




'''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(7_70)


__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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__=False					):
			UpperCAmelCase__  :					List[str]        =     model_type
			if use_small:
						key += "_small"
			return os.path.join(_lowerCamelCase ,      REMOTE_MODEL_PATHS[key]["""file_name"""]					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			os.makedirs(_lowerCamelCase ,      exist_ok=_lowerCamelCase					)
			hf_hub_download(repo_id=_lowerCamelCase ,      filename=_lowerCamelCase ,      local_dir=_lowerCamelCase					)







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

			# convert Bark model arguments to HF Bark model arguments
			UpperCAmelCase__  :					Optional[Any]        =     model_args.pop("""n_head"""					)
			UpperCAmelCase__  :					Optional[Any]        =     model_args.pop("""n_embd"""					)
			UpperCAmelCase__  :					Dict        =     model_args.pop("""n_layer"""					)

			UpperCAmelCase__  :					Union[str, Any]        =     ConfigClass(**checkpoint["""model_args"""]					)
			UpperCAmelCase__  :					Tuple        =     ModelClass(config=_lowerCamelCase					)
			UpperCAmelCase__  :					str        =     GenerationConfigClass()

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

									UpperCAmelCase__  :					Optional[Any]        =     state_dict.pop(_lowerCamelCase					)

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

			UpperCAmelCase__  :					List[Any]        =     """cpu"""  # do conversion on cpu

			UpperCAmelCase__  :					Union[str, Any]        =     _get_ckpt_path(_lowerCamelCase ,      use_small=_lowerCamelCase					)
			UpperCAmelCase__  :					Union[str, Any]        =     _load_model(_lowerCamelCase ,      _lowerCamelCase ,      model_type=_lowerCamelCase ,      use_small=_lowerCamelCase					)

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

			if model_type == "text":
						UpperCAmelCase__  :					List[str]        =     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
			UpperCAmelCase__  :					Dict        =     5
			UpperCAmelCase__  :					str        =     1_0

			if model_type in ["text", "coarse"]:
						UpperCAmelCase__  :					Tuple        =     torch.randint(2_5_6 ,      (batch_size, sequence_length) ,      dtype=torch.int					)
						UpperCAmelCase__  :					Optional[int]        =     bark_model(_lowerCamelCase					)[0]

						UpperCAmelCase__  :					List[Any]        =     model(_lowerCamelCase					)

						# take last logits
						UpperCAmelCase__  :					List[Any]        =     output_new_model_total.logits[:, [-1], :]

			else:
						UpperCAmelCase__  :					Optional[int]        =     3
						UpperCAmelCase__  :					str        =     8
						UpperCAmelCase__  :					Dict        =     torch.randint(2_5_6 ,      (batch_size, sequence_length, n_codes_total) ,      dtype=torch.int					)

						UpperCAmelCase__  :					List[str]        =     model(_lowerCamelCase ,      _lowerCamelCase					)
						UpperCAmelCase__  :					Tuple        =     bark_model(_lowerCamelCase ,      _lowerCamelCase					)

						UpperCAmelCase__  :					List[str]        =     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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      ):
			UpperCAmelCase__  :					List[str]        =     os.path.join(_lowerCamelCase ,      _lowerCamelCase					)

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

			UpperCAmelCase__  :					Optional[Any]        =     BarkSemanticModel.from_pretrained(_lowerCamelCase					)
			UpperCAmelCase__  :					Any        =     BarkCoarseModel.from_pretrained(_lowerCamelCase					)
			UpperCAmelCase__  :					List[str]        =     BarkFineModel.from_pretrained(_lowerCamelCase					)
			UpperCAmelCase__  :					List[Any]        =     EncodecModel.from_pretrained("""facebook/encodec_24khz"""					)

			UpperCAmelCase__  :					Any        =     BarkConfig.from_sub_model_configs(
			    _lowerCamelCase ,      _lowerCamelCase ,      _lowerCamelCase ,      _lowerCamelCase					)

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

			UpperCAmelCase__  :					List[str]        =     BarkModel(_lowerCamelCase					)

			UpperCAmelCase__  :					Tuple        =     semantic
			UpperCAmelCase__  :					List[str]        =     coarseAcoustic
			UpperCAmelCase__  :					Union[str, Any]        =     fineAcoustic
			UpperCAmelCase__  :					str        =     codec

			UpperCAmelCase__  :					str        =     bark_generation_config

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


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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A	={
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'NezhaForNextSentencePrediction',
		    'NezhaForMaskedLM',
		    'NezhaForPreTraining',
		    'NezhaForMultipleChoice',
		    'NezhaForQuestionAnswering',
		    'NezhaForSequenceClassification',
		    'NezhaForTokenClassification',
		    'NezhaModel',
		    'NezhaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

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