






from collections import OrderedDict
from typing import Mapping

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


snake_case     					=		logging.get_logger(__name__)

snake_case     					=		{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Union[str, Any]								=						'''roberta'''


     def __init__(					self							:						str       ,			UpperCAmelCase_							:						List[Any]=5_0265       ,			UpperCAmelCase_							:						Dict=768       ,			UpperCAmelCase_							:						str=12       ,			UpperCAmelCase_							:						str=12       ,			UpperCAmelCase_							:						Any=3072       ,			UpperCAmelCase_							:						Dict="gelu"       ,			UpperCAmelCase_							:						Union[str, Any]=0.1       ,			UpperCAmelCase_							:						str=0.1       ,			UpperCAmelCase_							:						int=512       ,			UpperCAmelCase_							:						List[Any]=2       ,			UpperCAmelCase_							:						Dict=0.02       ,			UpperCAmelCase_							:						List[str]=1E-12       ,			UpperCAmelCase_							:						Optional[int]=1       ,			UpperCAmelCase_							:						Any=0       ,			UpperCAmelCase_							:						str=2       ,			UpperCAmelCase_							:						str="absolute"       ,			UpperCAmelCase_							:						Tuple=True       ,			UpperCAmelCase_							:						Dict=None       ,			**UpperCAmelCase_							:						Tuple       ,			):
       super().__init__(pad_token_id=UpperCAmelCase_       ,			bos_token_id=UpperCAmelCase_       ,			eos_token_id=UpperCAmelCase_       ,			**UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Any						    =     vocab_size
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     hidden_size
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     num_hidden_layers
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     num_attention_heads
       SCREAMING_SNAKE_CASE							:   Dict						    =     hidden_act
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     intermediate_size
       SCREAMING_SNAKE_CASE							:   Tuple						    =     hidden_dropout_prob
       SCREAMING_SNAKE_CASE							:   str						    =     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE							:   int						    =     max_position_embeddings
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     type_vocab_size
       SCREAMING_SNAKE_CASE							:   Dict						    =     initializer_range
       SCREAMING_SNAKE_CASE							:   int						    =     layer_norm_eps
       SCREAMING_SNAKE_CASE							:   Tuple						    =     position_embedding_type
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     use_cache
       SCREAMING_SNAKE_CASE							:   List[str]						    =     classifier_dropout





class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     @property
     def 					_A							(					self							:						Optional[int]   ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]   )














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			:	Any								=logging.get_logger(__name__)

set_seed(770)


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



def  a__ (__lowercase							:int					,  __lowercase							:Any=False   )		-> Union[str, Any]:
				_A	:				str   =      model_type
				if use_small:
								key += "_small"
				return os.path.join(__lowercase					,  REMOTE_MODEL_PATHS[key]['''file_name''']   )



def  a__ (__lowercase							:str					,  __lowercase							:Optional[Any]   )		-> Dict:
				os.makedirs(__lowercase					,  exist_ok=__lowercase   )
				hf_hub_download(repo_id=__lowercase					,  filename=__lowercase					,  local_dir=__lowercase   )



def  a__ (__lowercase							:Union[str, Any]					,  __lowercase							:List[str]					,  __lowercase							:str=False					,  __lowercase							:int="text"   )		-> int:
				if model_type == "text":
								_A	:				Optional[Any]   =      BarkSemanticModel
								_A	:				Any   =      BarkSemanticConfig
								_A	:				Dict   =      BarkSemanticGenerationConfig
				elif model_type == "coarse":
								_A	:				Dict   =      BarkCoarseModel
								_A	:				str   =      BarkCoarseConfig
								_A	:				int   =      BarkCoarseGenerationConfig
				elif model_type == "fine":
								_A	:				str   =      BarkFineModel
								_A	:				Optional[Any]   =      BarkFineConfig
								_A	:				int   =      BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				_A	:				Dict   =      f"""{model_type}_small""" if use_small else model_type
				_A	:				Tuple   =      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''']   )
				_A	:				Optional[int]   =      torch.load(__lowercase					,  map_location=__lowercase   )
				# this is a hack
				_A	:				Any   =      checkpoint['''model_args''']
				if "input_vocab_size" not in model_args:
								_A	:				Optional[Any]   =      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[str]   =      model_args.pop('''n_head'''   )
				_A	:				int   =      model_args.pop('''n_embd'''   )
				_A	:				Tuple   =      model_args.pop('''n_layer'''   )

				_A	:				Dict   =      ConfigClass(**checkpoint['''model_args''']   )
				_A	:				Optional[Any]   =      ModelClass(config=__lowercase   )
				_A	:				Optional[Any]   =      GenerationConfigClass()

				_A	:				Dict   =      model_generation_config
				_A	:				int   =      checkpoint['''model''']
				# fixup checkpoint
				_A	:				str   =      '''_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
												_A	:				Optional[Any]   =      k[len(__lowercase   ) :]
												for old_layer_name in new_layer_name_dict:
																_A	:				Any   =      new_k.replace(__lowercase					,  new_layer_name_dict[old_layer_name]   )

												_A	:				str   =      state_dict.pop(__lowercase   )

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

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

				_A	:				Tuple   =      _get_ckpt_path(__lowercase					,  use_small=__lowercase   )
				_A	:				Any   =      _load_model(__lowercase					,  __lowercase					,  model_type=__lowercase					,  use_small=__lowercase   )

				# load bark initial model
				_A	:				Union[str, Any]   =      _bark_load_model(__lowercase					,  '''cpu'''					,  model_type=__lowercase					,  use_small=__lowercase   )

				if model_type == "text":
								_A	:				Any   =      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
				_A	:				List[str]   =      5
				_A	:				List[str]   =      10

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

								_A	:				Optional[Any]   =      model(__lowercase   )

								# take last logits
								_A	:				Dict   =      output_new_model_total.logits[:, [-1], :]

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

								_A	:				List[str]   =      model(__lowercase					,  __lowercase   )
								_A	:				Dict   =      bark_model(__lowercase					,  __lowercase   )

								_A	:				Optional[Any]   =      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  a__ (__lowercase							:Dict					,  __lowercase							:List[str]					,  __lowercase							:Union[str, Any]					,  __lowercase							:Dict					,  __lowercase							:List[Any]					,  __lowercase							:Optional[int]					,  )		-> Union[str, Any]:
				_A	:				List[Any]   =      os.path.join(__lowercase					,  __lowercase   )

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

				_A	:				str   =      BarkSemanticModel.from_pretrained(__lowercase   )
				_A	:				Tuple   =      BarkCoarseModel.from_pretrained(__lowercase   )
				_A	:				List[str]   =      BarkFineModel.from_pretrained(__lowercase   )
				_A	:				Union[str, Any]   =      EncodecModel.from_pretrained('''facebook/encodec_24khz'''   )

				_A	:				Tuple   =      BarkConfig.from_sub_model_configs(
				    __lowercase					,  __lowercase					,  __lowercase					,  __lowercase   )

				_A	:				Tuple   =      BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config					,  coarseAcoustic.generation_config					,  fineAcoustic.generation_config   )

				_A	:				Tuple   =      BarkModel(__lowercase   )

				_A	:				Optional[Any]   =      semantic
				_A	:				List[Any]   =      coarseAcoustic
				_A	:				int   =      fineAcoustic
				_A	:				List[Any]   =      codec

				_A	:				Optional[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			:	str								=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			:	Dict								=parser.parse_args()

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



