






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

set_seed(770)


lowercase__  			=					{
    """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""",
}


lowercase__  			=					{
    """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""",
    },
}

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

def 						__lowerCamelCase      (    __UpperCamelCase ,			__UpperCamelCase=False			)		->       Tuple:

		"""simple docstring"""







		lowerCAmelCase_  :				Optional[int]   							=     model_type
		if use_small:
				key += "_small"
		return os.path.join(_snake_case ,			REMOTE_MODEL_PATHS[key]["file_name"]			)

def 						__lowerCamelCase      (    __UpperCamelCase ,			__UpperCamelCase			)		->       int:

		"""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      (    __UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=False ,			__UpperCamelCase="text"			)		->       str:

		"""simple docstring"""







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

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

		lowerCAmelCase_  :				str   							=     ConfigClass(**checkpoint["model_args"]			)
		lowerCAmelCase_  :				Union[str, Any]   							=     ModelClass(config=_snake_case			)
		lowerCAmelCase_  :				Any   							=     GenerationConfigClass()

		lowerCAmelCase_  :				Tuple   							=     model_generation_config
		lowerCAmelCase_  :				Optional[Any]   							=     checkpoint['model']
		# fixup checkpoint
		lowerCAmelCase_  :				Dict   							=     '_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
						lowerCAmelCase_  :				Tuple   							=     k[len(_snake_case			) :]
						for old_layer_name in new_layer_name_dict:
								lowerCAmelCase_  :				List[str]   							=     new_k.replace(_snake_case ,			new_layer_name_dict[old_layer_name]			)

						lowerCAmelCase_  :				Optional[int]   							=     state_dict.pop(_snake_case			)

		lowerCAmelCase_  :				Optional[Any]   							=     set(state_dict.keys()			) - set(model.state_dict().keys()			)
		lowerCAmelCase_  :				Dict   							=     {k for k in extra_keys if not k.endswith(".attn.bias"			)}
		lowerCAmelCase_  :				List[str]   							=     set(model.state_dict().keys()			) - set(state_dict.keys()			)
		lowerCAmelCase_  :				Optional[int]   							=     {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			)
		lowerCAmelCase_  :				List[str]   							=     model.num_parameters(exclude_embeddings=_snake_case			)
		lowerCAmelCase_  :				Optional[int]   							=     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      (    __UpperCamelCase ,			__UpperCamelCase=False ,			__UpperCamelCase="text"			)		->       Optional[Any]:

		"""simple docstring"""







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

		lowerCAmelCase_  :				Dict   							=     'cpu'  # do conversion on cpu

		lowerCAmelCase_  :				List[Any]   							=     _get_ckpt_path(_snake_case ,			use_small=_snake_case			)
		lowerCAmelCase_  :				Optional[int]   							=     _load_model(_snake_case ,			_snake_case ,			model_type=_snake_case ,			use_small=_snake_case			)

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

		if model_type == "text":
				lowerCAmelCase_  :				int   							=     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
		lowerCAmelCase_  :				Tuple   							=     5
		lowerCAmelCase_  :				Dict   							=     10

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

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

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

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

				lowerCAmelCase_  :				int   							=     model(_snake_case ,			_snake_case			)
				lowerCAmelCase_  :				Dict   							=     bark_model(_snake_case ,			_snake_case			)

				lowerCAmelCase_  :				Dict   							=     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      (    __UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)		->       List[str]:

		"""simple docstring"""







		lowerCAmelCase_  :				Union[str, Any]   							=     os.path.join(_snake_case ,			_snake_case			)

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

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

		lowerCAmelCase_  :				Dict   							=     BarkConfig.from_sub_model_configs(
		    _snake_case ,			_snake_case ,			_snake_case ,			_snake_case			)

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

		lowerCAmelCase_  :				int   							=     BarkModel(_snake_case			)

		lowerCAmelCase_  :				Any   							=     semantic
		lowerCAmelCase_  :				Any   							=     coarseAcoustic
		lowerCAmelCase_  :				int   							=     fineAcoustic
		lowerCAmelCase_  :				Tuple   							=     codec

		lowerCAmelCase_  :				List[Any]   							=     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__":
					lowercase__  			=					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.""")

					lowercase__  			=					parser.parse_args()

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


"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__          =	{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__          =	[
        'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MegaForCausalLM',
        'MegaForMaskedLM',
        'MegaForMultipleChoice',
        'MegaForQuestionAnswering',
        'MegaForSequenceClassification',
        'MegaForTokenClassification',
        'MegaModel',
        'MegaPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mega import (
            MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
            MegaForCausalLM,
            MegaForMaskedLM,
            MegaForMultipleChoice,
            MegaForQuestionAnswering,
            MegaForSequenceClassification,
            MegaForTokenClassification,
            MegaModel,
            MegaPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase__          =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

