

'''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 							_lowerCAmelCase (      __magic_name__	:    List[Any]      ,   __magic_name__	:    str=False	)							->     Any:
						lowercase   :       List[str]								=model_type
						if use_small:
												key += "_small"
						return os.path.join(_lowerCamelCase      ,   REMOTE_MODEL_PATHS[key]['''file_name''']	)






def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]      ,   __magic_name__	:    Union[str, Any]	)							->     Optional[Any]:
						os.makedirs(_lowerCamelCase      ,   exist_ok=_lowerCamelCase	)
						hf_hub_download(repo_id=_lowerCamelCase      ,   filename=_lowerCamelCase      ,   local_dir=_lowerCamelCase	)






def 							_lowerCAmelCase (      __magic_name__	:    Any      ,   __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    List[str]=False      ,   __magic_name__	:    List[str]="text"	)							->     int:
						if model_type == "text":
												lowercase   :       Any								=BarkSemanticModel
												lowercase   :       Dict								=BarkSemanticConfig
												lowercase   :       Tuple								=BarkSemanticGenerationConfig
						elif model_type == "coarse":
												lowercase   :       Optional[int]								=BarkCoarseModel
												lowercase   :       Union[str, Any]								=BarkCoarseConfig
												lowercase   :       Optional[Any]								=BarkCoarseGenerationConfig
						elif model_type == "fine":
												lowercase   :       List[str]								=BarkFineModel
												lowercase   :       Optional[Any]								=BarkFineConfig
												lowercase   :       Any								=BarkFineGenerationConfig
						else:
												raise NotImplementedError()
						lowercase   :       List[Any]								=f'''{model_type}_small''' if use_small else model_type
						lowercase   :       Optional[int]								=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   :       Optional[Any]								=torch.load(_lowerCamelCase      ,   map_location=_lowerCamelCase	)
						# this is a hack
						lowercase   :       Any								=checkpoint["model_args"]
						if "input_vocab_size" not in model_args:
												lowercase   :       Union[str, Any]								=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   :       Optional[Any]								=model_args.pop('''n_head'''	)
						lowercase   :       Optional[Any]								=model_args.pop('''n_embd'''	)
						lowercase   :       Any								=model_args.pop('''n_layer'''	)

						lowercase   :       int								=ConfigClass(**checkpoint['''model_args''']	)
						lowercase   :       Optional[Any]								=ModelClass(config=_lowerCamelCase	)
						lowercase   :       int								=GenerationConfigClass()

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

																		lowercase   :       Optional[int]								=state_dict.pop(_lowerCamelCase	)

						lowercase   :       Tuple								=set(state_dict.keys()	) - set(model.state_dict().keys()	)
						lowercase   :       str								={k for k in extra_keys if not k.endswith('''.attn.bias'''	)}
						lowercase   :       str								=set(model.state_dict().keys()	) - set(state_dict.keys()	)
						lowercase   :       str								={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   :       List[str]								=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 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    str=False      ,   __magic_name__	:    Any="text"	)							->     List[Any]:
						if model_type not in ("text", "coarse", "fine"):
												raise NotImplementedError()

						lowercase   :       Dict								="cpu"  # do conversion on cpu

						lowercase   :       Tuple								=_get_ckpt_path(_lowerCamelCase      ,   use_small=_lowerCamelCase	)
						lowercase   :       str								=_load_model(_lowerCamelCase      ,   _lowerCamelCase      ,   model_type=_lowerCamelCase      ,   use_small=_lowerCamelCase	)

						# load bark initial model
						lowercase   :       Optional[Any]								=_bark_load_model(_lowerCamelCase      ,   '''cpu'''      ,   model_type=_lowerCamelCase      ,   use_small=_lowerCamelCase	)

						if model_type == "text":
												lowercase   :       Tuple								=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   :       Union[str, Any]								=5
						lowercase   :       Tuple								=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   :       Dict								=model(_lowerCamelCase	)

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

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

												lowercase   :       Optional[Any]								=model(_lowerCamelCase      ,   _lowerCamelCase	)
												lowercase   :       Optional[int]								=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 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    Optional[Any]      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    List[str]      ,   __magic_name__	:    int      ,   __magic_name__	:    Dict      ,   )							->     Dict:
						lowercase   :       str								=os.path.join(_lowerCamelCase      ,   _lowerCamelCase	)

						lowercase   :       List[Any]								=BarkSemanticConfig.from_pretrained(os.path.join(_lowerCamelCase      ,   '''config.json'''	)	)
						lowercase   :       Dict								=BarkCoarseConfig.from_pretrained(os.path.join(_lowerCamelCase      ,   '''config.json'''	)	)
						lowercase   :       List[Any]								=BarkFineConfig.from_pretrained(os.path.join(_lowerCamelCase      ,   '''config.json'''	)	)
						lowercase   :       Dict								=EncodecConfig.from_pretrained('''facebook/encodec_24khz'''	)

