





'''simple docstring'''






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


A_		:  Tuple		 =						8



def        UpperCamelCase__		(						__magic_name__  :   str		,       __magic_name__  :   Optional[int]=BITS      )				->			List[Any]:
 '''simple docstring'''






 snake_case__     :					List[str]    =  x.device

 snake_case__     :					Union[str, Any]    =  (x * 2_55).int().clamp(0		,       2_55      )

 snake_case__     :					Optional[Any]    =  2 ** torch.arange(bits - 1		,       -1		,       -1		,       device=__magic_name__      )
 snake_case__     :					Union[str, Any]    =  rearrange(__magic_name__		,       """d -> d 1 1"""      )
 snake_case__     :					Any    =  rearrange(__magic_name__		,       """b c h w -> b c 1 h w"""      )

 snake_case__     :					List[str]    =  ((x & mask) != 0).float()
 snake_case__     :					Optional[int]    =  rearrange(__magic_name__		,       """b c d h w -> b (c d) h w"""      )
 snake_case__     :					Any    =  bits * 2 - 1
 return bits



def        UpperCamelCase__		(						__magic_name__  :   Tuple		,       __magic_name__  :   int=BITS      )				->			Optional[int]:
 '''simple docstring'''






 snake_case__     :					Optional[int]    =  x.device

 snake_case__     :					str    =  (x > 0).int()
 snake_case__     :					Union[str, Any]    =  2 ** torch.arange(bits - 1		,       -1		,       -1		,       device=__magic_name__		,       dtype=torch.intaa      )

 snake_case__     :					int    =  rearrange(__magic_name__		,       """d -> d 1 1"""      )
 snake_case__     :					Tuple    =  rearrange(__magic_name__		,       """b (c d) h w -> b c d h w"""		,       d=8      )
 snake_case__     :					List[Any]    =  reduce(x * mask		,       """b c d h w -> b c h w"""		,       """sum"""      )
 return (dec / 2_55).clamp(0.0		,       1.0      )



def        UpperCamelCase__		(						self  :   Dict		,       __magic_name__  :   torch.FloatTensor		,       __magic_name__  :   int		,       __magic_name__  :   torch.FloatTensor		,       __magic_name__  :   float = 0.0		,       __magic_name__  :   bool = True		,       __magic_name__  :   str=None		,       __magic_name__  :   bool = True		,       )				->			Union[DDIMSchedulerOutput, Tuple]:
 '''simple docstring'''






 if self.num_inference_steps is None:
  raise ValueError(
      """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""      )

 # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
 # Ideally, read DDIM paper in-detail understanding

 # Notation (<variable name> -> <name in paper>
 # - pred_noise_t -> e_theta(x_t, t)
 # - pred_original_sample -> f_theta(x_t, t) or x_0
 # - std_dev_t -> sigma_t
 # - eta -> η
 # - pred_sample_direction -> "direction pointing to x_t"
 # - pred_prev_sample -> "x_t-1"

 # 1. get previous step value (=t-1)
 snake_case__     :					List[Any]    =  timestep - self.config.num_train_timesteps // self.num_inference_steps

 # 2. compute alphas, betas
 snake_case__     :					Union[str, Any]    =  self.alphas_cumprod[timestep]
 snake_case__     :					Tuple    =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

 snake_case__     :					List[str]    =  1 - alpha_prod_t

 # 3. compute predicted original sample from predicted noise also called
 # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
 snake_case__     :					str    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

 # 4. Clip "predicted x_0"
 snake_case__     :					Dict    =  self.bit_scale
 if self.config.clip_sample:
  snake_case__     :					Any    =  torch.clamp(__magic_name__		,       -scale		,       __magic_name__      )

 # 5. compute variance: "sigma_t(η)" -> see formula (16)
 # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
 snake_case__     :					Any    =  self._get_variance(__magic_name__		,       __magic_name__      )
 snake_case__     :					Optional[Any]    =  eta * variance ** 0.5

 if use_clipped_model_output:
  # the model_output is always re-derived from the clipped x_0 in Glide
  snake_case__     :					int    =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

 # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
 snake_case__     :					str    =  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

 # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
 snake_case__     :					List[str]    =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

 if eta > 0:
  # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
  snake_case__     :					str    =  model_output.device if torch.is_tensor(__magic_name__      ) else """cpu"""
  snake_case__     :					Union[str, Any]    =  torch.randn(model_output.shape		,       dtype=model_output.dtype		,       generator=__magic_name__      ).to(__magic_name__      )
  snake_case__     :					int    =  self._get_variance(__magic_name__		,       __magic_name__      ) ** 0.5 * eta * noise

  snake_case__     :					List[Any]    =  prev_sample + variance

 if not return_dict:
  return (prev_sample,)

 return DDIMSchedulerOutput(prev_sample=__magic_name__		,       pred_original_sample=__magic_name__      )



def        UpperCamelCase__		(						self  :   str		,       __magic_name__  :   torch.FloatTensor		,       __magic_name__  :   int		,       __magic_name__  :   torch.FloatTensor		,       __magic_name__  :   Any="epsilon"		,       __magic_name__  :   List[str]=None		,       __magic_name__  :   bool = True		,       )				->			Union[DDPMSchedulerOutput, Tuple]:
 '''simple docstring'''






 snake_case__     :					List[str]    =  timestep

 if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
  snake_case__  ,							snake_case__     :					List[str]    =  torch.split(__magic_name__		,       sample.shape[1]		,       dim=1      )
 else:
  snake_case__     :					Dict    =  None

 # 1. compute alphas, betas
 snake_case__     :					List[Any]    =  self.alphas_cumprod[t]
 snake_case__     :					List[str]    =  self.alphas_cumprod[t - 1] if t > 0 else self.one
 snake_case__     :					Union[str, Any]    =  1 - alpha_prod_t
 snake_case__     :					List[Any]    =  1 - alpha_prod_t_prev

 # 2. compute predicted original sample from predicted noise also called
 # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
 if prediction_type == "epsilon":
  snake_case__     :					List[str]    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
 elif prediction_type == "sample":
  snake_case__     :					Tuple    =  model_output
 else:
  raise ValueError(f"Unsupported prediction_type {prediction_type}."      )

 # 3. Clip "predicted x_0"
 snake_case__     :					int    =  self.bit_scale
 if self.config.clip_sample:
  snake_case__     :					List[str]    =  torch.clamp(__magic_name__		,       -scale		,       __magic_name__      )

 # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
 # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
 snake_case__     :					int    =  (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
 snake_case__     :					Dict    =  self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

 # 5. Compute predicted previous sample µ_t
 # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
 snake_case__     :					List[str]    =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

 # 6. Add noise
 snake_case__     :					Tuple    =  0
 if t > 0:
  snake_case__     :					Optional[int]    =  torch.randn(
      model_output.size()		,       dtype=model_output.dtype		,       layout=model_output.layout		,       generator=__magic_name__      ).to(model_output.device      )
  snake_case__     :					Tuple    =  (self._get_variance(__magic_name__		,       predicted_variance=__magic_name__      ) ** 0.5) * noise

 snake_case__     :					Optional[Any]    =  pred_prev_sample + variance

 if not return_dict:
  return (pred_prev_sample,)

 return DDPMSchedulerOutput(prev_sample=__magic_name__		,       pred_original_sample=__magic_name__      )





class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   def __init__(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE = 1.0	,		):
    super().__init__()
    snake_case__     :					List[str]    =  bit_scale
    snake_case__     :					Optional[int]    =  (
        ddim_bit_scheduler_step if isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ) else ddpm_bit_scheduler_step
    )

    self.register_modules(unet=__SCREAMING_SNAKE_CASE	,		scheduler=__SCREAMING_SNAKE_CASE       )


   @torch.no_grad()
   def __call__(							self	,		__SCREAMING_SNAKE_CASE = 2_5_6	,		__SCREAMING_SNAKE_CASE = 2_5_6	,		__SCREAMING_SNAKE_CASE = 5_0	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = 1	,		__SCREAMING_SNAKE_CASE = "pil"	,		__SCREAMING_SNAKE_CASE = True	,		**__SCREAMING_SNAKE_CASE	,		):
    snake_case__     :					Tuple    =  torch.randn(
        (batch_size, self.unet.config.in_channels, height, width)	,		generator=__SCREAMING_SNAKE_CASE	,		)
    snake_case__     :					Dict    =  decimal_to_bits(__SCREAMING_SNAKE_CASE       ) * self.bit_scale
    snake_case__     :					Tuple    =  latents.to(self.device       )

    self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

    for t in self.progress_bar(self.scheduler.timesteps       ):
     # predict the noise residual
     snake_case__     :					str    =  self.unet(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ).sample

