


'''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()
__UpperCAmelCase					       =							logging.get_logger(__name__)

set_seed(770)


__UpperCAmelCase					       =							{
    "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					       =							{
    "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					       =							os.path.dirname(os.path.abspath(__file__))
__UpperCAmelCase					       =							os.path.join(os.path.expanduser('''~'''), '''.cache''')
__UpperCAmelCase					       =							os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')


def       _snake_case     (  A				,					A=False )		->					int:
							lowerCAmelCase__      =			model_type
							if use_small:
														key += "_small"
							return os.path.join(UpperCAmelCase__				,					REMOTE_MODEL_PATHS[key]['''file_name'''] )


def       _snake_case     (  A				,					A )		->					int:
							os.makedirs(UpperCAmelCase__				,					exist_ok=UpperCAmelCase__ )
							hf_hub_download(repo_id=UpperCAmelCase__				,					filename=UpperCAmelCase__				,					local_dir=UpperCAmelCase__ )


def       _snake_case     (  A				,					A				,					A=False				,					A="text" )		->					str:
							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       _snake_case     (  A				,					A=False				,					A="text" )		->					str:
							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(256				,					(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(256				,					(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       _snake_case     (  A				,					A				,					A				,					A				,					A				,					A				,					)		->					List[str]:
							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					       =							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					       =							parser.parse_args()

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



'''simple docstring'''






def       _snake_case     (  A )		->					bool:
							if not isinstance(A				,					A ):
														raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
							if len(A ) == 0:
														raise ValueError('''Input list must be a non empty list''' )
							if len(A ) == 1:
														return True
							lowerCAmelCase__      =			series[1] - series[0]
							for index in range(len(A ) - 1 ):
														if series[index + 1] - series[index] != common_diff:
																					return False
							return True


def       _snake_case     (  A )		->					float:
							if not isinstance(A				,					A ):
														raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
							if len(A ) == 0:
														raise ValueError('''Input list must be a non empty list''' )
							lowerCAmelCase__      =			0
							for val in series:
														answer += val
							return answer / len(A )


if __name__ == "__main__":
		import doctest

		doctest.testmod()