






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()
A_		:		Tuple		     =			logging.get_logger(__name__)

set_seed(770)


A_		:		Dict		     =			{
    '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',
}


A_		:		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',
    },
}

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__=False  )	->    List[str]:
		UpperCamelCase_: Dict   				=    model_type
		if use_small:
				key += "_small"
		return os.path.join(lowerCamelCase__     ,   REMOTE_MODEL_PATHS[key]['file_name']  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Union[str, Any]:
		os.makedirs(lowerCamelCase__     ,   exist_ok=lowerCamelCase__  )
		hf_hub_download(repo_id=lowerCamelCase__     ,   filename=lowerCamelCase__     ,   local_dir=lowerCamelCase__  )

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

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

		UpperCamelCase_: Optional[int]   				=    ConfigClass(**checkpoint['model_args']  )
		UpperCamelCase_: Union[str, Any]   				=    ModelClass(config=lowerCamelCase__  )
		UpperCamelCase_: int   				=    GenerationConfigClass()

		UpperCamelCase_: Dict   				=    model_generation_config
		UpperCamelCase_: List[str]   				=    checkpoint['''model''']
		# fixup checkpoint
		UpperCamelCase_: List[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
						UpperCamelCase_: Optional[int]   				=    k[len(lowerCamelCase__  ) :]
						for old_layer_name in new_layer_name_dict:
								UpperCamelCase_: int   				=    new_k.replace(lowerCamelCase__     ,   new_layer_name_dict[old_layer_name]  )

						UpperCamelCase_: Any   				=    state_dict.pop(lowerCamelCase__  )

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

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

		UpperCamelCase_: Dict   				=    _get_ckpt_path(lowerCamelCase__     ,   use_small=lowerCamelCase__  )
		UpperCamelCase_: Dict   				=    _load_model(lowerCamelCase__     ,   lowerCamelCase__     ,   model_type=lowerCamelCase__     ,   use_small=lowerCamelCase__  )

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

		if model_type == "text":
				UpperCamelCase_: Any   				=    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
		UpperCamelCase_: Dict   				=    5
		UpperCamelCase_: List[str]   				=    1_0

		if model_type in ["text", "coarse"]:
				UpperCamelCase_: Union[str, Any]   				=    torch.randint(2_5_6     ,   (batch_size, sequence_length)     ,   dtype=torch.int  )
				UpperCamelCase_: List[Any]   				=    bark_model(lowerCamelCase__  )[0]

				UpperCamelCase_: Union[str, Any]   				=    model(lowerCamelCase__  )

				# take last logits
				UpperCamelCase_: Union[str, Any]   				=    output_new_model_total.logits[:, [-1], :]

		else:
				UpperCamelCase_: Union[str, Any]   				=    3
				UpperCamelCase_: Tuple   				=    8
				UpperCamelCase_: Union[str, Any]   				=    torch.randint(2_5_6     ,   (batch_size, sequence_length, n_codes_total)     ,   dtype=torch.int  )

				UpperCamelCase_: Any   				=    model(lowerCamelCase__     ,   lowerCamelCase__  )
				UpperCamelCase_: Dict   				=    bark_model(lowerCamelCase__     ,   lowerCamelCase__  )

				UpperCamelCase_: int   				=    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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   )	->    Optional[Any]:
		UpperCamelCase_: Dict   				=    os.path.join(lowerCamelCase__     ,   lowerCamelCase__  )

		UpperCamelCase_: Optional[int]   				=    BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase__     ,   'config.json'  )  )
		UpperCamelCase_: Any   				=    BarkCoarseConfig.from_pretrained(os.path.join(lowerCamelCase__     ,   'config.json'  )  )
		UpperCamelCase_: Any   				=    BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase__     ,   'config.json'  )  )
		UpperCamelCase_: Optional[Any]   				=    EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

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

		UpperCamelCase_: Union[str, Any]   				=    BarkConfig.from_sub_model_configs(
		    lowerCamelCase__     ,   lowerCamelCase__     ,   lowerCamelCase__     ,   lowerCamelCase__  )

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

		UpperCamelCase_: int   				=    BarkModel(lowerCamelCase__  )

		UpperCamelCase_: List[str]   				=    semantic
		UpperCamelCase_: List[str]   				=    coarseAcoustic
		UpperCamelCase_: Dict   				=    fineAcoustic
		UpperCamelCase_: List[Any]   				=    codec

		UpperCamelCase_: 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__":
			A_		:		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.')

			A_		:		Any		     =			parser.parse_args()

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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_		:		List[Any]		     =			logging.get_logger(__name__)

A_		:		Optional[Any]		     =			{
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									='''convbert'''





       def __init__( self  ,      _lowerCamelCase=3_0_5_2_2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=3_0_7_2  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1e-12  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=2  ,      _lowerCamelCase=9  ,      _lowerCamelCase=1  ,      _lowerCamelCase=None  ,      **_lowerCamelCase  ,      ):
         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )

         UpperCamelCase_: Dict   				=    vocab_size
         UpperCamelCase_: Tuple   				=    hidden_size
         UpperCamelCase_: Optional[int]   				=    num_hidden_layers
         UpperCamelCase_: Optional[int]   				=    num_attention_heads
         UpperCamelCase_: Optional[Any]   				=    intermediate_size
         UpperCamelCase_: Tuple   				=    hidden_act
         UpperCamelCase_: Any   				=    hidden_dropout_prob
         UpperCamelCase_: Any   				=    attention_probs_dropout_prob
         UpperCamelCase_: List[Any]   				=    max_position_embeddings
         UpperCamelCase_: List[Any]   				=    type_vocab_size
         UpperCamelCase_: Optional[int]   				=    initializer_range
         UpperCamelCase_: Tuple   				=    layer_norm_eps
         UpperCamelCase_: List[str]   				=    embedding_size
         UpperCamelCase_: int   				=    head_ratio
         UpperCamelCase_: Dict   				=    conv_kernel_size
         UpperCamelCase_: List[Any]   				=    num_groups
         UpperCamelCase_: Dict   				=    classifier_dropout







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @property
       def       _a   ( self							):
         if self.task == "multiple-choice":
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
                 ('token_type_ids', dynamic_axis),
             ]							)