     # compute the previous noisy sample x_t -> x_t-1
     snake_case__     :					List[Any]    =  self.scheduler.step(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ).prev_sample

    snake_case__     :					int    =  bits_to_decimal(__SCREAMING_SNAKE_CASE       )

    if output_type == "pil":
     snake_case__     :					Any    =  self.numpy_to_pil(__SCREAMING_SNAKE_CASE       )

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE       )








'''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()
UpperCAmelCase_			:  Union[str, Any]										=   logging.get_logger(__name__)

set_seed(7_7_0)


UpperCAmelCase_			:  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',
}


UpperCAmelCase_			:  List[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',
    },
}

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


def       _lowercase							(					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       Tuple=False      ):
 __A	:		List[Any]	  =      model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCamelCase__,					REMOTE_MODEL_PATHS[key]['file_name']      )


def       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Any      ):
 os.makedirs(UpperCamelCase__,					exist_ok=UpperCamelCase__      )
 hf_hub_download(repo_id=UpperCamelCase__,					filename=UpperCamelCase__,					local_dir=UpperCamelCase__      )


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

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

 __A	:		Optional[Any]	  =      ConfigClass(**checkpoint['model_args']      )
 __A	:		Union[str, Any]	  =      ModelClass(config=UpperCamelCase__      )
 __A	:		Tuple	  =      GenerationConfigClass()

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

   __A	:		Optional[Any]	  =      state_dict.pop(UpperCamelCase__      )

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

 return model


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       Tuple=False,					UpperCamelCase__							:       Optional[Any]="text"      ):
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

 __A	:		Any	  =      'cpu'  # do conversion on cpu

 __A	:		int	  =      _get_ckpt_path(UpperCamelCase__,					use_small=UpperCamelCase__      )
 __A	:		Any	  =      _load_model(UpperCamelCase__,					UpperCamelCase__,					model_type=UpperCamelCase__,					use_small=UpperCamelCase__      )

 # load bark initial model
 __A	:		Optional[int]	  =      _bark_load_model(UpperCamelCase__,					'cpu',					model_type=UpperCamelCase__,					use_small=UpperCamelCase__      )

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

 if model.num_parameters(exclude_embeddings=UpperCamelCase__      ) != 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
 __A	:		Optional[Any]	  =      5
 __A	:		str	  =      10

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

  __A	:		Union[str, Any]	  =      model(UpperCamelCase__      )

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

 else:
  __A	:		Optional[int]	  =      3
  __A	:		int	  =      8
  __A	:		List[str]	  =      torch.randint(256,					(batch_size, sequence_length, n_codes_total),					dtype=torch.int      )

  __A	:		List[str]	  =      model(UpperCamelCase__,					UpperCamelCase__      )
  __A	:		Optional[int]	  =      bark_model(UpperCamelCase__,					UpperCamelCase__      )

  __A	:		Dict	  =      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(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
 model.save_pretrained(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int,					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Optional[Any],					UpperCamelCase__							:       Tuple,					UpperCamelCase__							:       str,					):
 __A	:		Optional[Any]	  =      os.path.join(UpperCamelCase__,					UpperCamelCase__      )

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

 __A	:		List[Any]	  =      BarkSemanticModel.from_pretrained(UpperCamelCase__      )
 __A	:		Optional[int]	  =      BarkCoarseModel.from_pretrained(UpperCamelCase__      )
 __A	:		str	  =      BarkFineModel.from_pretrained(UpperCamelCase__      )
 __A	:		Optional[int]	  =      EncodecModel.from_pretrained('facebook/encodec_24khz'      )

 __A	:		int	  =      BarkConfig.from_sub_model_configs(
     UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__      )

 __A	:		Optional[Any]	  =      BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config,					coarseAcoustic.generation_config,					fineAcoustic.generation_config      )

 __A	:		int	  =      BarkModel(UpperCamelCase__      )

 __A	:		List[str]	  =      semantic
 __A	:		Any	  =      coarseAcoustic
 __A	:		Tuple	  =      fineAcoustic
 __A	:		Optional[Any]	  =      codec

 __A	:		Dict	  =      bark_generation_config

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


if __name__ == "__main__":
 UpperCAmelCase_			:  Optional[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.')

 UpperCAmelCase_			:  Any										=   parser.parse_args()

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






