


'''simple docstring'''

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
							from fairscale.optim import OSS


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)

lowerCAmelCase				:Any        =      {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Tuple  ,					_A		:						Union[str, Any]=None  ,					_A		:						Tuple=None  ,					*_A		:						Dict  ,					**_A		:						Optional[Any]      )  ->			Tuple:
			super().__init__(*_A  ,					**_A      )

			if config is None:
				assert isinstance(self.model  ,					_A      ), (
				    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
				    F' {self.model.__class__}'
				)
				__magic_name__	:   Tuple					     =					self.model.config
			else:
				__magic_name__	:   Optional[Any]					     =					config

			__magic_name__	:   str					     =					data_args
			__magic_name__	:   Any					     =					self.config.tgt_vocab_size if isinstance(self.config  ,					_A      ) else self.config.vocab_size

			if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
				assert self.config.pad_token_id is not None, (
				    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
				    " calculation or doing label smoothing."
				)

			if self.config.pad_token_id is None and self.config.eos_token_id is not None:
				logger.warning(
				    F'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
				    ' padding..'      )

			if self.args.label_smoothing == 0:
				__magic_name__	:   List[str]					     =					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id      )
			else:
				# dynamically import label_smoothed_nll_loss
				from utils import label_smoothed_nll_loss

				__magic_name__	:   int					     =					label_smoothed_nll_loss







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						int      )  ->			str:
			if self.optimizer is None:
				__magic_name__	:   Optional[int]					     =					['bias', 'LayerNorm.weight']
				__magic_name__	:   Any					     =					[
				    {
				        'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay      )],
				        'weight_decay': self.args.weight_decay,
				    },
				    {
				        'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay      )],
				        'weight_decay': 0.0,
				    },
				]
				__magic_name__	:   List[Any]					     =					Adafactor if self.args.adafactor else AdamW
				if self.args.adafactor:
					__magic_name__	:   int					     =					Adafactor
					__magic_name__	:   Tuple					     =					{'scale_parameter': False, 'relative_step': False}
				else:
					__magic_name__	:   str					     =					AdamW
					__magic_name__	:   Tuple					     =					{
					    'betas': (self.args.adam_betaa, self.args.adam_betaa),
					    'eps': self.args.adam_epsilon,
					}
				__magic_name__	:   List[Any]					     =					self.args.learning_rate
				if self.sharded_ddp:
					__magic_name__	:   Any					     =					OSS(
					    params=_A  ,					optim=_A  ,					**_A  ,					)
				else:
					__magic_name__	:   Dict					     =					optimizer_cls(_A  ,					**_A      )

			if self.lr_scheduler is None:
				__magic_name__	:   Tuple					     =					self._get_lr_scheduler(_A      )
			else:  # ignoring --lr_scheduler
				logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Tuple      )  ->			str:
			__magic_name__	:   int					     =					arg_to_scheduler[self.args.lr_scheduler]
			if self.args.lr_scheduler == "constant":
				__magic_name__	:   Dict					     =					schedule_func(self.optimizer      )
			elif self.args.lr_scheduler == "constant_w_warmup":
				__magic_name__	:   List[str]					     =					schedule_func(self.optimizer  ,					num_warmup_steps=self.args.warmup_steps      )
			else:
				__magic_name__	:   Dict					     =					schedule_func(
				    self.optimizer  ,					num_warmup_steps=self.args.warmup_steps  ,					num_training_steps=_A      )
			return scheduler







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Optional[torch.utils.data.Sampler]:
			if isinstance(self.train_dataset  ,					torch.utils.data.IterableDataset      ):
				return None
			elif is_torch_tpu_available():
				return get_tpu_sampler(self.train_dataset      )
			else:
				if self.args.sortish_sampler:
					self.train_dataset.make_sortish_sampler(
					    self.args.per_device_train_batch_size  ,					distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)  ,					)

				return (
				    RandomSampler(self.train_dataset      )
				    if self.args.local_rank == -1
				    else DistributedSampler(self.train_dataset      )
				)







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Optional[int]  ,					_A		:						Any  ,					_A		:						Optional[Any]      )  ->			str:
			if self.args.label_smoothing == 0:
				if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
					# force training to ignore pad token
					__magic_name__	:   Dict					     =					model(**_A  ,					use_cache=_A      )[0]
					__magic_name__	:   Tuple					     =					self.loss_fn(logits.view(-1  ,					logits.shape[-1]      )  ,					labels.view(-1      )      )
				else:
					# compute usual loss via models
					__magic_name__     ,    __magic_name__	:   List[Any]					     =					model(**_A  ,					labels=_A  ,					use_cache=_A      )[:2]
			else:
				# compute label smoothed loss
				__magic_name__	:   List[Any]					     =					model(**_A  ,					use_cache=_A      )[0]
				__magic_name__	:   List[str]					     =					torch.nn.functional.log_softmax(_A  ,					dim=-1      )
				__magic_name__     ,    __magic_name__	:   Any					     =					self.loss_fn(_A  ,					_A  ,					self.args.label_smoothing  ,					ignore_index=self.config.pad_token_id      )
			return loss, logits







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						Any  ,					_A		:						int      )  ->			Union[str, Any]:
			__magic_name__	:   Tuple					     =					inputs.pop('labels'      )
			__magic_name__     ,    __magic_name__	:   Dict					     =					self._compute_loss(_A  ,					_A  ,					_A      )
			return loss







