





"""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_   :   List[str]        =       logging.get_logger(__name__)

set_seed(7_7_0)


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



def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		Tuple=False	):




					'''simple docstring'''






					UpperCAmelCase			:   str      =				model_type
					if use_small:
										key += "_small"
					return os.path.join(_lowercase							,      REMOTE_MODEL_PATHS[key]["file_name"]	)



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		Any	):




					'''simple docstring'''






					os.makedirs(_lowercase							,      exist_ok=_lowercase	)
					hf_hub_download(repo_id=_lowercase							,      filename=_lowercase							,      local_dir=_lowercase	)



def   lowercase_	(    _lowercase       :		Any							,      _lowercase       :		Optional[Any]							,      _lowercase       :		str=False							,      _lowercase       :		List[str]="text"	):




					'''simple docstring'''






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

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

					UpperCAmelCase			:   str      =				ConfigClass(**checkpoint["model_args"]	)
					UpperCAmelCase			:   Any      =				ModelClass(config=_lowercase	)
					UpperCAmelCase			:   str      =				GenerationConfigClass()

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

															UpperCAmelCase			:   Optional[int]      =				state_dict.pop(_lowercase	)

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

					return model



def   lowercase_	(    _lowercase       :		Any							,      _lowercase       :		Tuple=False							,      _lowercase       :		List[Any]="text"	):




					'''simple docstring'''






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

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

					UpperCAmelCase			:   Union[str, Any]      =				_get_ckpt_path(_lowercase							,      use_small=_lowercase	)
					UpperCAmelCase			:   Dict      =				_load_model(_lowercase							,      _lowercase							,      model_type=_lowercase							,      use_small=_lowercase	)

					# load bark initial model
					UpperCAmelCase			:   Union[str, Any]      =				_bark_load_model(_lowercase							,      "cpu"							,      model_type=_lowercase							,      use_small=_lowercase	)

					if model_type == "text":
										UpperCAmelCase			:   Any      =				bark_model["""model"""]

					if model.num_parameters(exclude_embeddings=_lowercase	) != 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			:   List[str]      =				5
					UpperCAmelCase			:   Any      =				10

					if model_type in ["text", "coarse"]:
										UpperCAmelCase			:   Optional[int]      =				torch.randint(2_56							,      (batch_size, sequence_length)							,      dtype=torch.int	)
										UpperCAmelCase			:   Dict      =				bark_model(_lowercase	)[0]

										UpperCAmelCase			:   Optional[Any]      =				model(_lowercase	)

										# take last logits
										UpperCAmelCase			:   List[str]      =				output_new_model_total.logits[:, [-1], :]

					else:
										UpperCAmelCase			:   str      =				3
										UpperCAmelCase			:   Any      =				8
										UpperCAmelCase			:   Tuple      =				torch.randint(2_56							,      (batch_size, sequence_length, n_codes_total)							,      dtype=torch.int	)

										UpperCAmelCase			:   Optional[int]      =				model(_lowercase							,      _lowercase	)
										UpperCAmelCase			:   List[Any]      =				bark_model(_lowercase							,      _lowercase	)

										UpperCAmelCase			:   Optional[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(_lowercase	).mkdir(exist_ok=_lowercase	)
					model.save_pretrained(_lowercase	)



def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		List[Any]							,      _lowercase       :		Dict							,      _lowercase       :		int							,      _lowercase       :		str							,      _lowercase       :		int							,      ):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, Any]      =				os.path.join(_lowercase							,      _lowercase	)

					UpperCAmelCase			:   List[Any]      =				BarkSemanticConfig.from_pretrained(os.path.join(_lowercase							,      "config.json"	)	)
					UpperCAmelCase			:   Optional[int]      =				BarkCoarseConfig.from_pretrained(os.path.join(_lowercase							,      "config.json"	)	)
					UpperCAmelCase			:   Tuple      =				BarkFineConfig.from_pretrained(os.path.join(_lowercase							,      "config.json"	)	)
					UpperCAmelCase			:   Optional[Any]      =				EncodecConfig.from_pretrained("facebook/encodec_24khz"	)

					UpperCAmelCase			:   int      =				BarkSemanticModel.from_pretrained(_lowercase	)
					UpperCAmelCase			:   Any      =				BarkCoarseModel.from_pretrained(_lowercase	)
					UpperCAmelCase			:   int      =				BarkFineModel.from_pretrained(_lowercase	)
					UpperCAmelCase			:   Union[str, Any]      =				EncodecModel.from_pretrained("facebook/encodec_24khz"	)

					UpperCAmelCase			:   Optional[Any]      =				BarkConfig.from_sub_model_configs(
					    _lowercase							,      _lowercase							,      _lowercase							,      _lowercase	)

					UpperCAmelCase			:   Optional[int]      =				BarkGenerationConfig.from_sub_model_configs(
					    semantic.generation_config							,      coarseAcoustic.generation_config							,      fineAcoustic.generation_config	)

					UpperCAmelCase			:   List[Any]      =				BarkModel(_lowercase	)

