
'''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()
_snake_case				:   str     =				logging.get_logger(__name__)

set_seed(770)


_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""",
}


_snake_case				:   Optional[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""",
    },
}

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Tuple=False  ):
						_SCREAMING_SNAKE_CASE				= model_type
						if use_small:
												key += "_small"
						return os.path.join(_SCREAMING_SNAKE_CASE	,			REMOTE_MODEL_PATHS[key]["file_name"]  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_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 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Any=False	,			_SCREAMING_SNAKE_CASE		:   Tuple="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(_SCREAMING_SNAKE_CASE  ):
												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(_SCREAMING_SNAKE_CASE	,			map_location=_SCREAMING_SNAKE_CASE  )
						# 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=_SCREAMING_SNAKE_CASE  )
						_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(_SCREAMING_SNAKE_CASE  ):
																		# replace part of the key with corresponding layer name in HF implementation
																		_SCREAMING_SNAKE_CASE				= k[len(_SCREAMING_SNAKE_CASE  ) :]
																		for old_layer_name in new_layer_name_dict:
																								_SCREAMING_SNAKE_CASE				= new_k.replace(_SCREAMING_SNAKE_CASE	,			new_layer_name_dict[old_layer_name]  )

																		_SCREAMING_SNAKE_CASE				= state_dict.pop(_SCREAMING_SNAKE_CASE  )

						_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(_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  )
						_SCREAMING_SNAKE_CASE				= model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= 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 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=False	,			_SCREAMING_SNAKE_CASE		:   List[Any]="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(_SCREAMING_SNAKE_CASE	,			use_small=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= _load_model(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			model_type=_SCREAMING_SNAKE_CASE	,			use_small=_SCREAMING_SNAKE_CASE  )

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

						if model_type == "text":
												_SCREAMING_SNAKE_CASE				= 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
						_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(_SCREAMING_SNAKE_CASE  )[0]

												_SCREAMING_SNAKE_CASE				= model(_SCREAMING_SNAKE_CASE  )

												# 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(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= bark_model(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[int]	,			):
						_SCREAMING_SNAKE_CASE				= os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

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

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

						_SCREAMING_SNAKE_CASE				= BarkConfig.from_sub_model_configs(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

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

						_SCREAMING_SNAKE_CASE				= BarkModel(_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= semantic
						_SCREAMING_SNAKE_CASE				= coarseAcoustic
						_SCREAMING_SNAKE_CASE				= fineAcoustic
						_SCREAMING_SNAKE_CASE				= codec

						_SCREAMING_SNAKE_CASE				= 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__":
							_snake_case				:   List[str]     =				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.""")

							_snake_case				:   Tuple     =				parser.parse_args()

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

'''simple docstring'''







# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_snake_case				:   Optional[Any]     =				abspath(join(dirname(dirname(dirname(__file__))), """src"""))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple  ):
						from transformers.testing_utils import pytest_addoption_shared

						pytest_addoption_shared(_SCREAMING_SNAKE_CASE  )




def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						from transformers.testing_utils import pytest_terminal_summary_main

						_SCREAMING_SNAKE_CASE				= terminalreporter.config.getoption("--make-reports"  )
						if make_reports:
												pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE	,			id=_SCREAMING_SNAKE_CASE  )