


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					:  int			=						logging.get_logger(__name__)

set_seed(7_70)


UpperCAmelCase					:  Tuple			=						{
    "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					:  Optional[int]			=						{
    "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					:  int			=						os.path.dirname(os.path.abspath(__file__))
UpperCAmelCase					:  Union[str, Any]			=						os.path.join(os.path.expanduser("~"), ".cache")
UpperCAmelCase					:  Dict			=						os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")






def      __lowerCamelCase		(				lowerCamelCase__						:		List[Any]   ,				lowerCamelCase__						:		Optional[Any]=False	):





			'''simple docstring'''




			lowerCamelCase  						=   model_type
			if use_small:
						key += "_small"
			return os.path.join(lowerCamelCase__   ,				REMOTE_MODEL_PATHS[key]["""file_name"""]	)






def      __lowerCamelCase		(				lowerCamelCase__						:		Any   ,				lowerCamelCase__						:		Union[str, Any]	):





			'''simple docstring'''




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






def      __lowerCamelCase		(				lowerCamelCase__						:		Tuple   ,				lowerCamelCase__						:		Any   ,				lowerCamelCase__						:		Dict=False   ,				lowerCamelCase__						:		Optional[int]="text"	):





			'''simple docstring'''




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

			# convert Bark model arguments to HF Bark model arguments
			lowerCamelCase  						=   model_args.pop("""n_head"""	)
			lowerCamelCase  						=   model_args.pop("""n_embd"""	)
			lowerCamelCase  						=   model_args.pop("""n_layer"""	)

			lowerCamelCase  						=   ConfigClass(**checkpoint["""model_args"""]	)
			lowerCamelCase  						=   ModelClass(config=lowerCamelCase__	)
			lowerCamelCase  						=   GenerationConfigClass()

			lowerCamelCase  						=   model_generation_config
			lowerCamelCase  						=   checkpoint["""model"""]
			# fixup checkpoint
			lowerCamelCase  						=   """_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
									lowerCamelCase  						=   k[len(lowerCamelCase__	) :]
									for old_layer_name in new_layer_name_dict:
												lowerCamelCase  						=   new_k.replace(lowerCamelCase__   ,				new_layer_name_dict[old_layer_name]	)

									lowerCamelCase  						=   state_dict.pop(lowerCamelCase__	)

			lowerCamelCase  						=   set(state_dict.keys()	) - set(model.state_dict().keys()	)
			lowerCamelCase  						=   {k for k in extra_keys if not k.endswith(""".attn.bias"""	)}
			lowerCamelCase  						=   set(model.state_dict().keys()	) - set(state_dict.keys()	)
			lowerCamelCase  						=   {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__	)
			lowerCamelCase  						=   model.num_parameters(exclude_embeddings=lowerCamelCase__	)
			lowerCamelCase  						=   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__						:		Tuple   ,				lowerCamelCase__						:		List[str]=False   ,				lowerCamelCase__						:		Union[str, Any]="text"	):





			'''simple docstring'''




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

			lowerCamelCase  						=   """cpu"""  # do conversion on cpu

			lowerCamelCase  						=   _get_ckpt_path(lowerCamelCase__   ,				use_small=lowerCamelCase__	)
			lowerCamelCase  						=   _load_model(lowerCamelCase__   ,				lowerCamelCase__   ,				model_type=lowerCamelCase__   ,				use_small=lowerCamelCase__	)

			# load bark initial model
			lowerCamelCase  						=   _bark_load_model(lowerCamelCase__   ,				"""cpu"""   ,				model_type=lowerCamelCase__   ,				use_small=lowerCamelCase__	)

			if model_type == "text":
						lowerCamelCase  						=   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
			lowerCamelCase  						=   5
			lowerCamelCase  						=   10

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

						lowerCamelCase  						=   model(lowerCamelCase__	)

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

			else:
						lowerCamelCase  						=   3
						lowerCamelCase  						=   8
						lowerCamelCase  						=   torch.randint(256   ,				(batch_size, sequence_length, n_codes_total)   ,				dtype=torch.int	)

						lowerCamelCase  						=   model(lowerCamelCase__   ,				lowerCamelCase__	)
						lowerCamelCase  						=   bark_model(lowerCamelCase__   ,				lowerCamelCase__	)

						lowerCamelCase  						=   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__						:		Dict   ,				lowerCamelCase__						:		Tuple   ,				lowerCamelCase__						:		Tuple   ,				lowerCamelCase__						:		Optional[Any]   ,				lowerCamelCase__						:		int   ,				lowerCamelCase__						:		str   ,				):





			'''simple docstring'''




			lowerCamelCase  						=   os.path.join(lowerCamelCase__   ,				lowerCamelCase__	)

			lowerCamelCase  						=   BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase__   ,				"""config.json"""	)	)
			lowerCamelCase  						=   BarkCoarseConfig.from_pretrained(os.path.join(lowerCamelCase__   ,				"""config.json"""	)	)
			lowerCamelCase  						=   BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase__   ,				"""config.json"""	)	)
			lowerCamelCase  						=   EncodecConfig.from_pretrained("""facebook/encodec_24khz"""	)

			lowerCamelCase  						=   BarkSemanticModel.from_pretrained(lowerCamelCase__	)
			lowerCamelCase  						=   BarkCoarseModel.from_pretrained(lowerCamelCase__	)
			lowerCamelCase  						=   BarkFineModel.from_pretrained(lowerCamelCase__	)
			lowerCamelCase  						=   EncodecModel.from_pretrained("""facebook/encodec_24khz"""	)

			lowerCamelCase  						=   BarkConfig.from_sub_model_configs(
			    lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__	)

			lowerCamelCase  						=   BarkGenerationConfig.from_sub_model_configs(
			    semantic.generation_config   ,				coarseAcoustic.generation_config   ,				fineAcoustic.generation_config	)

			lowerCamelCase  						=   BarkModel(lowerCamelCase__	)

			lowerCamelCase  						=   semantic
			lowerCamelCase  						=   coarseAcoustic
			lowerCamelCase  						=   fineAcoustic
			lowerCamelCase  						=   codec

			lowerCamelCase  						=   bark_generation_config

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


if __name__ == "__main__":
		UpperCAmelCase					:  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.")

