
"""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()
_a														=		logging.get_logger(__name__)

set_seed(770)


_a														=		{
    """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""",
}


_a														=		{
    """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""",
    },
}

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



def 				lowerCamelCase__		(      __snake_case,					__snake_case=False     )							->		Dict:


					"""simple docstring"""





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



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Any:


					"""simple docstring"""





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



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case=False,					__snake_case="text"     )							->		List[Any]:


					"""simple docstring"""





					if model_type == "text":
										_UpperCamelCase										=  BarkSemanticModel
										_UpperCamelCase										=  BarkSemanticConfig
										_UpperCamelCase										=  BarkSemanticGenerationConfig
					elif model_type == "coarse":
										_UpperCamelCase										=  BarkCoarseModel
										_UpperCamelCase										=  BarkCoarseConfig
										_UpperCamelCase										=  BarkCoarseGenerationConfig
					elif model_type == "fine":
										_UpperCamelCase										=  BarkFineModel
										_UpperCamelCase										=  BarkFineConfig
										_UpperCamelCase										=  BarkFineGenerationConfig
					else:
										raise NotImplementedError()
					_UpperCamelCase										=  F'''{model_type}_small''' if use_small else model_type
					_UpperCamelCase										=  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''']     )
					_UpperCamelCase										=  torch.load(__snake_case,					map_location=__snake_case     )
					# this is a hack
					_UpperCamelCase										=  checkpoint['''model_args''']
					if "input_vocab_size" not in model_args:
										_UpperCamelCase										=  model_args['''vocab_size''']
										_UpperCamelCase										=  model_args['''vocab_size''']
										del model_args["vocab_size"]

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

					_UpperCamelCase										=  ConfigClass(**checkpoint['''model_args''']     )
					_UpperCamelCase										=  ModelClass(config=__snake_case     )
					_UpperCamelCase										=  GenerationConfigClass()

					_UpperCamelCase										=  model_generation_config
					_UpperCamelCase										=  checkpoint['''model''']
					# fixup checkpoint
					_UpperCamelCase										=  '''_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
															_UpperCamelCase										=  k[len(__snake_case     ) :]
															for old_layer_name in new_layer_name_dict:
																				_UpperCamelCase										=  new_k.replace(__snake_case,					new_layer_name_dict[old_layer_name]     )

															_UpperCamelCase										=  state_dict.pop(__snake_case     )

					_UpperCamelCase										=  set(state_dict.keys()     ) - set(model.state_dict().keys()     )
					_UpperCamelCase										=  {k for k in extra_keys if not k.endswith('''.attn.bias'''     )}
					_UpperCamelCase										=  set(model.state_dict().keys()     ) - set(state_dict.keys()     )
					_UpperCamelCase										=  {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     )
					_UpperCamelCase										=  model.num_parameters(exclude_embeddings=__snake_case     )
					_UpperCamelCase										=  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 				lowerCamelCase__		(      __snake_case,					__snake_case=False,					__snake_case="text"     )							->		str:


					"""simple docstring"""





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

					_UpperCamelCase										=  '''cpu'''  # do conversion on cpu

					_UpperCamelCase										=  _get_ckpt_path(__snake_case,					use_small=__snake_case     )
					_UpperCamelCase										=  _load_model(__snake_case,					__snake_case,					model_type=__snake_case,					use_small=__snake_case     )

					# load bark initial model
					_UpperCamelCase										=  _bark_load_model(__snake_case,					'''cpu''',					model_type=__snake_case,					use_small=__snake_case     )

					if model_type == "text":
										_UpperCamelCase										=  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
					_UpperCamelCase										=  5
					_UpperCamelCase										=  10

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

										_UpperCamelCase										=  model(__snake_case     )

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

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

										_UpperCamelCase										=  model(__snake_case,					__snake_case     )
										_UpperCamelCase										=  bark_model(__snake_case,					__snake_case     )

										_UpperCamelCase										=  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 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case,					)							->		Any:


					"""simple docstring"""





					_UpperCamelCase										=  os.path.join(__snake_case,					__snake_case     )

					_UpperCamelCase										=  BarkSemanticConfig.from_pretrained(os.path.join(__snake_case,					'''config.json'''     )     )
					_UpperCamelCase										=  BarkCoarseConfig.from_pretrained(os.path.join(__snake_case,					'''config.json'''     )     )
					_UpperCamelCase										=  BarkFineConfig.from_pretrained(os.path.join(__snake_case,					'''config.json'''     )     )
					_UpperCamelCase										=  EncodecConfig.from_pretrained('''facebook/encodec_24khz'''     )

					_UpperCamelCase										=  BarkSemanticModel.from_pretrained(__snake_case     )
					_UpperCamelCase										=  BarkCoarseModel.from_pretrained(__snake_case     )
					_UpperCamelCase										=  BarkFineModel.from_pretrained(__snake_case     )
					_UpperCamelCase										=  EncodecModel.from_pretrained('''facebook/encodec_24khz'''     )

					_UpperCamelCase										=  BarkConfig.from_sub_model_configs(
					    __snake_case,					__snake_case,					__snake_case,					__snake_case     )

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

					_UpperCamelCase										=  BarkModel(__snake_case     )

					_UpperCamelCase										=  semantic
					_UpperCamelCase										=  coarseAcoustic
					_UpperCamelCase										=  fineAcoustic
					_UpperCamelCase										=  codec

					_UpperCamelCase										=  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__":
				_a														=		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.""")

				_a														=		parser.parse_args()

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








"""simple docstring"""





class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  name
							_UpperCamelCase										=  value
							_UpperCamelCase										=  weight



		def __repr__( self)	->	List[str]:






							'''simple docstring'''
							return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							return self.value



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							return self.name



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.weight




		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							return self.value / self.weight





def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  []
					for i in range(len(__snake_case     )     ):
										menu.append(Things(name[i],					value[i],					weight[i]     )     )
					return menu



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase										=  sorted(__snake_case,					key=__snake_case,					reverse=__snake_case     )
					_UpperCamelCase										=  []
					_UpperCamelCase			,  _UpperCamelCase										=  0.0, 0.0
					for i in range(len(__snake_case     )     ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]     )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def 				lowerCamelCase__		(      )							->		List[str]:


					"""simple docstring"""







if __name__ == "__main__":
				import doctest

				doctest.testmod()






