
'''simple docstring'''
import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script







@require_cpu
class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self	):
				debug_launcher(test_script.main	)






			def 					lowerCAmelCase__					(       self	):
				debug_launcher(test_ops.main	)








"""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            =	logging.get_logger(__name__)

set_seed(7_70)


_lowerCAmelCase            =	{
    '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            =	{
    '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            =	os.path.dirname(os.path.abspath(__file__))
_lowerCAmelCase            =	os.path.join(os.path.expanduser('~'), '.cache')
_lowerCAmelCase            =	os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')


def      UpperCamelCase       ( _A  ,				_A=False )     ->       Dict:
				lowercase    :      Union[str, Any]       = model_type
				if use_small:
								key += "_small"
				return os.path.join(_A  ,				REMOTE_MODEL_PATHS[key]["""file_name"""] )


def      UpperCamelCase       ( _A  ,				_A )     ->       List[Any]:
				os.makedirs(_A  ,				exist_ok=_A )
				hf_hub_download(repo_id=_A  ,				filename=_A  ,				local_dir=_A )


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

				# convert Bark model arguments to HF Bark model arguments
				lowercase    :      Optional[int]       = model_args.pop("""n_head""" )
				lowercase    :      Union[str, Any]       = model_args.pop("""n_embd""" )
				lowercase    :      Dict       = model_args.pop("""n_layer""" )

				lowercase    :      List[str]       = ConfigClass(**checkpoint["""model_args"""] )
				lowercase    :      Dict       = ModelClass(config=_A )
				lowercase    :      List[str]       = GenerationConfigClass()

				lowercase    :      int       = model_generation_config
				lowercase    :      List[Any]       = checkpoint["""model"""]
				# fixup checkpoint
				lowercase    :      Union[str, Any]       = """_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    :      List[str]       = k[len(_A ) :]
												for old_layer_name in new_layer_name_dict:
																lowercase    :      int       = new_k.replace(_A  ,				new_layer_name_dict[old_layer_name] )

												lowercase    :      List[Any]       = state_dict.pop(_A )

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

				lowercase    :      Optional[Any]       = """cpu"""  # do conversion on cpu

				lowercase    :      Dict       = _get_ckpt_path(_A  ,				use_small=_A )
				lowercase    :      Any       = _load_model(_A  ,				_A  ,				model_type=_A  ,				use_small=_A )

				# load bark initial model
				lowercase    :      Tuple       = _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    :      Union[str, Any]       = 5
				lowercase    :      Tuple       = 10

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

								lowercase    :      Tuple       = model(_A )

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

				else:
								lowercase    :      Dict       = 3
								lowercase    :      Any       = 8
								lowercase    :      str       = torch.randint(256  ,				(batch_size, sequence_length, n_codes_total)  ,				dtype=torch.int )

								lowercase    :      str       = model(_A  ,				_A )
								lowercase    :      List[Any]       = bark_model(_A  ,				_A )

								lowercase    :      Any       = 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  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A  ,				)     ->       Optional[int]:
				lowercase    :      str       = os.path.join(_A  ,				_A )

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

				lowercase    :      Any       = BarkSemanticModel.from_pretrained(_A )
				lowercase    :      str       = BarkCoarseModel.from_pretrained(_A )
				lowercase    :      int       = BarkFineModel.from_pretrained(_A )
				lowercase    :      Optional[Any]       = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )

				lowercase    :      int       = BarkConfig.from_sub_model_configs(
				    _A  ,				_A  ,				_A  ,				_A )

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

				lowercase    :      str       = BarkModel(_A )

				lowercase    :      int       = semantic
				lowercase    :      List[Any]       = coarseAcoustic
				lowercase    :      Union[str, Any]       = fineAcoustic
				lowercase    :      Any       = codec

				lowercase    :      int       = bark_generation_config

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


if __name__ == "__main__":
				_lowerCAmelCase            =	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            =	parser.parse_args()

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