_small=snake_case							)
		__lowercase											=     _load_model(snake_case						,   snake_case						,   model_type=snake_case						,   use_small=snake_case							)

		# load bark initial model
		__lowercase											=     _bark_load_model(snake_case						,   'cpu'						,   model_type=snake_case						,   use_small=snake_case							)

		if model_type == "text":
				__lowercase											=     bark_model['model']

		if model.num_parameters(exclude_embeddings=snake_case							) != bark_model.get_num_params():
				raise ValueError('initial and new models don\'t have the same number of parameters'							)

		# check if same output as the bark model
		__lowercase											=     5
		__lowercase											=     10

		if model_type in ["text", "coarse"]:
				__lowercase											=     torch.randint(256						,   (batch_size, sequence_length)						,   dtype=torch.int							)
				__lowercase											=     bark_model(snake_case							)[0]

				__lowercase											=     model(snake_case							)

				# take last logits
				__lowercase											=     output_new_model_total.logits[:, [-1], :]

		else:
				__lowercase											=     3
				__lowercase											=     8
				__lowercase											=     torch.randint(256						,   (batch_size, sequence_length, n_codes_total)						,   dtype=torch.int							)

				__lowercase											=     model(snake_case						,   snake_case							)
				__lowercase											=     bark_model(snake_case						,   snake_case							)

				__lowercase											=     output_new_model_total.logits

		# output difference should come from the difference of self-attention implementation design
		if output_new_model.shape != output_old_model.shape:
				raise ValueError('initial and new outputs don\'t have the same shape'							)
		if (output_new_model - output_old_model).abs().max().item() > 1E-3:
				raise ValueError('initial and new outputs are not equal'							)

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		model.save_pretrained(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   )		->    List[str]:
		__lowercase											=     os.path.join(snake_case						,   snake_case							)

		__lowercase											=     BarkSemanticConfig.from_pretrained(os.path.join(snake_case						,   'config.json'							)							)
		__lowercase											=     BarkCoarseConfig.from_pretrained(os.path.join(snake_case						,   'config.json'							)							)
		__lowercase											=     BarkFineConfig.from_pretrained(os.path.join(snake_case						,   'config.json'							)							)
		__lowercase											=     EncodecConfig.from_pretrained('facebook/encodec_24khz'							)

		__lowercase											=     BarkSemanticModel.from_pretrained(snake_case							)
		__lowercase											=     BarkCoarseModel.from_pretrained(snake_case							)
		__lowercase											=     BarkFineModel.from_pretrained(snake_case							)
		__lowercase											=     EncodecModel.from_pretrained('facebook/encodec_24khz'							)

		__lowercase											=     BarkConfig.from_sub_model_configs(
		    snake_case						,   snake_case						,   snake_case						,   snake_case							)

		__lowercase											=     BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config						,   coarseAcoustic.generation_config						,   fineAcoustic.generation_config							)

		__lowercase											=     BarkModel(snake_case							)

		__lowercase											=     semantic
		__lowercase											=     coarseAcoustic
		__lowercase											=     fineAcoustic
		__lowercase											=     codec

		__lowercase											=     bark_generation_config

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		bark.save_pretrained(snake_case						,   repo_id=snake_case						,   push_to_hub=snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   argparse.ArgumentParser()
		# Required parameters

		parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   parser.parse_args()

		load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)




