





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

set_seed(770)


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





def 		UpperCamelCase							(				_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   List[Any]=False			):
    A__							=     model_type
    if use_small:
        key += "_small"
    return os.path.join(_lowerCamelCase		,			REMOTE_MODEL_PATHS[key]["file_name"]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   int			):
    os.makedirs(_lowerCamelCase		,			exist_ok=_lowerCamelCase			)
    hf_hub_download(repo_id=_lowerCamelCase		,			filename=_lowerCamelCase		,			local_dir=_lowerCamelCase			)





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

    # convert Bark model arguments to HF Bark model arguments
    A__							=     model_args.pop("n_head"			)
    A__							=     model_args.pop("n_embd"			)
    A__							=     model_args.pop("n_layer"			)

    A__							=     ConfigClass(**checkpoint["model_args"]			)
    A__							=     ModelClass(config=_lowerCamelCase			)
    A__							=     GenerationConfigClass()

    A__							=     model_generation_config
    A__							=     checkpoint["model"]
    # fixup checkpoint
    A__							=     "_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
            A__							=     k[len(_lowerCamelCase			) :]
            for old_layer_name in new_layer_name_dict:
                A__							=     new_k.replace(_lowerCamelCase		,			new_layer_name_dict[old_layer_name]			)

            A__							=     state_dict.pop(_lowerCamelCase			)

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

    A__							=     "cpu"  # do conversion on cpu

    A__							=     _get_ckpt_path(_lowerCamelCase		,			use_small=_lowerCamelCase			)
    A__							=     _load_model(_lowerCamelCase		,			_lowerCamelCase		,			model_type=_lowerCamelCase		,			use_small=_lowerCamelCase			)

    # load bark initial model
    A__							=     _bark_load_model(_lowerCamelCase		,			"cpu"		,			model_type=_lowerCamelCase		,			use_small=_lowerCamelCase			)

    if model_type == "text":
        A__							=     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
    A__							=     5
    A__							=     10

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

        A__							=     model(_lowerCamelCase			)

        # take last logits
        A__							=     output_new_model_total.logits[:, [-1], :]

    else:
        A__							=     3
        A__							=     8
        A__							=     torch.randint(2_56		,			(batch_size, sequence_length, n_codes_total)		,			dtype=torch.int			)

        A__							=     model(_lowerCamelCase		,			_lowerCamelCase			)
        A__							=     bark_model(_lowerCamelCase		,			_lowerCamelCase			)

        A__							=     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   :   Dict		,			_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   Any		,			_lowerCamelCase   :   Any		,			_lowerCamelCase   :   Tuple		,			):
    A__							=     os.path.join(_lowerCamelCase		,			_lowerCamelCase			)

    A__							=     BarkSemanticConfig.from_pretrained(os.path.join(_lowerCamelCase		,			"config.json"			)			)
    A__							=     BarkCoarseConfig.from_pretrained(os.path.join(_lowerCamelCase		,			"config.json"			)			)
    A__							=     BarkFineConfig.from_pretrained(os.path.join(_lowerCamelCase		,			"config.json"			)			)
    A__							=     EncodecConfig.from_pretrained("facebook/encodec_24khz"			)

    A__							=     BarkSemanticModel.from_pretrained(_lowerCamelCase			)
    A__							=     BarkCoarseModel.from_pretrained(_lowerCamelCase			)
    A__							=     BarkFineModel.from_pretrained(_lowerCamelCase			)
    A__							=     EncodecModel.from_pretrained("facebook/encodec_24khz"			)

    A__							=     BarkConfig.from_sub_model_configs(
        _lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)

    A__							=     BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config		,			coarseAcoustic.generation_config		,			fineAcoustic.generation_config			)

    A__							=     BarkModel(_lowerCamelCase			)

    A__							=     semantic
    A__							=     coarseAcoustic
    A__							=     fineAcoustic
    A__							=     codec

    A__							=     bark_generation_config

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


if __name__ == "__main__":
  __lowerCAmelCase				:      List[Any]						      =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				:      Union[str, Any]						      =parser.parse_args()

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










'''simple docstring'''

import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase				:      int						      =5_0000
__lowerCAmelCase				:      Union[str, Any]						      =5000

__lowerCAmelCase      ,	__lowerCAmelCase				:      Tuple						      =os.path.split(__file__)
__lowerCAmelCase				:      int						      =os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   int			):
    for i in range(_lowerCamelCase			):
        A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Any			):
    for i in range(0		,			len(_lowerCamelCase			)		,			_lowerCamelCase			):
        A__							=     dataset[i : i + batch_size]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   List[str]			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(_lowerCamelCase			):
            A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Dict			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(0		,			_lowerCamelCase		,			_lowerCamelCase			):
            A__							=     dataset[i : i + batch_size]





def 		UpperCamelCase							(				):
    A__							=     {"num examples": SPEED_TEST_N_EXAMPLES}
    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
        (read_formatted, {"type": "torch", "length": SMALL_TEST}),
        (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]

    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"			)
        A__							=     datasets.Features(
            {"list": datasets.Sequence(datasets.Value("float32"			)			), "numbers": datasets.Value("float32"			)}			)
        A__							=     generate_example_dataset(
            os.path.join(_lowerCamelCase		,			"dataset.arrow"			)		,			_lowerCamelCase		,			num_examples=_lowerCamelCase		,			seq_shapes={"list": (1_00,)}		,			)
        print("first set of iterations"			)
        for func, kwargs in functions:
            print(func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(_lowerCamelCase		,			**_lowerCamelCase			)

        print("shuffling dataset"			)
        A__							=     dataset.shuffle()
        print("Second set of iterations (after shuffling"			)
        for func, kwargs in functions_shuffled:
            print("shuffled "		,			func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(
                _lowerCamelCase		,			**_lowerCamelCase			)

    with open(_lowerCamelCase		,			"wb"			) as f:
        f.write(json.dumps(_lowerCamelCase			).encode("utf-8"			)			)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_iterating()



