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(7_7_0)


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_     (		lowercase:							str  ,					lowercase:							List[str]=False		)    ->				Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= model_type
 if use_small:
  key += "_small"
 return os.path.join(lowercase  ,					REMOTE_MODEL_PATHS[key]['''file_name''']		)




def       lowerCAmelCase_     (		lowercase:							str  ,					lowercase:							Dict		)    ->				str:







 '''simple docstring'''
 os.makedirs(lowercase  ,					exist_ok=lowercase		)
 hf_hub_download(repo_id=lowercase  ,					filename=lowercase  ,					local_dir=lowercase		)




def       lowerCAmelCase_     (		lowercase:							str  ,					lowercase:							Union[str, Any]  ,					lowercase:							List[Any]=False  ,					lowercase:							List[Any]="text"		)    ->				Any:







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

 # convert Bark model arguments to HF Bark model arguments
 _UpperCamelCase:     Tuple							= model_args.pop('''n_head'''		)
 _UpperCamelCase:     List[str]							= model_args.pop('''n_embd'''		)
 _UpperCamelCase:     Optional[int]							= model_args.pop('''n_layer'''		)

 _UpperCamelCase:     List[str]							= ConfigClass(**checkpoint['''model_args''']		)
 _UpperCamelCase:     Dict							= ModelClass(config=lowercase		)
 _UpperCamelCase:     Optional[int]							= GenerationConfigClass()

 _UpperCamelCase:     List[str]							= model_generation_config
 _UpperCamelCase:     str							= 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:     str							= k[len(lowercase		) :]
   for old_layer_name in new_layer_name_dict:
    _UpperCamelCase:     Any							= new_k.replace(lowercase  ,					new_layer_name_dict[old_layer_name]		)

   _UpperCamelCase:     List[str]							= state_dict.pop(lowercase		)

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







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

 _UpperCamelCase:     List[str]							= '''cpu'''  # do conversion on cpu

 _UpperCamelCase:     int							= _get_ckpt_path(lowercase  ,					use_small=lowercase		)
 _UpperCamelCase:     List[str]							= _load_model(lowercase  ,					lowercase  ,					model_type=lowercase  ,					use_small=lowercase		)

 # load bark initial model
 _UpperCamelCase:     Tuple							= _bark_load_model(lowercase  ,					'''cpu'''  ,					model_type=lowercase  ,					use_small=lowercase		)

 if model_type == "text":
  _UpperCamelCase:     str							= 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:     Optional[Any]							= 5
 _UpperCamelCase:     Optional[Any]							= 10

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

  _UpperCamelCase:     Optional[int]							= model(lowercase		)

  # take last logits
  _UpperCamelCase:     Optional[Any]							= output_new_model_total.logits[:, [-1], :]

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

  _UpperCamelCase:     Optional[Any]							= model(lowercase  ,					lowercase		)
  _UpperCamelCase:     str							= bark_model(lowercase  ,					lowercase		)

  _UpperCamelCase:     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(lowercase		).mkdir(exist_ok=lowercase		)
 model.save_pretrained(lowercase		)




def       lowerCAmelCase_     (		lowercase:							int  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							Union[str, Any]  ,					lowercase:							Any  ,					lowercase:							int  ,					)    ->				Tuple:







 '''simple docstring'''
 _UpperCamelCase:     int							= os.path.join(lowercase  ,					lowercase		)

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

 _UpperCamelCase:     int							= BarkSemanticModel.from_pretrained(lowercase		)
 _UpperCamelCase:     Any							= BarkCoarseModel.from_pretrained(lowercase		)
 _UpperCamelCase:     Any							= BarkFineModel.from_pretrained(lowercase		)
 _UpperCamelCase:     Optional[Any]							= EncodecModel.from_pretrained('''facebook/encodec_24khz'''		)

 _UpperCamelCase:     List[str]							= BarkConfig.from_sub_model_configs(
     lowercase  ,					lowercase  ,					lowercase  ,					lowercase		)

 _UpperCamelCase:     Any							= BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config  ,					coarseAcoustic.generation_config  ,					fineAcoustic.generation_config		)

 _UpperCamelCase:     Optional[int]							= BarkModel(lowercase		)

 _UpperCamelCase:     str							= semantic
 _UpperCamelCase:     Any							= coarseAcoustic
 _UpperCamelCase:     str							= fineAcoustic
 _UpperCamelCase:     Tuple							= codec

 _UpperCamelCase:     List[Any]							= bark_generation_config

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


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)
import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase_			    =							'''1'''
UpperCAmelCase_			    =							'''0'''
UpperCAmelCase_			    =							'''1'''

UpperCAmelCase_			    =							ort.SessionOptions()
UpperCAmelCase_			    =							ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
UpperCAmelCase_			    =							['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
UpperCAmelCase_			    =							ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase_			    =							ort.RunOptions()

UpperCAmelCase_			    =							1_2_8
UpperCAmelCase_			    =							1
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
UpperCAmelCase_			    =							time.time()
UpperCAmelCase_			    =							2_0_0_0
UpperCAmelCase_			    =							{}
for iter in range(max_iters):
  UpperCAmelCase_			    =							sess.run(
      None,
      {
          sess.get_inputs()[0].name: input_ids,
          sess.get_inputs()[1].name: attention_mask,
          sess.get_inputs()[2].name: token_type_ids,
      },
      run_options=run_opt,
  )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1_0_0_0 / max_iters))