


'''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(7_7_0)


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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__=False						)    ->	Any:
    UpperCAmelCase__			:		Optional[int]													=				model_type
    if use_small:
        key += "_small"
    return os.path.join(__lowerCamelCase  ,					REMOTE_MODEL_PATHS[key]['''file_name''']						)





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Any:
    os.makedirs(__lowerCamelCase  ,					exist_ok=__lowerCamelCase						)
    hf_hub_download(repo_id=__lowerCamelCase  ,					filename=__lowerCamelCase  ,					local_dir=__lowerCamelCase						)





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

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

    UpperCAmelCase__			:		List[str]													=				ConfigClass(**checkpoint['''model_args''']						)
    UpperCAmelCase__			:		Any													=				ModelClass(config=__lowerCamelCase						)
    UpperCAmelCase__			:		Tuple													=				GenerationConfigClass()

    UpperCAmelCase__			:		int													=				model_generation_config
    UpperCAmelCase__			:		int													=				checkpoint['''model''']
    # fixup checkpoint
    UpperCAmelCase__			:		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
            UpperCAmelCase__			:		Tuple													=				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__			:		Optional[Any]													=				set(state_dict.keys()						) - set(model.state_dict().keys()						)
    UpperCAmelCase__			:		Optional[int]													=				{k for k in extra_keys if not k.endswith('''.attn.bias'''						)}
    UpperCAmelCase__			:		Tuple													=				set(model.state_dict().keys()						) - set(state_dict.keys()						)
    UpperCAmelCase__			:		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						)
    UpperCAmelCase__			:		List[Any]													=				model.num_parameters(exclude_embeddings=__lowerCamelCase						)
    UpperCAmelCase__			:		Dict													=				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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__=False  ,					lowerCAmelCase__="text"						)    ->	Tuple:
    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    UpperCAmelCase__			:		List[str]													=				'''cpu'''  # do conversion on cpu

    UpperCAmelCase__			:		Optional[int]													=				_get_ckpt_path(__lowerCamelCase  ,					use_small=__lowerCamelCase						)
    UpperCAmelCase__			:		List[str]													=				_load_model(__lowerCamelCase  ,					__lowerCamelCase  ,					model_type=__lowerCamelCase  ,					use_small=__lowerCamelCase						)

    # load bark initial model
    UpperCAmelCase__			:		List[Any]													=				_bark_load_model(__lowerCamelCase  ,					'''cpu'''  ,					model_type=__lowerCamelCase  ,					use_small=__lowerCamelCase						)

    if model_type == "text":
        UpperCAmelCase__			:		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__			:		Optional[int]													=				5
    UpperCAmelCase__			:		Optional[int]													=				10

    if model_type in ["text", "coarse"]:
        UpperCAmelCase__			:		Tuple													=				torch.randint(2_56  ,					(batch_size, sequence_length)  ,					dtype=torch.int						)
        UpperCAmelCase__			:		int													=				bark_model(__lowerCamelCase						)[0]

        UpperCAmelCase__			:		Optional[int]													=				model(__lowerCamelCase						)

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

    else:
        UpperCAmelCase__			:		Optional[Any]													=				3
        UpperCAmelCase__			:		Union[str, Any]													=				8
        UpperCAmelCase__			:		Tuple													=				torch.randint(2_56  ,					(batch_size, sequence_length, n_codes_total)  ,					dtype=torch.int						)

        UpperCAmelCase__			:		int													=				model(__lowerCamelCase  ,					__lowerCamelCase						)
        UpperCAmelCase__			:		Dict													=				bark_model(__lowerCamelCase  ,					__lowerCamelCase						)

        UpperCAmelCase__			:		int													=				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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					)    ->	List[str]:
    UpperCAmelCase__			:		str													=				os.path.join(__lowerCamelCase  ,					__lowerCamelCase						)

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

    UpperCAmelCase__			:		Optional[Any]													=				BarkSemanticModel.from_pretrained(__lowerCamelCase						)
    UpperCAmelCase__			:		str													=				BarkCoarseModel.from_pretrained(__lowerCamelCase						)
    UpperCAmelCase__			:		Union[str, Any]													=				BarkFineModel.from_pretrained(__lowerCamelCase						)
    UpperCAmelCase__			:		Tuple													=				EncodecModel.from_pretrained('''facebook/encodec_24khz'''						)

    UpperCAmelCase__			:		Tuple													=				BarkConfig.from_sub_model_configs(
        __lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase						)

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

    UpperCAmelCase__			:		List[Any]													=				BarkModel(__lowerCamelCase						)

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

    UpperCAmelCase__			:		List[str]													=				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'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline



