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		:     Dict         =	logging.get_logger(__name__)

set_seed(770)


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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Optional[int]=False			):
						UpperCamelCase_		:							Optional[int]  =		model_type
						if use_small:
												key += "_small"
						return os.path.join(_SCREAMING_SNAKE_CASE		, REMOTE_MODEL_PATHS[key]["""file_name"""]			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				Tuple			):
						os.makedirs(_SCREAMING_SNAKE_CASE		, exist_ok=_SCREAMING_SNAKE_CASE			)
						hf_hub_download(repo_id=_SCREAMING_SNAKE_CASE		, filename=_SCREAMING_SNAKE_CASE		, local_dir=_SCREAMING_SNAKE_CASE			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				List[str]=False		, _SCREAMING_SNAKE_CASE     :				List[str]="text"			):
						if model_type == "text":
												UpperCamelCase_		:							List[str]  =		BarkSemanticModel
												UpperCamelCase_		:							List[str]  =		BarkSemanticConfig
												UpperCamelCase_		:							Optional[Any]  =		BarkSemanticGenerationConfig
						elif model_type == "coarse":
												UpperCamelCase_		:							Tuple  =		BarkCoarseModel
												UpperCamelCase_		:							List[Any]  =		BarkCoarseConfig
												UpperCamelCase_		:							Union[str, Any]  =		BarkCoarseGenerationConfig
						elif model_type == "fine":
												UpperCamelCase_		:							str  =		BarkFineModel
												UpperCamelCase_		:							Any  =		BarkFineConfig
												UpperCamelCase_		:							Dict  =		BarkFineGenerationConfig
						else:
												raise NotImplementedError()
						UpperCamelCase_		:							List[str]  =		f'''{model_type}_small''' if use_small else model_type
						UpperCamelCase_		:							List[Any]  =		REMOTE_MODEL_PATHS[model_key]
						if not os.path.exists(_SCREAMING_SNAKE_CASE			):
												logger.info(f'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''			)
												_download(model_info["""repo_id"""]		, model_info["""file_name"""]			)
						UpperCamelCase_		:							int  =		torch.load(_SCREAMING_SNAKE_CASE		, map_location=_SCREAMING_SNAKE_CASE			)
						# this is a hack
						UpperCamelCase_		:							Union[str, Any]  =		checkpoint["""model_args"""]
						if "input_vocab_size" not in model_args:
												UpperCamelCase_		:							Any  =		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_		:							Dict  =		model_args.pop("""n_head"""			)
						UpperCamelCase_		:							Dict  =		model_args.pop("""n_embd"""			)
						UpperCamelCase_		:							Any  =		model_args.pop("""n_layer"""			)

						UpperCamelCase_		:							int  =		ConfigClass(**checkpoint["""model_args"""]			)
						UpperCamelCase_		:							Optional[int]  =		ModelClass(config=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Optional[int]  =		GenerationConfigClass()

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

																		UpperCamelCase_		:							Optional[Any]  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)

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

						return model






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				Optional[Any]=False		, _SCREAMING_SNAKE_CASE     :				Optional[Any]="text"			):
						if model_type not in ("text", "coarse", "fine"):
												raise NotImplementedError()

						UpperCamelCase_		:							Optional[int]  =		"""cpu"""  # do conversion on cpu

						UpperCamelCase_		:							Optional[int]  =		_get_ckpt_path(_SCREAMING_SNAKE_CASE		, use_small=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Union[str, Any]  =		_load_model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, model_type=_SCREAMING_SNAKE_CASE		, use_small=_SCREAMING_SNAKE_CASE			)

						# load bark initial model
						UpperCamelCase_		:							Dict  =		_bark_load_model(_SCREAMING_SNAKE_CASE		, """cpu"""		, model_type=_SCREAMING_SNAKE_CASE		, use_small=_SCREAMING_SNAKE_CASE			)

						if model_type == "text":
												UpperCamelCase_		:							Optional[int]  =		bark_model["""model"""]

						if model.num_parameters(exclude_embeddings=_SCREAMING_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
						UpperCamelCase_		:							Tuple  =		5
						UpperCamelCase_		:							int  =		10

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

												UpperCamelCase_		:							Tuple  =		model(_SCREAMING_SNAKE_CASE			)

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

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

												UpperCamelCase_		:							str  =		model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
												UpperCamelCase_		:							List[Any]  =		bark_model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Any		, ):
						UpperCamelCase_		:							Optional[Any]  =		os.path.join(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							List[Any]  =		BarkSemanticConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE		, """config.json"""			)			)
						UpperCamelCase_		:							str  =		BarkCoarseConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE		, """config.json"""			)			)
						UpperCamelCase_		:							Tuple  =		BarkFineConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE		, """config.json"""			)			)
						UpperCamelCase_		:							Dict  =		EncodecConfig.from_pretrained("""facebook/encodec_24khz"""			)

						UpperCamelCase_		:							Dict  =		BarkSemanticModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Dict  =		BarkCoarseModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Tuple  =		BarkFineModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							int  =		EncodecModel.from_pretrained("""facebook/encodec_24khz"""			)

						UpperCamelCase_		:							List[Any]  =		BarkConfig.from_sub_model_configs(
						    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Dict  =		BarkGenerationConfig.from_sub_model_configs(
						    semantic.generation_config		, coarseAcoustic.generation_config		, fineAcoustic.generation_config			)

						UpperCamelCase_		:							str  =		BarkModel(_SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Dict  =		semantic
						UpperCamelCase_		:							List[str]  =		coarseAcoustic
						UpperCamelCase_		:							Dict  =		fineAcoustic
						UpperCamelCase_		:							Optional[Any]  =		codec

						UpperCamelCase_		:							List[Any]  =		bark_generation_config

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


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE		:     int         =	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		:     Optional[Any]         =	parser.parse_args()

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







from __future__ import annotations

SCREAMING_SNAKE_CASE		:     Tuple         =	1.6_021E-19  # units = C






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, ):
						if (conductivity, electron_conc, mobility).count(0			) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""			)
						elif conductivity < 0:
												raise ValueError("""Conductivity cannot be negative"""			)
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative"""			)
						elif mobility < 0:
												raise ValueError("""mobility cannot be negative"""			)
						elif conductivity == 0:
												return (
												    "conductivity",
												    mobility * electron_conc * ELECTRON_CHARGE,
												)
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    conductivity / (mobility * ELECTRON_CHARGE),
												)
						else:
												return (
												    "mobility",
												    conductivity / (electron_conc * ELECTRON_CHARGE),
												)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






