
"""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__   :					Any												=				logging.get_logger(__name__)

set_seed(7_7_0)


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

def lowercase_						(     _snake_case     ,_snake_case=False		):
							SCREAMING_SNAKE_CASE__		: Dict     =     model_type
							if use_small:
														key += "_small"
							return os.path.join(_snake_case     ,REMOTE_MODEL_PATHS[key]["""file_name"""]		)

def lowercase_						(     _snake_case     ,_snake_case		):
							os.makedirs(_snake_case     ,exist_ok=_snake_case		)
							hf_hub_download(repo_id=_snake_case     ,filename=_snake_case     ,local_dir=_snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case=False     ,_snake_case="text"		):
							if model_type == "text":
														SCREAMING_SNAKE_CASE__		: int     =     BarkSemanticModel
														SCREAMING_SNAKE_CASE__		: Tuple     =     BarkSemanticConfig
														SCREAMING_SNAKE_CASE__		: str     =     BarkSemanticGenerationConfig
							elif model_type == "coarse":
														SCREAMING_SNAKE_CASE__		: List[Any]     =     BarkCoarseModel
														SCREAMING_SNAKE_CASE__		: Dict     =     BarkCoarseConfig
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     BarkCoarseGenerationConfig
							elif model_type == "fine":
														SCREAMING_SNAKE_CASE__		: int     =     BarkFineModel
														SCREAMING_SNAKE_CASE__		: List[Any]     =     BarkFineConfig
														SCREAMING_SNAKE_CASE__		: str     =     BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     f'''{model_type}_small''' if use_small else model_type
							SCREAMING_SNAKE_CASE__		: str     =     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"""]		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     torch.load(_snake_case     ,map_location=_snake_case		)
							# this is a hack
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     checkpoint["""model_args"""]
							if "input_vocab_size" not in model_args:
														SCREAMING_SNAKE_CASE__		: List[Any]     =     model_args["""vocab_size"""]
														SCREAMING_SNAKE_CASE__		: Dict     =     model_args["""vocab_size"""]
														del model_args["vocab_size"]

							# convert Bark model arguments to HF Bark model arguments
							SCREAMING_SNAKE_CASE__		: Any     =     model_args.pop("""n_head"""		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     model_args.pop("""n_embd"""		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model_args.pop("""n_layer"""		)

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     ConfigClass(**checkpoint["""model_args"""]		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     ModelClass(config=_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[Any]     =     GenerationConfigClass()

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     model_generation_config
							SCREAMING_SNAKE_CASE__		: Dict     =     checkpoint["""model"""]
							# fixup checkpoint
							SCREAMING_SNAKE_CASE__		: Any     =     """_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
																					SCREAMING_SNAKE_CASE__		: str     =     k[len(_snake_case		) :]
																					for old_layer_name in new_layer_name_dict:
																												SCREAMING_SNAKE_CASE__		: Dict     =     new_k.replace(_snake_case     ,new_layer_name_dict[old_layer_name]		)

																					SCREAMING_SNAKE_CASE__		: List[Any]     =     state_dict.pop(_snake_case		)

							SCREAMING_SNAKE_CASE__		: Dict     =     set(state_dict.keys()		) - set(model.state_dict().keys()		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     {k for k in extra_keys if not k.endswith(""".attn.bias"""		)}
							SCREAMING_SNAKE_CASE__		: Any     =     set(model.state_dict().keys()		) - set(state_dict.keys()		)
							SCREAMING_SNAKE_CASE__		: str     =     {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		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model.num_parameters(exclude_embeddings=_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[Any]     =     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 lowercase_						(     _snake_case     ,_snake_case=False     ,_snake_case="text"		):
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     """cpu"""  # do conversion on cpu

							SCREAMING_SNAKE_CASE__		: str     =     _get_ckpt_path(_snake_case     ,use_small=_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[Any]     =     _load_model(_snake_case     ,_snake_case     ,model_type=_snake_case     ,use_small=_snake_case		)

