
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()
__lowerCAmelCase       :      Any         =		logging.get_logger(__name__)

set_seed(770)


__lowerCAmelCase       :      Union[str, 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",
}


__lowerCAmelCase       :      str         =		{
    "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",
    },
}

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


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


def      UpperCAmelCase_	(    __lowerCAmelCase						,       __lowerCAmelCase						)			->    List[str]:
    os.makedirs(__lowerCAmelCase						,       exist_ok=__lowerCAmelCase						)
    hf_hub_download(repo_id=__lowerCAmelCase						,       filename=__lowerCAmelCase						,       local_dir=__lowerCAmelCase						)


def      UpperCAmelCase_	(    __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase=False						,       __lowerCAmelCase="text"						)			->    Optional[int]:




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

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

    __lowercase		:      Dict						     =							ConfigClass(**checkpoint['''model_args''']						)
    __lowercase		:      Tuple						     =							ModelClass(config=__lowerCAmelCase						)
    __lowercase		:      List[Any]						     =							GenerationConfigClass()

    __lowercase		:      Union[str, Any]						     =							model_generation_config
    __lowercase		:      Union[str, Any]						     =							checkpoint["model"]
    # fixup checkpoint
    __lowercase		:      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
            __lowercase		:      Dict						     =							k[len(__lowerCAmelCase						) :]
            for old_layer_name in new_layer_name_dict:
                __lowercase		:      Dict						     =							new_k.replace(__lowerCAmelCase						,       new_layer_name_dict[old_layer_name]						)

            __lowercase		:      Union[str, Any]						     =							state_dict.pop(__lowerCAmelCase						)

    __lowercase		:      Tuple						     =							set(state_dict.keys()						) - set(model.state_dict().keys()						)
    __lowercase		:      str						     =							{k for k in extra_keys if not k.endswith('''.attn.bias'''						)}
    __lowercase		:      Tuple						     =							set(model.state_dict().keys()						) - set(state_dict.keys()						)
    __lowercase		:      Tuple						     =							{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						)
    __lowercase		:      Optional[int]						     =							model.num_parameters(exclude_embeddings=__lowerCAmelCase						)
    __lowercase		:      str						     =							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_	(    __lowerCAmelCase						,       __lowerCAmelCase=False						,       __lowerCAmelCase="text"						)			->    Tuple:




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

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

    __lowercase		:      str						     =							_get_ckpt_path(__lowerCAmelCase						,       use_small=__lowerCAmelCase						)
    __lowercase		:      int						     =							_load_model(__lowerCAmelCase						,       __lowerCAmelCase						,       model_type=__lowerCAmelCase						,       use_small=__lowerCAmelCase						)

    # load bark initial model
    __lowercase		:      int						     =							_bark_load_model(__lowerCAmelCase						,       '''cpu'''						,       model_type=__lowerCAmelCase						,       use_small=__lowerCAmelCase						)

    if model_type == "text":
        __lowercase		:      Optional[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
    __lowercase		:      Optional[int]						     =							5
    __lowercase		:      str						     =							10

    if model_type in ["text", "coarse"]:
        __lowercase		:      Tuple						     =							torch.randint(256						,       (batch_size, sequence_length)						,       dtype=torch.int						)
        __lowercase		:      List[str]						     =							bark_model(__lowerCAmelCase						)[0]

        __lowercase		:      Dict						     =							model(__lowerCAmelCase						)

        # take last logits
        __lowercase		:      Union[str, Any]						     =							output_new_model_total.logits[:, [-1], :]

    else:
        __lowercase		:      Dict						     =							3
        __lowercase		:      int						     =							8
        __lowercase		:      int						     =							torch.randint(256						,       (batch_size, sequence_length, n_codes_total)						,       dtype=torch.int						)

        __lowercase		:      Union[str, Any]						     =							model(__lowerCAmelCase						,       __lowerCAmelCase						)
        __lowercase		:      Dict						     =							bark_model(__lowerCAmelCase						,       __lowerCAmelCase						)

