


'''simple docstring'''




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

set_seed(770)


_lowerCAmelCase  :		Optional[int]		     =	{
    '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',
}


_lowerCAmelCase  :		int		     =	{
    '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',
    },
}

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

def 			__UpperCamelCase    (    _A    :				List[str]					,   _A    :				List[Any]=False				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= model_type
	if use_small:
		key += "_small"
	return os.path.join(_A					,   REMOTE_MODEL_PATHS[key]['file_name']				)

def 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				List[str]				)    ->      List[str]:




	"""simple docstring"""





	os.makedirs(_A					,   exist_ok=_A				)
	hf_hub_download(repo_id=_A					,   filename=_A					,   local_dir=_A				)

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Union[str, Any]					,   _A    :				Any=False					,   _A    :				Union[str, Any]="text"				)    ->      Optional[int]:




	"""simple docstring"""





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

	# convert Bark model arguments to HF Bark model arguments
	lowerCAmelCase					:    Any      							= model_args.pop('n_head'				)
	lowerCAmelCase					:    Tuple      							= model_args.pop('n_embd'				)
	lowerCAmelCase					:    List[str]      							= model_args.pop('n_layer'				)

	lowerCAmelCase					:    Tuple      							= ConfigClass(**checkpoint['model_args']				)
	lowerCAmelCase					:    List[Any]      							= ModelClass(config=_A				)
	lowerCAmelCase					:    Union[str, Any]      							= GenerationConfigClass()

	lowerCAmelCase					:    Dict      							= model_generation_config
	lowerCAmelCase					:    List[Any]      							= checkpoint['model']
	# fixup checkpoint
	lowerCAmelCase					:    Any      							= '_orig_mod.'
	for k, v in list(state_dict.items()				):
		if k.startswith(_A				):
			# replace part of the key with corresponding layer name in HF implementation
			lowerCAmelCase					:    Union[str, Any]      							= k[len(_A				) :]
			for old_layer_name in new_layer_name_dict:
				lowerCAmelCase					:    int      							= new_k.replace(_A					,   new_layer_name_dict[old_layer_name]				)

			lowerCAmelCase					:    Dict      							= state_dict.pop(_A				)

	lowerCAmelCase					:    Any      							= set(state_dict.keys()				) - set(model.state_dict().keys()				)
	lowerCAmelCase					:    str      							= {k for k in extra_keys if not k.endswith('.attn.bias'				)}
	lowerCAmelCase					:    int      							= set(model.state_dict().keys()				) - set(state_dict.keys()				)
	lowerCAmelCase					:    Optional[int]      							= {k for k in missing_keys if not k.endswith('.attn.bias'				)}
	if len(_A				) != 0:
		raise ValueError(F"extra keys found: {extra_keys}"				)
	if len(_A				) != 0:
		raise ValueError(F"missing keys: {missing_keys}"				)
	model.load_state_dict(_A					,   strict=_A				)
	lowerCAmelCase					:    Tuple      							= model.num_parameters(exclude_embeddings=_A				)
	lowerCAmelCase					:    int      							= checkpoint['best_val_loss'].item()
	logger.info(F"model loaded: {round(n_params/1e6					,   1				)}M params, {round(_A					,   3				)} loss"				)
	model.eval()
	model.to(_A				)
	del checkpoint, state_dict

	return model

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				List[str]=False					,   _A    :				Union[str, Any]="text"				)    ->      Optional[Any]:




	"""simple docstring"""





	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	lowerCAmelCase					:    str      							= 'cpu'  # do conversion on cpu

	lowerCAmelCase					:    str      							= _get_ckpt_path(_A					,   use_small=_A				)
	lowerCAmelCase					:    List[Any]      							= _load_model(_A					,   _A					,   model_type=_A					,   use_small=_A				)

	# load bark initial model
	lowerCAmelCase					:    Any      							= _bark_load_model(_A					,   'cpu'					,   model_type=_A					,   use_small=_A				)

	if model_type == "text":
		lowerCAmelCase					:    Optional[Any]      							= bark_model['model']

	if model.num_parameters(exclude_embeddings=_A				) != 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
	lowerCAmelCase					:    Union[str, Any]      							= 5
	lowerCAmelCase					:    Optional[Any]      							= 10

	if model_type in ["text", "coarse"]:
		lowerCAmelCase					:    int      							= torch.randint(2_56					,   (batch_size, sequence_length)					,   dtype=torch.int				)
		lowerCAmelCase					:    Dict      							= bark_model(_A				)[0]

		lowerCAmelCase					:    int      							= model(_A				)

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

	else:
		lowerCAmelCase					:    int      							= 3
		lowerCAmelCase					:    int      							= 8
		lowerCAmelCase					:    List[str]      							= torch.randint(2_56					,   (batch_size, sequence_length, n_codes_total)					,   dtype=torch.int				)

		lowerCAmelCase					:    Optional[Any]      							= model(_A					,   _A				)
		lowerCAmelCase					:    int      							= bark_model(_A					,   _A				)

		lowerCAmelCase					:    Any      							= 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(_A				).mkdir(exist_ok=_A				)
	model.save_pretrained(_A				)

