



"""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__       :    List[Any]				       =      logging.get_logger(__name__)

set_seed(7_70)


lowercase__       :    Any				       =      {
    """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__       :    int				       =      {
    """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__       :    Optional[Any]				       =      os.path.dirname(os.path.abspath(__file__))
lowercase__       :    int				       =      os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowercase__       :    int				       =      os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')




def __lowercase     (				_a						,     _a=False       ):
    snake_case_					:	Union[str, Any]							=      model_type
    if use_small:
        key += "_small"
    return os.path.join(lowerCAmelCase__						,     REMOTE_MODEL_PATHS[key]['''file_name''']       )




def __lowercase     (				_a						,     _a       ):
    os.makedirs(lowerCAmelCase__						,     exist_ok=lowerCAmelCase__       )
    hf_hub_download(repo_id=lowerCAmelCase__						,     filename=lowerCAmelCase__						,     local_dir=lowerCAmelCase__       )




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

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

    snake_case_					:	Dict							=      ConfigClass(**checkpoint['''model_args''']       )
    snake_case_					:	str							=      ModelClass(config=lowerCAmelCase__       )
    snake_case_					:	Union[str, Any]							=      GenerationConfigClass()

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

            snake_case_					:	Dict							=      state_dict.pop(lowerCAmelCase__       )

    snake_case_					:	List[str]							=      set(state_dict.keys()       ) - set(model.state_dict().keys()       )
    snake_case_					:	str							=      {k for k in extra_keys if not k.endswith('''.attn.bias'''       )}
    snake_case_					:	List[Any]							=      set(model.state_dict().keys()       ) - set(state_dict.keys()       )
    snake_case_					:	Dict							=      {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__       )
    snake_case_					:	List[str]							=      model.num_parameters(exclude_embeddings=lowerCAmelCase__       )
    snake_case_					:	Optional[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 __lowercase     (				_a						,     _a=False						,     _a="text"       ):
    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    snake_case_					:	List[Any]							=      'cpu'  # do conversion on cpu

    snake_case_					:	List[str]							=      _get_ckpt_path(lowerCAmelCase__						,     use_small=lowerCAmelCase__       )
    snake_case_					:	Optional[int]							=      _load_model(lowerCAmelCase__						,     lowerCAmelCase__						,     model_type=lowerCAmelCase__						,     use_small=lowerCAmelCase__       )

    # load bark initial model
    snake_case_					:	Any							=      _bark_load_model(lowerCAmelCase__						,     '''cpu'''						,     model_type=lowerCAmelCase__						,     use_small=lowerCAmelCase__       )

    if model_type == "text":
        snake_case_					:	List[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
    snake_case_					:	Optional[Any]							=      5
    snake_case_					:	int							=      10

    if model_type in ["text", "coarse"]:
        snake_case_					:	Tuple							=      torch.randint(256						,     (batch_size, sequence_length)						,     dtype=torch.int       )
        snake_case_					:	Optional[int]							=      bark_model(lowerCAmelCase__       )[0]

        snake_case_					:	int							=      model(lowerCAmelCase__       )

        # take last logits
        snake_case_					:	Optional[Any]							=      output_new_model_total.logits[:, [-1], :]

    else:
        snake_case_					:	str							=      3
        snake_case_					:	str							=      8
        snake_case_					:	Any							=      torch.randint(256						,     (batch_size, sequence_length, n_codes_total)						,     dtype=torch.int       )

        snake_case_					:	Dict							=      model(lowerCAmelCase__						,     lowerCAmelCase__       )
        snake_case_					:	Tuple							=      bark_model(lowerCAmelCase__						,     lowerCAmelCase__       )

        snake_case_					:	Tuple							=      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 __lowercase     (				_a						,     _a						,     _a						,     _a						,     _a						,     _a						,     ):
    snake_case_					:	Optional[Any]							=      os.path.join(lowerCAmelCase__						,     lowerCAmelCase__       )

    snake_case_					:	Tuple							=      BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase__						,     '''config.json'''       )       )
    snake_case_					:	str							=      BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase__						,     '''config.json'''       )       )
    snake_case_					:	Dict							=      BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase__						,     '''config.json'''       )       )
    snake_case_					:	int							=      EncodecConfig.from_pretrained('''facebook/encodec_24khz'''       )

    snake_case_					:	List[str]							=      BarkSemanticModel.from_pretrained(lowerCAmelCase__       )
    snake_case_					:	List[Any]							=      BarkCoarseModel.from_pretrained(lowerCAmelCase__       )
    snake_case_					:	Optional[int]							=      BarkFineModel.from_pretrained(lowerCAmelCase__       )
    snake_case_					:	Tuple							=      EncodecModel.from_pretrained('''facebook/encodec_24khz'''       )

    snake_case_					:	Optional[Any]							=      BarkConfig.from_sub_model_configs(
        lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__       )

    snake_case_					:	Optional[Any]							=      BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config						,     coarseAcoustic.generation_config						,     fineAcoustic.generation_config       )

    snake_case_					:	str							=      BarkModel(lowerCAmelCase__       )

    snake_case_					:	Optional[Any]							=      semantic
    snake_case_					:	Union[str, Any]							=      coarseAcoustic
    snake_case_					:	Dict							=      fineAcoustic
    snake_case_					:	Tuple							=      codec

    snake_case_					:	Tuple							=      bark_generation_config

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


if __name__ == "__main__":
     lowercase__       :    List[str]				       =      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__       :    int				       =      parser.parse_args()

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












from typing import TYPE_CHECKING

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


_lowercase		:  Dict			       ={
    """configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ResNetForImageClassification""",
		    """ResNetModel""",
		    """ResNetPreTrainedModel""",
		    """ResNetBackbone""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFResNetForImageClassification""",
		    """TFResNetModel""",
		    """TFResNetPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Tuple			       =[
		    """FlaxResNetForImageClassification""",
		    """FlaxResNetModel""",
		    """FlaxResNetPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		_lowercase		:  Union[str, Any]			       =_LazyModule(__name__, globals()["""__file__"""], _import_structure)