        __lowercase		:      Union[str, Any]						     =							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_	(    __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						,       )			->    Union[str, Any]:
    __lowercase		:      Optional[Any]						     =							os.path.join(__lowerCAmelCase						,       __lowerCAmelCase						)

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

    __lowercase		:      Optional[int]						     =							BarkSemanticModel.from_pretrained(__lowerCAmelCase						)
    __lowercase		:      Optional[int]						     =							BarkCoarseModel.from_pretrained(__lowerCAmelCase						)
    __lowercase		:      Tuple						     =							BarkFineModel.from_pretrained(__lowerCAmelCase						)
    __lowercase		:      Dict						     =							EncodecModel.from_pretrained('''facebook/encodec_24khz'''						)

    __lowercase		:      List[str]						     =							BarkConfig.from_sub_model_configs(
        __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						)

    __lowercase		:      str						     =							BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config						,       coarseAcoustic.generation_config						,       fineAcoustic.generation_config						)

    __lowercase		:      Union[str, Any]						     =							BarkModel(__lowerCAmelCase						)

    __lowercase		:      Any						     =							semantic
    __lowercase		:      int						     =							coarseAcoustic
    __lowercase		:      Optional[int]						     =							fineAcoustic
    __lowercase		:      Optional[Any]						     =							codec

    __lowercase		:      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__":
 __lowerCAmelCase       :      Dict         =		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.")

 __lowerCAmelCase       :      Any         =		parser.parse_args()

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






from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__     					=			Lock()


def     lowercase	(    a     , a     , a     , a     , a     , a     , a ):



		'''simple docstring'''




		global process_lock

		# we perform n swaps since after n swaps we know we are sorted
		# we *could* stop early if we are sorted already, but it takes as long to
		# find out we are sorted as it does to sort the list with this algorithm
		for i in range(0     , 10 ):
				if (i + position) % 2 == 0 and r_send is not None:
						# send your value to your right neighbor
						process_lock.acquire()
						r_send[1].send(a )
						process_lock.release()

						# receive your right neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:str				     =						rr_cv[0].recv()
						process_lock.release()

						# take the lower value since you are on the left
						SCREAMING_SNAKE_CASE_				:int				     =						min(a     , a )
				elif (i + position) % 2 != 0 and l_send is not None:
						# send your value to your left neighbor
						process_lock.acquire()
						l_send[1].send(a )
						process_lock.release()

						# receive your left neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:int				     =						lr_cv[0].recv()
						process_lock.release()

						# take the higher value since you are on the right
						SCREAMING_SNAKE_CASE_				:Dict				     =						max(a     , a )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(a )


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]
		# initialize the list of pipes where the values will be retrieved
		for _ in arr:
				result_pipe.append(Pipe() )
		# creates the processes
		# the first and last process only have one neighbor so they are made outside
		# of the loop
		SCREAMING_SNAKE_CASE_				:str				     =						Pipe()
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						Pipe()
		process_array_.append(
		    Process(
		        target=a     , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])     , ) )
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						temp_rs
		SCREAMING_SNAKE_CASE_				:Any				     =						temp_rr

		for i in range(1     , len(a ) - 1 ):
				SCREAMING_SNAKE_CASE_				:int				     =						Pipe()
				SCREAMING_SNAKE_CASE_				:Dict				     =						Pipe()
				process_array_.append(
				    Process(
				        target=a     , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])     , ) )
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp_rs
				SCREAMING_SNAKE_CASE_				:int				     =						temp_rr

		process_array_.append(
		    Process(
		        target=a     , args=(
		            len(a ) - 1,
		            arr[len(a ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(a ) - 1],
		        )     , ) )

		# start the processes
		for p in process_array_:
				p.start()

		# wait for the processes to end and write their values to the list
		for p in range(0     , len(a ) ):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						result_pipe[p][0].recv()
				process_array_[p].join()
		return arr


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[str]				     =						list(range(10     , 0     , -1 ) )
		print("Initial List" )
		print(*a )
		SCREAMING_SNAKE_CASE_				:int				     =						odd_even_transposition(a )
		print("Sorted List\n" )
		print(*a )


if __name__ == "__main__":
		main()






