






'''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()
A:      Dict  				=				logging.get_logger(__name__)

set_seed(7_7_0)


A:      Optional[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",
}


A:      Dict  				=				{
    "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",
    },
}

A:      Dict  				=				os.path.dirname(os.path.abspath(__file__))
A:      Tuple  				=				os.path.join(os.path.expanduser("~"), ".cache")
A:      List[str]  				=				os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	Union[str, Any]=False							)			->			Dict:





						"""simple docstring"""
						lowercase_      :   Optional[Any]        =			model_type
						if use_small:
												key += "_small"
						return os.path.join(a						,			REMOTE_MODEL_PATHS[key]['file_name']							)






def 	_UpperCAmelCase    (  a		:	List[str]						,			a		:	Any							)			->			Any:





						"""simple docstring"""
						os.makedirs(a						,			exist_ok=a							)
						hf_hub_download(repo_id=a						,			filename=a						,			local_dir=a							)






def 	_UpperCAmelCase    (  a		:	Dict						,			a		:	Optional[Any]						,			a		:	Optional[int]=False						,			a		:	int="text"							)			->			Union[str, Any]:





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

						# convert Bark model arguments to HF Bark model arguments
						lowercase_      :   List[Any]        =			model_args.pop('n_head'							)
						lowercase_      :   Optional[int]        =			model_args.pop('n_embd'							)
						lowercase_      :   Optional[int]        =			model_args.pop('n_layer'							)

						lowercase_      :   Union[str, Any]        =			ConfigClass(**checkpoint['model_args']							)
						lowercase_      :   Union[str, Any]        =			ModelClass(config=a							)
						lowercase_      :   Any        =			GenerationConfigClass()

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

																		lowercase_      :   Dict        =			state_dict.pop(a							)

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

						return model






def 	_UpperCAmelCase    (  a		:	Any						,			a		:	Dict=False						,			a		:	List[str]="text"							)			->			List[str]:





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

						lowercase_      :   Tuple        =			'cpu'  # do conversion on cpu

						lowercase_      :   Optional[Any]        =			_get_ckpt_path(a						,			use_small=a							)
						lowercase_      :   int        =			_load_model(a						,			a						,			model_type=a						,			use_small=a							)

						# load bark initial model
						lowercase_      :   str        =			_bark_load_model(a						,			'cpu'						,			model_type=a						,			use_small=a							)

						if model_type == "text":
												lowercase_      :   str        =			bark_model['model']

						if model.num_parameters(exclude_embeddings=a							) != 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_      :   Any        =			5
						lowercase_      :   Optional[Any]        =			1_0

						if model_type in ["text", "coarse"]:
												lowercase_      :   Dict        =			torch.randint(2_5_6						,			(batch_size, sequence_length)						,			dtype=torch.int							)
												lowercase_      :   Any        =			bark_model(a							)[0]

												lowercase_      :   Optional[Any]        =			model(a							)

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

						else:
												lowercase_      :   Any        =			3
												lowercase_      :   Dict        =			8
												lowercase_      :   Dict        =			torch.randint(2_5_6						,			(batch_size, sequence_length, n_codes_total)						,			dtype=torch.int							)

												lowercase_      :   Any        =			model(a						,			a							)
												lowercase_      :   List[Any]        =			bark_model(a						,			a							)

												lowercase_      :   Tuple        =			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(a							).mkdir(exist_ok=a							)
						model.save_pretrained(a							)






def 	_UpperCAmelCase    (  a		:	Any						,			a		:	Tuple						,			a		:	Union[str, Any]						,			a		:	Union[str, Any]						,			a		:	Dict						,			a		:	Union[str, Any]						,			)			->			List[str]:





						"""simple docstring"""
						lowercase_      :   str        =			os.path.join(a						,			a							)

