



'''simple docstring'''

import gc
import threading
import time

import psutil
import torch

class      __magic_name__						:







							def __init__(							self :	int		):
									_a					:				Optional[int]				=							psutil.Process()
									_a					:				str				=							False







							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				str				=							-1

									while True:
											_a					:				Dict				=							max(self.process.memory_info().rss     ,self.cpu_memory_peak		)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											if not self.peak_monitoring:
													break







							def 							__lowercase      (							self :	List[str]		):
									_a					:				List[str]				=							True
									_a					:				Tuple				=							threading.Thread(target=self.peak_monitor		)
									_a					:				Optional[Any]				=							True
									self.thread.start()







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Any				=							False
									self.thread.join()
									return self.cpu_memory_peak
__lowerCAmelCase						      =       PeakCPUMemory()



def 					__lowerCamelCase					(				)      ->				Tuple:
		# Time
		_a					:				Optional[int]				=							{'time': time.time()}

		gc.collect()
		torch.cuda.empty_cache()

		# CPU mem
		_a					:				Optional[int]				=							psutil.Process().memory_info().rss
		cpu_peak_tracker.start()

		# GPU mem
		for i in range(torch.cuda.device_count()  ):
				_a					:				Union[str, Any]				=							torch.cuda.memory_allocated(lowerCAmelCase_  )
		torch.cuda.reset_peak_memory_stats()

		return measures



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				List[Any]:
		# Time
		_a					:				Any				=							{'time': time.time() - start_measures['time']}

		gc.collect()
		torch.cuda.empty_cache()

		# CPU mem
		_a					:				List[str]				=							(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
		_a					:				Union[str, Any]				=							(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

		# GPU mem
		for i in range(torch.cuda.device_count()  ):
				_a					:				int				=							(torch.cuda.memory_allocated(lowerCAmelCase_  ) - start_measures[str(lowerCAmelCase_  )]) / 2**20
				_a					:				List[str]				=							(torch.cuda.max_memory_allocated(lowerCAmelCase_  ) - start_measures[str(lowerCAmelCase_  )]) / 2**20

		return measures



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Any:
		print(f"""{description}:"""  )
		print(f"""- Time: {measures['time']:.2f}s"""  )
		for i in range(torch.cuda.device_count()  ):
				print(f"""- GPU {i} allocated: {measures[str(lowerCAmelCase_  )]:.2f}MiB"""  )
				_a					:				Union[str, Any]				=							measures[f"""{i}-peak"""]
				print(f"""- GPU {i} peak: {peak:.2f}MiB"""  )
		print(f"""- CPU RAM allocated: {measures['cpu']:.2f}MiB"""  )
		print(f"""- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"""  )









'''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()
__lowerCAmelCase						      =       logging.get_logger(__name__)

set_seed(770)


__lowerCAmelCase						      =       {
    '''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''',
}


__lowerCAmelCase						      =       {
    '''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''',
    },
}

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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Optional[int]:
		_a					:				int				=							model_type
		if use_small:
				key += "_small"
		return os.path.join(lowerCAmelCase_						,      REMOTE_MODEL_PATHS[key]['file_name']  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		os.makedirs(lowerCAmelCase_						,      exist_ok=lowerCAmelCase_  )
		hf_hub_download(repo_id=lowerCAmelCase_						,      filename=lowerCAmelCase_						,      local_dir=lowerCAmelCase_  )



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

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

		_a					:				Optional[int]				=							ConfigClass(**checkpoint['model_args']  )
		_a					:				List[str]				=							ModelClass(config=lowerCAmelCase_  )
		_a					:				Tuple				=							GenerationConfigClass()

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

						_a					:				List[Any]				=							state_dict.pop(lowerCAmelCase_  )

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

		return model



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_="text"  )      ->				List[Any]:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		_a					:				Optional[int]				=							'cpu'  # do conversion on cpu

		_a					:				Tuple				=							_get_ckpt_path(lowerCAmelCase_						,      use_small=lowerCAmelCase_  )
		_a					:				List[Any]				=							_load_model(lowerCAmelCase_						,      lowerCAmelCase_						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

		# load bark initial model
		_a					:				Any				=							_bark_load_model(lowerCAmelCase_						,      'cpu'						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

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

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

		if model_type in ["text", "coarse"]:
				_a					:				Dict				=							torch.randint(256						,      (batch_size, sequence_length)						,      dtype=torch.int  )
				_a					:				Dict				=							bark_model(lowerCAmelCase_  )[0]

				_a					:				Tuple				=							model(lowerCAmelCase_  )

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

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

				_a					:				Union[str, Any]				=							model(lowerCAmelCase_						,      lowerCAmelCase_  )
				_a					:				int				=							bark_model(lowerCAmelCase_						,      lowerCAmelCase_  )

				_a					:				List[str]				=							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(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		model.save_pretrained(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				Any:
		_a					:				Any				=							os.path.join(lowerCAmelCase_						,      lowerCAmelCase_  )

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

		_a					:				str				=							BarkSemanticModel.from_pretrained(lowerCAmelCase_  )
		_a					:				Dict				=							BarkCoarseModel.from_pretrained(lowerCAmelCase_  )
		_a					:				int				=							BarkFineModel.from_pretrained(lowerCAmelCase_  )
		_a					:				List[Any]				=							EncodecModel.from_pretrained('facebook/encodec_24khz'  )

		_a					:				Any				=							BarkConfig.from_sub_model_configs(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				List[str]				=							BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config						,      coarseAcoustic.generation_config						,      fineAcoustic.generation_config  )

		_a					:				Optional[Any]				=							BarkModel(lowerCAmelCase_  )

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

		_a					:				List[Any]				=							bark_generation_config

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


if __name__ == "__main__":
							__lowerCAmelCase						      =       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.''')

							__lowerCAmelCase						      =       parser.parse_args()

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