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Union[str, Any]					,   _A    :				Any					,   _A    :				Optional[Any]					,   _A    :				List[str]					,   _A    :				Tuple					,   )    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= os.path.join(_A					,   _A				)

	lowerCAmelCase					:    Dict      							= BarkSemanticConfig.from_pretrained(os.path.join(_A					,   'config.json'				)				)
	lowerCAmelCase					:    int      							= BarkCoarseConfig.from_pretrained(os.path.join(_A					,   'config.json'				)				)
	lowerCAmelCase					:    Tuple      							= BarkFineConfig.from_pretrained(os.path.join(_A					,   'config.json'				)				)
	lowerCAmelCase					:    str      							= EncodecConfig.from_pretrained('facebook/encodec_24khz'				)

	lowerCAmelCase					:    int      							= BarkSemanticModel.from_pretrained(_A				)
	lowerCAmelCase					:    Optional[int]      							= BarkCoarseModel.from_pretrained(_A				)
	lowerCAmelCase					:    str      							= BarkFineModel.from_pretrained(_A				)
	lowerCAmelCase					:    List[str]      							= EncodecModel.from_pretrained('facebook/encodec_24khz'				)

	lowerCAmelCase					:    Optional[Any]      							= BarkConfig.from_sub_model_configs(
	    _A					,   _A					,   _A					,   _A				)

	lowerCAmelCase					:    int      							= BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config					,   coarseAcoustic.generation_config					,   fineAcoustic.generation_config				)

	lowerCAmelCase					:    Dict      							= BarkModel(_A				)

	lowerCAmelCase					:    int      							= semantic
	lowerCAmelCase					:    Union[str, Any]      							= coarseAcoustic
	lowerCAmelCase					:    int      							= fineAcoustic
	lowerCAmelCase					:    Dict      							= codec

	lowerCAmelCase					:    List[Any]      							= bark_generation_config

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


if __name__ == "__main__":
				_lowerCAmelCase  :		List[str]		     =	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.')

				_lowerCAmelCase  :		str		     =	parser.parse_args()

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




'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase  :		List[str]		     =	logging.get_logger('transformers.models.speecht5')

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Any				)    ->      Union[str, Any]:




	"""simple docstring"""





	hf_model.apply_weight_norm()

	lowerCAmelCase					:    int      							= checkpoint['input_conv.weight_g']
	lowerCAmelCase					:    Optional[int]      							= checkpoint['input_conv.weight_v']
	lowerCAmelCase					:    Dict      							= checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates				)				):
		lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"upsamples.{i}.1.weight_g"]
		lowerCAmelCase					:    str      							= checkpoint[F"upsamples.{i}.1.weight_v"]
		lowerCAmelCase					:    str      							= checkpoint[F"upsamples.{i}.1.bias"]

	for i in range(len(config.upsample_rates				) * len(config.resblock_kernel_sizes				)				):
		for j in range(len(config.resblock_dilation_sizes				)				):
			lowerCAmelCase					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
			lowerCAmelCase					:    str      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
			lowerCAmelCase					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

			lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

	lowerCAmelCase					:    List[Any]      							= checkpoint['output_conv.1.weight_g']
	lowerCAmelCase					:    List[str]      							= checkpoint['output_conv.1.weight_v']
	lowerCAmelCase					:    Optional[Any]      							= checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				List[Any]					,   _A    :				Any=None					,   _A    :				Any=None					,   )    ->      Dict:




	"""simple docstring"""





	if config_path is not None:
		lowerCAmelCase					:    Dict      							= SpeechTaHifiGanConfig.from_pretrained(_A				)
	else:
		lowerCAmelCase					:    Union[str, Any]      							= SpeechTaHifiGanConfig()

	lowerCAmelCase					:    List[Any]      							= SpeechTaHifiGan(_A				)

	lowerCAmelCase					:    List[str]      							= torch.load(_A				)
	load_weights(orig_checkpoint['model']['generator']					,   _A					,   _A				)

	lowerCAmelCase					:    Tuple      							= np.load(_A				)
	lowerCAmelCase					:    List[Any]      							= stats[0].reshape(-1				)
	lowerCAmelCase					:    int      							= stats[1].reshape(-1				)
	lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(_A				).float()
	lowerCAmelCase					:    int      							= torch.from_numpy(_A				).float()

	model.save_pretrained(_A				)

	if repo_id:
		print('Pushing to the hub...'				)
		model.push_to_hub(_A				)


if __name__ == "__main__":
				_lowerCAmelCase  :		List[Any]		     =	argparse.ArgumentParser()
				parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
				parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				parser.add_argument(
				    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
				)

				_lowerCAmelCase  :		Union[str, Any]		     =	parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)
