
"""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()
__magic_name__       =			logging.get_logger(__name__)

set_seed(770)


__magic_name__       =			{
    "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",
}


__magic_name__       =			{
    "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",
    },
}

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



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_=False				):
		__SCREAMING_SNAKE_CASE            = model_type
		if use_small:
				key += "_small"
		return os.path.join(UpperCamelCase_  ,				REMOTE_MODEL_PATHS[key]["""file_name"""]				)



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		os.makedirs(UpperCamelCase_  ,				exist_ok=UpperCamelCase_				)
		hf_hub_download(repo_id=UpperCamelCase_  ,				filename=UpperCamelCase_  ,				local_dir=UpperCamelCase_				)



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

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

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

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

						__SCREAMING_SNAKE_CASE            = state_dict.pop(UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = set(state_dict.keys()				) - set(model.state_dict().keys()				)
		__SCREAMING_SNAKE_CASE            = {k for k in extra_keys if not k.endswith(""".attn.bias"""				)}
		__SCREAMING_SNAKE_CASE            = set(model.state_dict().keys()				) - set(state_dict.keys()				)
		__SCREAMING_SNAKE_CASE            = {k for k in missing_keys if not k.endswith(""".attn.bias"""				)}
		if len(UpperCamelCase_				) != 0:
				raise ValueError(f"extra keys found: {extra_keys}"				)
		if len(UpperCamelCase_				) != 0:
				raise ValueError(f"missing keys: {missing_keys}"				)
		model.load_state_dict(UpperCamelCase_  ,				strict=UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = model.num_parameters(exclude_embeddings=UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = checkpoint["""best_val_loss"""].item()
		logger.info(f"model loaded: {round(n_params/1e6  ,				1				)}M params, {round(UpperCamelCase_  ,				3				)} loss"				)
		model.eval()
		model.to(UpperCamelCase_				)
		del checkpoint, state_dict

		return model



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_=False  ,				UpperCamelCase_="text"				):
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		__SCREAMING_SNAKE_CASE            = """cpu"""  # do conversion on cpu

		__SCREAMING_SNAKE_CASE            = _get_ckpt_path(UpperCamelCase_  ,				use_small=UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = _load_model(UpperCamelCase_  ,				UpperCamelCase_  ,				model_type=UpperCamelCase_  ,				use_small=UpperCamelCase_				)

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

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

		if model.num_parameters(exclude_embeddings=UpperCamelCase_				) != 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
		__SCREAMING_SNAKE_CASE            = 5
		__SCREAMING_SNAKE_CASE            = 10

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

				__SCREAMING_SNAKE_CASE            = model(UpperCamelCase_				)

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

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

				__SCREAMING_SNAKE_CASE            = model(UpperCamelCase_  ,				UpperCamelCase_				)
				__SCREAMING_SNAKE_CASE            = bark_model(UpperCamelCase_  ,				UpperCamelCase_				)

				__SCREAMING_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(UpperCamelCase_				).mkdir(exist_ok=UpperCamelCase_				)
		model.save_pretrained(UpperCamelCase_				)



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				):
		__SCREAMING_SNAKE_CASE            = os.path.join(UpperCamelCase_  ,				UpperCamelCase_				)

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

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

		__SCREAMING_SNAKE_CASE            = BarkConfig.from_sub_model_configs(
		    UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)

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

		__SCREAMING_SNAKE_CASE            = BarkModel(UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = semantic
		__SCREAMING_SNAKE_CASE            = coarseAcoustic
		__SCREAMING_SNAKE_CASE            = fineAcoustic
		__SCREAMING_SNAKE_CASE            = codec

		__SCREAMING_SNAKE_CASE            = bark_generation_config

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


if __name__ == "__main__":
							__magic_name__       =			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.")

							__magic_name__       =			parser.parse_args()

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






"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_ = 100				):
		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		for i in range(1  ,				n + 1				):
				sum_of_squares += i**2
				sum_of_ints += i
		return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
							print(F"""{solution() = }""")




