


"""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()
__SCREAMING_SNAKE_CASE							:    Optional[int]		=    logging.get_logger(__name__)

set_seed(770)


__SCREAMING_SNAKE_CASE							:    str		=    {
    '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',
}


__SCREAMING_SNAKE_CASE							:    Union[str, 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',
    },
}

__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    os.path.dirname(os.path.abspath(__file__))
__SCREAMING_SNAKE_CASE							:    str		=    os.path.join(os.path.expanduser('~'), '.cache')
__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')







def 						_a				(       _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=False				)    ->				Optional[int]:
							snake_case_           =		model_type
							if use_small:
														key += "_small"
							return os.path.join(snake_case_				, REMOTE_MODEL_PATHS[key]["""file_name"""]				)







def 						_a				(       _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							os.makedirs(snake_case_				, exist_ok=snake_case_				)
							hf_hub_download(repo_id=snake_case_				, filename=snake_case_				, local_dir=snake_case_				)







def 						_a				(       _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=False				, _SCREAMING_SNAKE_CASE="text"				)    ->				Tuple:
							if model_type == "text":
														snake_case_           =		BarkSemanticModel
														snake_case_           =		BarkSemanticConfig
														snake_case_           =		BarkSemanticGenerationConfig
							elif model_type == "coarse":
														snake_case_           =		BarkCoarseModel
														snake_case_           =		BarkCoarseConfig
														snake_case_           =		BarkCoarseGenerationConfig
							elif model_type == "fine":
														snake_case_           =		BarkFineModel
														snake_case_           =		BarkFineConfig
														snake_case_           =		BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							snake_case_           =		f"""{model_type}_small""" if use_small else model_type
							snake_case_           =		REMOTE_MODEL_PATHS[model_key]
							if not os.path.exists(snake_case_				):
														logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""				)
														_download(model_info["""repo_id"""]				, model_info["""file_name"""]				)
							snake_case_           =		torch.load(snake_case_				, map_location=snake_case_				)
							# this is a hack
							snake_case_           =		checkpoint["""model_args"""]
							if "input_vocab_size" not in model_args:
														snake_case_           =		model_args["""vocab_size"""]
														snake_case_           =		model_args["""vocab_size"""]
														del model_args["vocab_size"]

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

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

							snake_case_           =		model_generation_config
							snake_case_           =		checkpoint["""model"""]
							# fixup checkpoint
							snake_case_           =		"""_orig_mod."""
							for k, v in list(state_dict.items()				):
														if k.startswith(snake_case_				):
																					# replace part of the key with corresponding layer name in HF implementation
																					snake_case_           =		k[len(snake_case_				) :]
																					for old_layer_name in new_layer_name_dict:
																												snake_case_           =		new_k.replace(snake_case_				, new_layer_name_dict[old_layer_name]				)

																					snake_case_           =		state_dict.pop(snake_case_				)

							snake_case_           =		set(state_dict.keys()				) - set(model.state_dict().keys()				)
							snake_case_           =		{k for k in extra_keys if not k.endswith(""".attn.bias"""				)}
							snake_case_           =		set(model.state_dict().keys()				) - set(state_dict.keys()				)
							snake_case_           =		{k for k in missing_keys if not k.endswith(""".attn.bias"""				)}
							if len(snake_case_				) != 0:
														raise ValueError(f"""extra keys found: {extra_keys}"""				)
							if len(snake_case_				) != 0:
														raise ValueError(f"""missing keys: {missing_keys}"""				)
							model.load_state_dict(snake_case_				, strict=snake_case_				)
							snake_case_           =		model.num_parameters(exclude_embeddings=snake_case_				)
							snake_case_           =		checkpoint["""best_val_loss"""].item()
							logger.info(f"""model loaded: {round(n_params/1E6				, 1				)}M params, {round(snake_case_				, 3				)} loss"""				)
							model.eval()
							model.to(snake_case_				)
							del checkpoint, state_dict

							return model







def 						_a				(       _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=False				, _SCREAMING_SNAKE_CASE="text"				)    ->				Dict:
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							snake_case_           =		"""cpu"""  # do conversion on cpu

							snake_case_           =		_get_ckpt_path(snake_case_				, use_small=snake_case_				)
							snake_case_           =		_load_model(snake_case_				, snake_case_				, model_type=snake_case_				, use_small=snake_case_				)

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

							if model_type == "text":
														snake_case_           =		bark_model["""model"""]

							if model.num_parameters(exclude_embeddings=snake_case_				) != 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_           =		5
							snake_case_           =		10

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

														snake_case_           =		model(snake_case_				)

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

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

														snake_case_           =		model(snake_case_				, snake_case_				)
														snake_case_           =		bark_model(snake_case_				, snake_case_				)

														snake_case_           =		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(snake_case_				).mkdir(exist_ok=snake_case_				)
							model.save_pretrained(snake_case_				)







def 						_a				(       _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, )    ->				Union[str, Any]:
							snake_case_           =		os.path.join(snake_case_				, snake_case_				)

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

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

							snake_case_           =		BarkConfig.from_sub_model_configs(
							    snake_case_				, snake_case_				, snake_case_				, snake_case_				)

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

							snake_case_           =		BarkModel(snake_case_				)

							snake_case_           =		semantic
							snake_case_           =		coarseAcoustic
							snake_case_           =		fineAcoustic
							snake_case_           =		codec

							snake_case_           =		bark_generation_config

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    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.')

				__SCREAMING_SNAKE_CASE							:    str		=    parser.parse_args()

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







"""simple docstring"""
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				int:
							if index == number_of_items:
														return 0
							snake_case_           =		0
							snake_case_           =		0
							snake_case_           =		knapsack(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, index + 1				)
							if weights[index] <= max_weight:
														snake_case_           =		values[index] + knapsack(
														    _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, max_weight - weights[index]				, index + 1				)
							return max(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