							# load bark initial model
							SCREAMING_SNAKE_CASE__		: int     =     _bark_load_model(_snake_case     ,"""cpu"""     ,model_type=_snake_case     ,use_small=_snake_case		)

							if model_type == "text":
														SCREAMING_SNAKE_CASE__		: Tuple     =     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
							SCREAMING_SNAKE_CASE__		: List[str]     =     5
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     10

							if model_type in ["text", "coarse"]:
														SCREAMING_SNAKE_CASE__		: Tuple     =     torch.randint(256     ,(batch_size, sequence_length)     ,dtype=torch.int		)
														SCREAMING_SNAKE_CASE__		: List[Any]     =     bark_model(_snake_case		)[0]

														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model(_snake_case		)

														# take last logits
														SCREAMING_SNAKE_CASE__		: List[str]     =     output_new_model_total.logits[:, [-1], :]

							else:
														SCREAMING_SNAKE_CASE__		: Tuple     =     3
														SCREAMING_SNAKE_CASE__		: List[str]     =     8
														SCREAMING_SNAKE_CASE__		: Any     =     torch.randint(256     ,(batch_size, sequence_length, n_codes_total)     ,dtype=torch.int		)

														SCREAMING_SNAKE_CASE__		: str     =     model(_snake_case     ,_snake_case		)
														SCREAMING_SNAKE_CASE__		: Tuple     =     bark_model(_snake_case     ,_snake_case		)

														SCREAMING_SNAKE_CASE__		: List[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(_snake_case		).mkdir(exist_ok=_snake_case		)
							model.save_pretrained(_snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,):
							SCREAMING_SNAKE_CASE__		: Dict     =     os.path.join(_snake_case     ,_snake_case		)

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     BarkSemanticConfig.from_pretrained(os.path.join(_snake_case     ,"""config.json"""		)		)
							SCREAMING_SNAKE_CASE__		: List[Any]     =     BarkCoarseConfig.from_pretrained(os.path.join(_snake_case     ,"""config.json"""		)		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     BarkFineConfig.from_pretrained(os.path.join(_snake_case     ,"""config.json"""		)		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     EncodecConfig.from_pretrained("""facebook/encodec_24khz"""		)

							SCREAMING_SNAKE_CASE__		: int     =     BarkSemanticModel.from_pretrained(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[Any]     =     BarkCoarseModel.from_pretrained(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     BarkFineModel.from_pretrained(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Dict     =     EncodecModel.from_pretrained("""facebook/encodec_24khz"""		)

							SCREAMING_SNAKE_CASE__		: Any     =     BarkConfig.from_sub_model_configs(
							    _snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)

							SCREAMING_SNAKE_CASE__		: List[str]     =     BarkGenerationConfig.from_sub_model_configs(
							    semantic.generation_config     ,coarseAcoustic.generation_config     ,fineAcoustic.generation_config		)

							SCREAMING_SNAKE_CASE__		: Any     =     BarkModel(_snake_case		)

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     semantic
							SCREAMING_SNAKE_CASE__		: Tuple     =     coarseAcoustic
							SCREAMING_SNAKE_CASE__		: Tuple     =     fineAcoustic
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     codec

							SCREAMING_SNAKE_CASE__		: List[str]     =     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__":
					UpperCAmelCase__   :					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__   :					Any												=				parser.parse_args()

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






"""simple docstring"""




import baseaa

def lowercase_						(     _snake_case		):
							return baseaa.baaencode(string.encode("""utf-8"""		)		)

def lowercase_						(     _snake_case		):
							return baseaa.baadecode(_snake_case		).decode("""utf-8"""		)


if __name__ == "__main__":
					UpperCAmelCase__   :					Dict												=				'Hello World!'
					UpperCAmelCase__   :					Tuple												=				baseaa_encode(test)
					print(encoded)

					UpperCAmelCase__   :					Tuple												=				baseaa_decode(encoded)
					print(decoded)