		UpperCAmelCase					:  Tuple			=						parser.parse_args()

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





'''simple docstring'''




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

__snake_case    						=logging.get_logger(__name__)


__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

__snake_case    						={"""facebook/blenderbot-3B""": 128}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	List[Any]		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	str		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     ['''input_ids''', '''attention_mask''']
     lowerCamelCase				:	List[Any]		=     BlenderbotTokenizer





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Tuple=None    ,      UpperCAmelCase__       :      Tuple=None    ,      UpperCAmelCase__       :      List[Any]=None    ,      UpperCAmelCase__       :      str="replace"    ,      UpperCAmelCase__       :      Dict="<s>"    ,      UpperCAmelCase__       :      Tuple="</s>"    ,      UpperCAmelCase__       :      Optional[Any]="</s>"    ,      UpperCAmelCase__       :      Any="<s>"    ,      UpperCAmelCase__       :      List[str]="<unk>"    ,      UpperCAmelCase__       :      int="<pad>"    ,      UpperCAmelCase__       :      Union[str, Any]="<mask>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Union[str, Any]=True    ,      **UpperCAmelCase__       :      Optional[int]    ,      )				->  int:
        super().__init__(
            UpperCAmelCase__    ,      UpperCAmelCase__    ,      tokenizer_file=UpperCAmelCase__    ,      errors=UpperCAmelCase__    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      sep_token=UpperCAmelCase__    ,      cls_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      pad_token=UpperCAmelCase__    ,      mask_token=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )

        lowerCAmelCase     =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
        if pre_tok_state.get('add_prefix_space'    ,      UpperCAmelCase__    ) != add_prefix_space:
           lowerCAmelCase     =			getattr(UpperCAmelCase__    ,      pre_tok_state.pop('type'    )    )
           lowerCAmelCase     =			add_prefix_space
           lowerCAmelCase     =			pre_tok_class(**UpperCAmelCase__    )

        lowerCAmelCase     =			add_prefix_space

        lowerCAmelCase     =			'post_processor'
        lowerCAmelCase     =			getattr(self.backend_tokenizer    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
        if tokenizer_component_instance:
           lowerCAmelCase     =			json.loads(tokenizer_component_instance.__getstate__()    )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
              lowerCAmelCase     =			tuple(state['sep']    )
           if "cls" in state:
              lowerCAmelCase     =			tuple(state['cls']    )

           lowerCAmelCase     =			False

           if state.get('add_prefix_space'    ,      UpperCAmelCase__    ) != add_prefix_space:
              lowerCAmelCase     =			add_prefix_space
              lowerCAmelCase     =			True

           if state.get('trim_offsets'    ,      UpperCAmelCase__    ) != trim_offsets:
              lowerCAmelCase     =			trim_offsets
              lowerCAmelCase     =			True

           if changes_to_apply:
              lowerCAmelCase     =			getattr(UpperCAmelCase__    ,      state.pop('type'    )    )
              lowerCAmelCase     =			component_class(**UpperCAmelCase__    )
              setattr(self.backend_tokenizer    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )





     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  str:
        if self._mask_token is None:
           if self.verbose:
              logger.error('Using mask_token, but it is not set yet.'    )
           return None
        return str(self._mask_token    )





     @mask_token.setter
     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Tuple:
        lowerCAmelCase     =			AddedToken(UpperCAmelCase__    ,      lstrip=UpperCAmelCase__    ,      rstrip=UpperCAmelCase__    ) if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) else value
        lowerCAmelCase     =			value





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      *UpperCAmelCase__       :      Union[str, Any]    ,      **UpperCAmelCase__       :      List[str]    )				->  BatchEncoding:
        lowerCAmelCase     =			kwargs.get('is_split_into_words'    ,      UpperCAmelCase__    )
        assert self.add_prefix_space or not is_split_into_words, (
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      *UpperCAmelCase__       :      str    ,      **UpperCAmelCase__       :      List[str]    )				->  BatchEncoding:
        lowerCAmelCase     =			kwargs.get('is_split_into_words'    ,      UpperCAmelCase__    )

        assert self.add_prefix_space or not is_split_into_words, (
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Optional[str] = None    )				->  Tuple[str]:
        lowerCAmelCase     =			self._tokenizer.model.save(UpperCAmelCase__    ,      name=UpperCAmelCase__    )
        return tuple(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  Any:
        return token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      "Conversation"    )				->  List[int]:
        lowerCAmelCase     =			[]
        for is_user, text in conversation.iter_texts():
           if is_user:
              # We need to space prefix as it's being done within blenderbot
              inputs.append(' ' + text    )
           else:
              # Generated responses should contain them already.
              inputs.append(UpperCAmelCase__    )

        lowerCAmelCase     =			'  '.join(UpperCAmelCase__    )
        lowerCAmelCase     =			self.encode(UpperCAmelCase__    )
        if len(UpperCAmelCase__    ) > self.model_max_length:
           lowerCAmelCase     =			input_ids[-self.model_max_length :]
           logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''    )
        return input_ids