						lowercase   :       int								=BarkSemanticModel.from_pretrained(_lowerCamelCase	)
						lowercase   :       Tuple								=BarkCoarseModel.from_pretrained(_lowerCamelCase	)
						lowercase   :       Optional[Any]								=BarkFineModel.from_pretrained(_lowerCamelCase	)
						lowercase   :       Optional[int]								=EncodecModel.from_pretrained('''facebook/encodec_24khz'''	)

						lowercase   :       int								=BarkConfig.from_sub_model_configs(
						    _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase	)

						lowercase   :       Tuple								=BarkGenerationConfig.from_sub_model_configs(
						    semantic.generation_config      ,   coarseAcoustic.generation_config      ,   fineAcoustic.generation_config	)

						lowercase   :       Union[str, Any]								=BarkModel(_lowerCamelCase	)

						lowercase   :       Any								=semantic
						lowercase   :       Any								=coarseAcoustic
						lowercase   :       Any								=fineAcoustic
						lowercase   :       Tuple								=codec

						lowercase   :       Any								=bark_generation_config

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


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'''





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
							from ... import PreTrainedTokenizerBase, TensorType

UpperCamelCase_							=					logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						'vision-encoder-decoder'
				lowerCamelCase_							=						True







				def __init__(					self    :		Optional[int]		,				**UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__      )
										if "encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError(
																    F'''A configuraton of type {self.model_type} cannot be instantiated because '''
																    F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''      )

										lowercase   :       Optional[Any]								=kwargs.pop('''encoder'''      )
										lowercase   :       List[Any]								=encoder_config.pop('''model_type'''      )
										lowercase   :       List[str]								=kwargs.pop('''decoder'''      )
										lowercase   :       Dict								=decoder_config.pop('''model_type'''      )

										lowercase   :       Union[str, Any]								=AutoConfig.for_model(UpperCAmelCase__		,				**UpperCAmelCase__      )
										lowercase   :       List[str]								=AutoConfig.for_model(UpperCAmelCase__		,				**UpperCAmelCase__      )
										lowercase   :       str								=True







				@classmethod
				def    lowerCamelCase_  (					cls    :		List[str]		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		PretrainedConfig		,				**UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''      )
										lowercase   :       int								=True
										lowercase   :       Optional[Any]								=True

										return cls(encoder=encoder_config.to_dict()		,				decoder=decoder_config.to_dict()		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       int								=copy.deepcopy(self.__dict__      )
										lowercase   :       Union[str, Any]								=self.encoder.to_dict()
										lowercase   :       Union[str, Any]								=self.decoder.to_dict()
										lowercase   :       int								=self.__class__.model_type
										return output





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						version.parse('1.11'			)







				@property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]      )







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										return 1E-4







				@property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}      )





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@property
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       List[str]								=OrderedDict()
										lowercase   :       Tuple								={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										lowercase   :       Optional[int]								={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										lowercase   :       int								={0: '''batch''', 1: '''encoder_sequence'''}

										return common_inputs







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		"PreTrainedTokenizerBase"		,				UpperCAmelCase__    :		int = -1		,				UpperCAmelCase__    :		int = -1		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		Optional["TensorType"] = None		,				):
										'''simple docstring'''
										import torch

										lowercase   :       Optional[Any]								=OrderedDict()

										lowercase   :       List[Any]								=super().generate_dummy_inputs(
										    UpperCAmelCase__		,				batch_size=UpperCAmelCase__		,				seq_length=UpperCAmelCase__		,				is_pair=UpperCAmelCase__		,				framework=UpperCAmelCase__      )

										lowercase		,  lowercase   :       Optional[int]								=dummy_input['''input_ids'''].shape
										lowercase   :       Union[str, Any]								=(batch, encoder_sequence, self._config.encoder_hidden_size)
										lowercase   :       List[str]								=dummy_input.pop('''input_ids'''      )
										lowercase   :       Tuple								=dummy_input.pop('''attention_mask'''      )
										lowercase   :       Union[str, Any]								=torch.zeros(UpperCAmelCase__      )

										return common_inputs





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@property
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										pass







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		PretrainedConfig      ):
										'''simple docstring'''
										return VisionEncoderDecoderEncoderOnnxConfig(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		str = "default"      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=encoder_config.hidden_size
										return VisionEncoderDecoderDecoderOnnxConfig(UpperCAmelCase__		,				UpperCAmelCase__      )





