





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()
UpperCAmelCase__						:			Tuple									=logging.get_logger(__name__)

set_seed(7_70)


UpperCAmelCase__						:			str									={
    '''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''',
}


UpperCAmelCase__						:			List[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''',
    },
}

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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=False				)						->  List[Any]:
				lowerCamelCase	 =model_type
				if use_small:
								key += "_small"
				return os.path.join(_UpperCAmelCase			,      REMOTE_MODEL_PATHS[key]["""file_name"""]				)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				os.makedirs(_UpperCAmelCase			,      exist_ok=_UpperCAmelCase				)
				hf_hub_download(repo_id=_UpperCAmelCase			,      filename=_UpperCAmelCase			,      local_dir=_UpperCAmelCase				)





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

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

				lowerCamelCase	 =ConfigClass(**checkpoint["""model_args"""]				)
				lowerCamelCase	 =ModelClass(config=_UpperCAmelCase				)
				lowerCamelCase	 =GenerationConfigClass()

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

												lowerCamelCase	 =state_dict.pop(_UpperCAmelCase				)

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

				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=False			,      _UpperCAmelCase="text"				)						->  Optional[int]:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				lowerCamelCase	 ="""cpu"""  # do conversion on cpu

				lowerCamelCase	 =_get_ckpt_path(_UpperCAmelCase			,      use_small=_UpperCAmelCase				)
				lowerCamelCase	 =_load_model(_UpperCAmelCase			,      _UpperCAmelCase			,      model_type=_UpperCAmelCase			,      use_small=_UpperCAmelCase				)

				# load bark initial model
				lowerCamelCase	 =_bark_load_model(_UpperCAmelCase			,      """cpu"""			,      model_type=_UpperCAmelCase			,      use_small=_UpperCAmelCase				)

				if model_type == "text":
								lowerCamelCase	 =bark_model["""model"""]

				if model.num_parameters(exclude_embeddings=_UpperCAmelCase				) != 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	 =5
				lowerCamelCase	 =10

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

								lowerCamelCase	 =model(_UpperCAmelCase				)

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

				else:
								lowerCamelCase	 =3
								lowerCamelCase	 =8
								lowerCamelCase	 =torch.randint(2_56			,      (batch_size, sequence_length, n_codes_total)			,      dtype=torch.int				)

								lowerCamelCase	 =model(_UpperCAmelCase			,      _UpperCAmelCase				)
								lowerCamelCase	 =bark_model(_UpperCAmelCase			,      _UpperCAmelCase				)

								lowerCamelCase	 =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(_UpperCAmelCase				).mkdir(exist_ok=_UpperCAmelCase				)
				model.save_pretrained(_UpperCAmelCase				)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      )						->  int:
				lowerCamelCase	 =os.path.join(_UpperCAmelCase			,      _UpperCAmelCase				)

				lowerCamelCase	 =BarkSemanticConfig.from_pretrained(os.path.join(_UpperCAmelCase			,      """config.json"""				)				)
				lowerCamelCase	 =BarkCoarseConfig.from_pretrained(os.path.join(_UpperCAmelCase			,      """config.json"""				)				)
				lowerCamelCase	 =BarkFineConfig.from_pretrained(os.path.join(_UpperCAmelCase			,      """config.json"""				)				)
				lowerCamelCase	 =EncodecConfig.from_pretrained("""facebook/encodec_24khz"""				)

				lowerCamelCase	 =BarkSemanticModel.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =BarkCoarseModel.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =BarkFineModel.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =EncodecModel.from_pretrained("""facebook/encodec_24khz"""				)

				lowerCamelCase	 =BarkConfig.from_sub_model_configs(
				    _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)

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

				lowerCamelCase	 =BarkModel(_UpperCAmelCase				)

				lowerCamelCase	 =semantic
				lowerCamelCase	 =coarseAcoustic
				lowerCamelCase	 =fineAcoustic
				lowerCamelCase	 =codec

				lowerCamelCase	 =bark_generation_config

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


if __name__ == "__main__":
							UpperCAmelCase__						:			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.''')

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

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












import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase__						:			Tuple									=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  int:
				output_path.parent.mkdir(parents=_UpperCAmelCase			,      exist_ok=_UpperCAmelCase				)
				# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
				# so we check the torch version for backwards compatibility
				if is_torch_less_than_1_11:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      use_external_data_format=_UpperCAmelCase			,      enable_onnx_checker=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )
				else:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  Tuple:
				lowerCamelCase	 =torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								lowerCamelCase	 ="""cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
				else:
								lowerCamelCase	 ="""cpu"""
				lowerCamelCase	 =Path(_UpperCAmelCase				)

				# VAE DECODER
				lowerCamelCase	 =AutoencoderKL.from_pretrained(model_path + """/vae"""				)
				lowerCamelCase	 =vae_decoder.config.latent_channels
				# forward only through the decoder part
				lowerCamelCase	 =vae_decoder.decode
				onnx_export(
				    _UpperCAmelCase			,      model_args=(
				        torch.randn(1			,      _UpperCAmelCase			,      25			,      25				).to(device=_UpperCAmelCase			,      dtype=_UpperCAmelCase				),
				        False,
				    )			,      output_path=output_path / """vae_decoder""" / """model.onnx"""			,      ordered_input_names=["""latent_sample""", """return_dict"""]			,      output_names=["""sample"""]			,      dynamic_axes={
				        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
				    }			,      opset=_UpperCAmelCase			,      )
				del vae_decoder


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									=argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_path''',
							    type=str,
							    required=True,
							    help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
							)

							parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
							parser.add_argument(
							    '''--opset''',
							    default=14,
							    type=int,
							    help='''The version of the ONNX operator set to use.''',
							)
							parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

							UpperCAmelCase__						:			Union[str, Any]									=parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print('''SD: Done: ONNX''')