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

						lowercase_      :   List[str]        =			BarkSemanticModel.from_pretrained(a							)
						lowercase_      :   List[Any]        =			BarkCoarseModel.from_pretrained(a							)
						lowercase_      :   Any        =			BarkFineModel.from_pretrained(a							)
						lowercase_      :   Any        =			EncodecModel.from_pretrained('facebook/encodec_24khz'							)

						lowercase_      :   Optional[Any]        =			BarkConfig.from_sub_model_configs(
						    a						,			a						,			a						,			a							)

						lowercase_      :   Tuple        =			BarkGenerationConfig.from_sub_model_configs(
						    semantic.generation_config						,			coarseAcoustic.generation_config						,			fineAcoustic.generation_config							)

						lowercase_      :   Any        =			BarkModel(a							)

						lowercase_      :   List[Any]        =			semantic
						lowercase_      :   Any        =			coarseAcoustic
						lowercase_      :   Optional[Any]        =			fineAcoustic
						lowercase_      :   int        =			codec

						lowercase_      :   Tuple        =			bark_generation_config

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


if __name__ == "__main__":
						A:      Union[str, 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.")

						A:      Any  				=				parser.parse_args()

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








'''simple docstring'''

import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   Tuple        =			[
							    'safety_checker/pytorch_model.bin',
							    'safety_checker/model.safetensors',
							    'vae/diffusion_pytorch_model.bin',
							    'vae/diffusion_pytorch_model.safetensors',
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertTrue(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   Tuple        =			[
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertTrue(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   int        =			[
							    'safety_checker/pytorch_model.bin',
							    'safety_checker/model.safetensors',
							    'vae/diffusion_pytorch_model.bin',
							    'vae/diffusion_pytorch_model.safetensors',
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							    'unet/diffusion_pytorch_model.bin',
							    # Removed: 'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertFalse(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Dict        =			[
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							]
							self.assertTrue(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   List[Any]        =			[
							    'safety_checker/pytorch_model.bin',
							    'safety_checker/model.safetensors',
							    'vae/diffusion_pytorch_model.bin',
							    'vae/diffusion_pytorch_model.safetensors',
							    'text_encoder/pytorch_model.bin',
							    # Removed: 'text_encoder/model.safetensors',
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertFalse(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_      :   str        =			[
							    'safety_checker/pytorch_model.fp16.bin',
							    'safety_checker/model.fp16.safetensors',
							    'vae/diffusion_pytorch_model.fp16.bin',
							    'vae/diffusion_pytorch_model.fp16.safetensors',
							    'text_encoder/pytorch_model.fp16.bin',
							    'text_encoder/model.fp16.safetensors',
							    'unet/diffusion_pytorch_model.fp16.bin',
							    'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Union[str, Any]        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Optional[int]        =			[
							    'unet/diffusion_pytorch_model.fp16.bin',
							    'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  int:
							# pass variant but use the non-variant filenames
							lowercase_      :   Optional[int]        =			[
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   int        =			[
							    'safety_checker/pytorch_model.fp16.bin',
							    'safety_checker/model.fp16.safetensors',
							    'vae/diffusion_pytorch_model.fp16.bin',
							    'vae/diffusion_pytorch_model.fp16.safetensors',
							    'text_encoder/pytorch_model.fp16.bin',
							    'text_encoder/model.fp16.safetensors',
							    'unet/diffusion_pytorch_model.fp16.bin',
							    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertFalse(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   str        =			[
							    'text_encoder/pytorch_model.fp16.bin',
							    'text_encoder/model.fp16.safetensors',
							]
							lowercase_      :   str        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							# pass variant but use the non-variant filenames
							lowercase_      :   List[Any]        =			[
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )







	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   Union[str, Any]        =			[
							    'safety_checker/pytorch_model.fp16.bin',
							    'safety_checker/model.fp16.safetensors',
							    'vae/diffusion_pytorch_model.fp16.bin',
							    'vae/diffusion_pytorch_model.fp16.safetensors',
							    'text_encoder/pytorch_model.fp16.bin',
							    # 'text_encoder/model.fp16.safetensors',
							    'unet/diffusion_pytorch_model.fp16.bin',
							    'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertFalse(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
