






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 : Union[str, Any]						   =       logging.get_logger(__name__)

set_seed(7_70)


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 : 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''',
    },
}

lowerCamelCase : Any						   =       os.path.dirname(os.path.abspath(__file__))
lowerCamelCase : List[str]						   =       os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowerCamelCase : str						   =       os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')
def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						str=False      ):
							__lowercase      :							Optional[int]				=     model_type
							if use_small:
														key += "_small"
							return os.path.join(lowerCAmelCase_       ,       REMOTE_MODEL_PATHS[key]["""file_name"""]      )
def       snake_case_		(     lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						Optional[Any]      ):
							os.makedirs(lowerCAmelCase_       ,       exist_ok=lowerCAmelCase_      )
							hf_hub_download(repo_id=lowerCAmelCase_       ,       filename=lowerCAmelCase_       ,       local_dir=lowerCAmelCase_      )
def       snake_case_		(     lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						Tuple=False       ,       lowerCAmelCase_       :						Any="text"      ):
							if model_type == "text":
														__lowercase      :							Any				=     BarkSemanticModel
														__lowercase      :							Union[str, Any]				=     BarkSemanticConfig
														__lowercase      :							Any				=     BarkSemanticGenerationConfig
							elif model_type == "coarse":
														__lowercase      :							Dict				=     BarkCoarseModel
														__lowercase      :							List[Any]				=     BarkCoarseConfig
														__lowercase      :							Union[str, Any]				=     BarkCoarseGenerationConfig
							elif model_type == "fine":
														__lowercase      :							List[str]				=     BarkFineModel
														__lowercase      :							Tuple				=     BarkFineConfig
														__lowercase      :							Dict				=     BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							__lowercase      :							int				=     F"{model_type}_small" if use_small else model_type
							__lowercase      :							Union[str, Any]				=     REMOTE_MODEL_PATHS[model_key]
							if not os.path.exists(lowerCAmelCase_      ):
														logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."      )
														_download(model_info["""repo_id"""]       ,       model_info["""file_name"""]      )
							__lowercase      :							str				=     torch.load(lowerCAmelCase_       ,       map_location=lowerCAmelCase_      )
							# this is a hack
							__lowercase      :							Union[str, Any]				=     checkpoint["""model_args"""]
							if "input_vocab_size" not in model_args:
														__lowercase      :							List[str]				=     model_args["""vocab_size"""]
														__lowercase      :							Tuple				=     model_args["""vocab_size"""]
														del model_args["vocab_size"]

							# convert Bark model arguments to HF Bark model arguments
							__lowercase      :							List[Any]				=     model_args.pop("""n_head"""      )
							__lowercase      :							str				=     model_args.pop("""n_embd"""      )
							__lowercase      :							int				=     model_args.pop("""n_layer"""      )

							__lowercase      :							List[Any]				=     ConfigClass(**checkpoint["""model_args"""]      )
							__lowercase      :							Optional[int]				=     ModelClass(config=lowerCAmelCase_      )
							__lowercase      :							List[str]				=     GenerationConfigClass()

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

																					__lowercase      :							List[str]				=     state_dict.pop(lowerCAmelCase_      )

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

							return model
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						int=False       ,       lowerCAmelCase_       :						int="text"      ):
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							__lowercase      :							Any				=     """cpu"""  # do conversion on cpu

							__lowercase      :							str				=     _get_ckpt_path(lowerCAmelCase_       ,       use_small=lowerCAmelCase_      )
							__lowercase      :							Optional[Any]				=     _load_model(lowerCAmelCase_       ,       lowerCAmelCase_       ,       model_type=lowerCAmelCase_       ,       use_small=lowerCAmelCase_      )

							# load bark initial model
							__lowercase      :							Optional[int]				=     _bark_load_model(lowerCAmelCase_       ,       """cpu"""       ,       model_type=lowerCAmelCase_       ,       use_small=lowerCAmelCase_      )

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

							if model.num_parameters(exclude_embeddings=lowerCAmelCase_      ) != 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      :							Dict				=     5
							__lowercase      :							Any				=     10

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

														__lowercase      :							Optional[int]				=     model(lowerCAmelCase_      )

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

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

														__lowercase      :							Any				=     model(lowerCAmelCase_       ,       lowerCAmelCase_      )
														__lowercase      :							Dict				=     bark_model(lowerCAmelCase_       ,       lowerCAmelCase_      )

														__lowercase      :							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(lowerCAmelCase_      ).mkdir(exist_ok=lowerCAmelCase_      )
							model.save_pretrained(lowerCAmelCase_      )
def       snake_case_		(     lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						Optional[int]       ,       ):
							__lowercase      :							str				=     os.path.join(lowerCAmelCase_       ,       lowerCAmelCase_      )

							__lowercase      :							Optional[int]				=     BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_       ,       """config.json"""      )      )
							__lowercase      :							Dict				=     BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_       ,       """config.json"""      )      )
							__lowercase      :							Union[str, Any]				=     BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_       ,       """config.json"""      )      )
							__lowercase      :							Dict				=     EncodecConfig.from_pretrained("""facebook/encodec_24khz"""      )

							__lowercase      :							str				=     BarkSemanticModel.from_pretrained(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     BarkCoarseModel.from_pretrained(lowerCAmelCase_      )
							__lowercase      :							Any				=     BarkFineModel.from_pretrained(lowerCAmelCase_      )
							__lowercase      :							List[str]				=     EncodecModel.from_pretrained("""facebook/encodec_24khz"""      )

							__lowercase      :							List[Any]				=     BarkConfig.from_sub_model_configs(
							    lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )

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

							__lowercase      :							List[str]				=     BarkModel(lowerCAmelCase_      )

							__lowercase      :							int				=     semantic
							__lowercase      :							List[Any]				=     coarseAcoustic
							__lowercase      :							Union[str, Any]				=     fineAcoustic
							__lowercase      :							Any				=     codec

							__lowercase      :							str				=     bark_generation_config

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


if __name__ == "__main__":
	lowerCamelCase : Tuple						   =       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 : Dict						   =       parser.parse_args()

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







def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						int      ):
							return int((input_a, input_a).count(0      ) == 0      )
def       snake_case_		(     ):
							assert and_gate(0       ,       0      ) == 0
							assert and_gate(0       ,       1      ) == 0
							assert and_gate(1       ,       0      ) == 0
							assert and_gate(1       ,       1      ) == 1


if __name__ == "__main__":
	test_and_gate()
	print(and_gate(1, 0))
	print(and_gate(0, 0))
	print(and_gate(0, 1))
	print(and_gate(1, 1))