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  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=False     ):
    '''simple docstring'''







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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=False  ,      SCREAMING_SNAKE_CASE="text"     ):
    '''simple docstring'''







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

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

    __UpperCamelCase					:Optional[int]        =		ConfigClass(**checkpoint['''model_args''']     )
    __UpperCamelCase					:List[str]        =		ModelClass(config=lowerCAmelCase_     )
    __UpperCamelCase					:Tuple        =		GenerationConfigClass()

    __UpperCamelCase					:Optional[Any]        =		model_generation_config
    __UpperCamelCase					:Optional[Any]        =		checkpoint['model']
    # fixup checkpoint
    __UpperCamelCase					:int        =		'_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					: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					:List[Any]        =		state_dict.pop(lowerCAmelCase_     )

    __UpperCamelCase					:List[Any]        =		set(state_dict.keys()     ) - set(model.state_dict().keys()     )
    __UpperCamelCase					:Tuple        =		{k for k in extra_keys if not k.endswith('''.attn.bias'''     )}
    __UpperCamelCase					:Tuple        =		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					:Dict        =		model.num_parameters(exclude_embeddings=lowerCAmelCase_     )
    __UpperCamelCase					:Tuple        =		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  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=False  ,      SCREAMING_SNAKE_CASE="text"     ):
    '''simple docstring'''







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

    __UpperCamelCase					:Optional[int]        =		'cpu'  # do conversion on cpu

    __UpperCamelCase					:Tuple        =		_get_ckpt_path(lowerCAmelCase_  ,      use_small=lowerCAmelCase_     )
    __UpperCamelCase					:List[Any]        =		_load_model(lowerCAmelCase_  ,      lowerCAmelCase_  ,      model_type=lowerCAmelCase_  ,      use_small=lowerCAmelCase_     )

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

    if model_type == "text":
        __UpperCamelCase					:int        =		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					:Any        =		5
    __UpperCamelCase					:List[str]        =		10

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

        __UpperCamelCase					:Tuple        =		model(lowerCAmelCase_     )

        # take last logits
        __UpperCamelCase					:Optional[int]        =		output_new_model_total.logits[:, [-1], :]

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

        __UpperCamelCase					:Union[str, Any]        =		model(lowerCAmelCase_  ,      lowerCAmelCase_     )
        __UpperCamelCase					:int        =		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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      ):
    '''simple docstring'''







    __UpperCamelCase					:Any        =		os.path.join(lowerCAmelCase_  ,      lowerCAmelCase_     )

    __UpperCamelCase					:int        =		BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_  ,      '''config.json'''     )     )
    __UpperCamelCase					:Any        =		BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_  ,      '''config.json'''     )     )
    __UpperCamelCase					:List[Any]        =		BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_  ,      '''config.json'''     )     )
    __UpperCamelCase					:List[str]        =		EncodecConfig.from_pretrained('''facebook/encodec_24khz'''     )

    __UpperCamelCase					:str        =		BarkSemanticModel.from_pretrained(lowerCAmelCase_     )
    __UpperCamelCase					:Dict        =		BarkCoarseModel.from_pretrained(lowerCAmelCase_     )
    __UpperCamelCase					:int        =		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					:List[str]        =		BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config  ,      coarseAcoustic.generation_config  ,      fineAcoustic.generation_config     )

    __UpperCamelCase					:Optional[Any]        =		BarkModel(lowerCAmelCase_     )

    __UpperCamelCase					:List[str]        =		semantic
    __UpperCamelCase					:Union[str, Any]        =		coarseAcoustic
    __UpperCamelCase					:Optional[int]        =		fineAcoustic
    __UpperCamelCase					:Optional[Any]        =		codec

    __UpperCamelCase					:List[Any]        =		bark_generation_config

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


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)






from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      ):
    '''simple docstring'''







    if (stress, tangential_force, area).count(0     ) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''     )
    elif stress < 0:
        raise ValueError('''Stress cannot be negative'''     )
    elif tangential_force < 0:
        raise ValueError('''Tangential Force cannot be negative'''     )
    elif area < 0:
        raise ValueError('''Area cannot be negative'''     )
    elif stress == 0:
        return (
            "stress",
            tangential_force / area,
        )
    elif tangential_force == 0:
        return (
            "tangential_force",
            stress * area,
        )
    else:
        return (
            "area",
            tangential_force / stress,
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