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

					UpperCAmelCase			:   str      =				bark_generation_config

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


if __name__ == "__main__":
	snake_case_   :   Tuple        =       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_   :   str        =       parser.parse_args()

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





"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput







@dataclass
class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						42







class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						1







			@register_to_config
			def __init__(			self			: Optional[int]			,  lowercase			: int = 20_00			,  lowercase			: float = 0.1_5			,  lowercase			: float = 0.0_1			,  lowercase			: float = 1_3_4_8.0			,  lowercase			: float = 1E-5			,  lowercase			: int = 1			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				sigma_max

								# setable values
								UpperCAmelCase			:   Any      =				None

								self.set_sigmas(lowercase			,  lowercase			,  lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[int] = None  ):





								'''simple docstring'''



								return sample







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: int			,  lowercase			: float = None			,  lowercase			: Union[str, torch.device] = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

								UpperCAmelCase			:   Tuple      =				torch.linspace(1			,  lowercase			,  lowercase			,  device=lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: int			,  lowercase			: float = None			,  lowercase			: float = None			,  lowercase			: float = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				sigma_min if sigma_min is not None else self.config.sigma_min
								UpperCAmelCase			:   List[str]      =				sigma_max if sigma_max is not None else self.config.sigma_max
								UpperCAmelCase			:   Union[str, Any]      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
													self.set_timesteps(lowercase			,  lowercase  )

								UpperCAmelCase			:   List[Any]      =				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								UpperCAmelCase			:   Any      =				torch.exp(torch.linspace(math.log(lowercase  )			,  math.log(lowercase  )			,  lowercase  )  )
								UpperCAmelCase			:   str      =				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Dict			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								return torch.where(
								    timesteps == 0			,  torch.zeros_like(t.to(timesteps.device  )  )			,  self.discrete_sigmas[timesteps - 1].to(timesteps.device  )			,  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: int			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[torch.Generator] = None			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

								UpperCAmelCase			:   Any      =				timestep * torch.ones(
								    sample.shape[0]			,  device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
								UpperCAmelCase			:   Dict      =				(timestep * (len(self.timesteps  ) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								UpperCAmelCase			:   List[str]      =				timesteps.to(self.discrete_sigmas.device  )

								UpperCAmelCase			:   Optional[Any]      =				self.discrete_sigmas[timesteps].to(sample.device  )
								UpperCAmelCase			:   Tuple      =				self.get_adjacent_sigma(lowercase			,  lowercase  ).to(sample.device  )
								UpperCAmelCase			:   Optional[int]      =				torch.zeros_like(lowercase  )
								UpperCAmelCase			:   Union[str, Any]      =				(sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								UpperCAmelCase			:   List[Any]      =				diffusion.flatten()
								while len(diffusion.shape  ) < len(sample.shape  ):
													UpperCAmelCase			:   Optional[int]      =				diffusion.unsqueeze(-1  )
								UpperCAmelCase			:   Optional[Any]      =				drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								UpperCAmelCase			:   List[Any]      =				randn_tensor(
								    sample.shape			,  layout=sample.layout			,  generator=lowercase			,  device=sample.device			,  dtype=sample.dtype  )
								UpperCAmelCase			:   int      =				sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								UpperCAmelCase			:   Tuple      =				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
													return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowercase			,  prev_sample_mean=lowercase  )







			def 			__lowerCAmelCase				(			self			: List[str]			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[torch.Generator] = None			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								UpperCAmelCase			:   str      =				randn_tensor(sample.shape			,  layout=sample.layout			,  generator=lowercase  ).to(sample.device  )

								# compute step size from the model_output, the noise, and the snr
								UpperCAmelCase			:   Tuple      =				torch.norm(model_output.reshape(model_output.shape[0]			,  -1  )			,  dim=-1  ).mean()
								UpperCAmelCase			:   List[Any]      =				torch.norm(noise.reshape(noise.shape[0]			,  -1  )			,  dim=-1  ).mean()
								UpperCAmelCase			:   Tuple      =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
								UpperCAmelCase			:   int      =				step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								UpperCAmelCase			:   int      =				step_size.flatten()
								while len(step_size.shape  ) < len(sample.shape  ):
													UpperCAmelCase			:   Union[str, Any]      =				step_size.unsqueeze(-1  )
								UpperCAmelCase			:   Optional[Any]      =				sample + step_size * model_output
								UpperCAmelCase			:   Union[str, Any]      =				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
													return (prev_sample,)

								return SchedulerOutput(prev_sample=lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				timesteps.to(original_samples.device  )
								UpperCAmelCase			:   Optional[Any]      =				self.discrete_sigmas.to(original_samples.device  )[timesteps]
								UpperCAmelCase			:   List[str]      =				(
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowercase  ) * sigmas[:, None, None, None]
								)
								UpperCAmelCase			:   Optional[Any]      =				noise + original_samples
								return noisy_samples







			def __len__(			self			: List[str]  ):





								'''simple docstring'''



								return self.config.num_train_timesteps