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						nn.Module  ,					_A		:						Dict[str, Union[torch.Tensor, Any]]  ,					_A		:						bool  ,					_A		:						Optional[List[str]] = None  ,					)  ->			Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
			__magic_name__	:   Optional[int]					     =					self._prepare_inputs(_A      )

			__magic_name__	:   Optional[Any]					     =					{
			    'max_length': self.data_args.val_max_target_length
			    if self.data_args is not None
			    else self.config.max_length,
			    'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
			}

			if self.args.predict_with_generate and not self.args.prediction_loss_only:
				__magic_name__	:   Tuple					     =					self.model.generate(
				    inputs['input_ids']  ,					attention_mask=inputs['attention_mask']  ,					**_A  ,					)
				# in case the batch is shorter than max length, the output should be padded
				if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
					__magic_name__	:   Union[str, Any]					     =					self._pad_tensors_to_max_len(_A  ,					gen_kwargs['max_length']      )

			__magic_name__	:   Union[str, Any]					     =					inputs.pop('labels'      )
			with torch.no_grad():
				# compute loss on predict data
				__magic_name__     ,    __magic_name__	:   Any					     =					self._compute_loss(_A  ,					_A  ,					_A      )

			__magic_name__	:   List[str]					     =					loss.mean().detach()
			if self.args.prediction_loss_only:
				return (loss, None, None)

			__magic_name__	:   List[Any]					     =					generated_tokens if self.args.predict_with_generate else logits

			if labels.shape[-1] < gen_kwargs["max_length"]:
				__magic_name__	:   str					     =					self._pad_tensors_to_max_len(_A  ,					gen_kwargs['max_length']      )

			return (loss, logits, labels)







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						Dict  ,					_A		:						Tuple      )  ->			Union[str, Any]:
			# If PAD token is not defined at least EOS token has to be defined
			__magic_name__	:   Optional[int]					     =					self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

			if pad_token_id is None:
				raise ValueError(
				    'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
				    F' padded to `max_length`={max_length}'      )

			__magic_name__	:   Union[str, Any]					     =					pad_token_id * torch.ones(
			    (tensor.shape[0], max_length)  ,					dtype=tensor.dtype  ,					device=tensor.device      )
			__magic_name__	:   List[str]					     =					tensor
			return padded_tensor



'''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				:Optional[int]        =      logging.get_logger(__name__)

set_seed(7_7_0)


lowerCAmelCase				: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''',
}


lowerCAmelCase				: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''',
    },
}

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





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]=False		):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Optional[Any]=False		,		lowerCAmelCase    :     str="text"		):





	"""simple docstring"""







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

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

	__magic_name__	:   Optional[Any]					     =					ConfigClass(**checkpoint['model_args']		)
	__magic_name__	:   Any					     =					ModelClass(config=lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					GenerationConfigClass()

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

			__magic_name__	:   Union[str, Any]					     =					state_dict.pop(lowerCAmelCase		)

	__magic_name__	:   Optional[Any]					     =					set(state_dict.keys()		) - set(model.state_dict().keys()		)
	__magic_name__	:   Any					     =					{k for k in extra_keys if not k.endswith('.attn.bias'		)}
	__magic_name__	:   Any					     =					set(model.state_dict().keys()		) - set(state_dict.keys()		)
	__magic_name__	:   Dict					     =					{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		)
	__magic_name__	:   Union[str, Any]					     =					model.num_parameters(exclude_embeddings=lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					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    :     Any		,		lowerCAmelCase    :     Optional[Any]=False		,		lowerCAmelCase    :     Tuple="text"		):





	"""simple docstring"""







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

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

	__magic_name__	:   int					     =					_get_ckpt_path(lowerCAmelCase		,		use_small=lowerCAmelCase		)
	__magic_name__	:   Any					     =					_load_model(lowerCAmelCase		,		lowerCAmelCase		,		model_type=lowerCAmelCase		,		use_small=lowerCAmelCase		)

	# load bark initial model
	__magic_name__	:   List[str]					     =					_bark_load_model(lowerCAmelCase		,		'cpu'		,		model_type=lowerCAmelCase		,		use_small=lowerCAmelCase		)

	if model_type == "text":
		__magic_name__	:   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
	__magic_name__	:   Union[str, Any]					     =					5
	__magic_name__	:   Optional[int]					     =					10

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

		__magic_name__	:   Optional[int]					     =					model(lowerCAmelCase		)

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

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

		__magic_name__	:   str					     =					model(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   Tuple					     =					bark_model(lowerCAmelCase		,		lowerCAmelCase		)

		__magic_name__	:   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(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
	model.save_pretrained(lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		,		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)

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

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

	__magic_name__	:   Dict					     =					BarkConfig.from_sub_model_configs(
	    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   List[Any]					     =					BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config		,		coarseAcoustic.generation_config		,		fineAcoustic.generation_config		)

	__magic_name__	:   int					     =					BarkModel(lowerCAmelCase		)

	__magic_name__	:   List[str]					     =					semantic
	__magic_name__	:   Optional[int]					     =					coarseAcoustic
	__magic_name__	:   List[str]					     =					fineAcoustic
	__magic_name__	:   int					     =					codec

	__magic_name__	:   Union[str, 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				: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.''')

							lowerCAmelCase				:Union[str, Any]        =      parser.parse_args()

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