


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      		=			logging.get_logger(__name__)

set_seed(770)


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






def       lowerCamelCase					(a_						:Optional[int]				,				a_						:List[Any]=False)     ->							Tuple:
       lowercase		:Optional[Any]       							=				model_type
       if use_small:
              key += "_small"
       return os.path.join(a_				,				REMOTE_MODEL_PATHS[key]['''file_name'''])






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






def       lowerCamelCase					(a_						:List[Any]				,				a_						:Tuple				,				a_						:str=False				,				a_						:str="text")     ->							Union[str, Any]:
       if model_type == "text":
              lowercase		:Dict       							=				BarkSemanticModel
              lowercase		:Optional[int]       							=				BarkSemanticConfig
              lowercase		:Union[str, Any]       							=				BarkSemanticGenerationConfig
       elif model_type == "coarse":
              lowercase		:Optional[int]       							=				BarkCoarseModel
              lowercase		:Dict       							=				BarkCoarseConfig
              lowercase		:List[Any]       							=				BarkCoarseGenerationConfig
       elif model_type == "fine":
              lowercase		:Dict       							=				BarkFineModel
              lowercase		:Optional[int]       							=				BarkFineConfig
              lowercase		:Optional[Any]       							=				BarkFineGenerationConfig
       else:
              raise NotImplementedError()
       lowercase		:List[str]       							=				F"""{model_type}_small""" if use_small else model_type
       lowercase		:Any       							=				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		:str       							=				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		:Optional[Any]       							=				model_args['''vocab_size''']
              lowercase		:Optional[int]       							=				model_args['''vocab_size''']
              del model_args["vocab_size"]

       # convert Bark model arguments to HF Bark model arguments
       lowercase		:Dict       							=				model_args.pop('''n_head''')
       lowercase		:List[str]       							=				model_args.pop('''n_embd''')
       lowercase		:Dict       							=				model_args.pop('''n_layer''')

       lowercase		:List[Any]       							=				ConfigClass(**checkpoint['''model_args'''])
       lowercase		:List[str]       							=				ModelClass(config=a_)
       lowercase		:List[str]       							=				GenerationConfigClass()

       lowercase		:str       							=				model_generation_config
       lowercase		:Tuple       							=				checkpoint['''model''']
       # fixup checkpoint
       lowercase		:int       							=				'''_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[Any]       							=				k[len(a_) :]
                     for old_layer_name in new_layer_name_dict:
                            lowercase		:str       							=				new_k.replace(a_				,				new_layer_name_dict[old_layer_name])

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

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

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

       lowercase		:Dict       							=				_get_ckpt_path(a_				,				use_small=a_)
       lowercase		:List[Any]       							=				_load_model(a_				,				a_				,				model_type=a_				,				use_small=a_)

       # load bark initial model
       lowercase		:Optional[Any]       							=				_bark_load_model(a_				,				'''cpu'''				,				model_type=a_				,				use_small=a_)

       if model_type == "text":
              lowercase		:Optional[Any]       							=				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		:List[str]       							=				5
       lowercase		:Optional[int]       							=				10

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

              lowercase		:Any       							=				model(a_)

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

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

              lowercase		:Union[str, Any]       							=				model(a_				,				a_)
              lowercase		:List[str]       							=				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       lowerCamelCase					(a_						:str				,				a_						:str				,				a_						:Optional[int]				,				a_						:str				,				a_						:List[Any]				,				a_						:Optional[Any]				,				)     ->							int:
       lowercase		:List[str]       							=				os.path.join(a_				,				a_)

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

       lowercase		:str       							=				BarkSemanticModel.from_pretrained(a_)
       lowercase		:Union[str, Any]       							=				BarkCoarseModel.from_pretrained(a_)
       lowercase		:List[Any]       							=				BarkFineModel.from_pretrained(a_)
       lowercase		:Optional[int]       							=				EncodecModel.from_pretrained('''facebook/encodec_24khz''')

       lowercase		:Union[str, Any]       							=				BarkConfig.from_sub_model_configs(
           a_				,				a_				,				a_				,				a_)

       lowercase		:Union[str, Any]       							=				BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config				,				coarseAcoustic.generation_config				,				fineAcoustic.generation_config)

       lowercase		:List[Any]       							=				BarkModel(a_)

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

       lowercase		:Any       							=				bark_generation_config

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


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

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





"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      UNetaDModel
       __A			:      ScoreSdeVeScheduler






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    UNetaDModel  ,     snake_case__  :    ScoreSdeVeScheduler				):

              '''simple docstring'''






              super().__init__()
              self.register_modules(unet=snake_case__  ,     scheduler=snake_case__				)







       @torch.no_grad()
       def __call__(   self  :    Tuple  ,     snake_case__  :    int = 1  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     snake_case__  :    Optional[str] = "pil"  ,     snake_case__  :    bool = True  ,     **snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:str       							=				self.unet.config.sample_size
              lowercase		:str       							=				(batch_size, 3, img_size, img_size)

              lowercase		:Any       							=				self.unet

              lowercase		:Dict       							=				randn_tensor(snake_case__  ,     generator=snake_case__				) * self.scheduler.init_noise_sigma
              lowercase		:int       							=				sample.to(self.device				)

              self.scheduler.set_timesteps(snake_case__				)
              self.scheduler.set_sigmas(snake_case__				)

              for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
                     lowercase		:Optional[int]       							=				self.scheduler.sigmas[i] * torch.ones(shape[0]  ,     device=self.device				)

                     # correction step
                     for _ in range(self.scheduler.config.correct_steps				):
                            lowercase		:str       							=				self.unet(snake_case__  ,     snake_case__				).sample
                            lowercase		:Optional[Any]       							=				self.scheduler.step_correct(snake_case__  ,     snake_case__  ,     generator=snake_case__				).prev_sample

                     # prediction step
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample
                     lowercase		:Dict       							=				self.scheduler.step_pred(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				)

                     lowercase							,  lowercase		:Tuple       							=				output.prev_sample, output.prev_sample_mean

              lowercase		:str       							=				sample_mean.clamp(0  ,     1				)
              lowercase		:int       							=				sample.cpu().permute(0  ,     2  ,     3  ,     1				).numpy()
              if output_type == "pil":
                     lowercase		:str       							=				self.numpy_to_pil(snake_case__				)

              if not return_dict:
                     return (sample,)

              return ImagePipelineOutput(images=snake_case__				)

