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()
__lowercase							 =					logging.get_logger(__name__)

set_seed(770)


__lowercase							 =					{
    '''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''',
}


__lowercase							 =					{
    '''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''',
    },
}

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE="text"	):
    '''simple docstring'''







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

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

    __UpperCamelCase     :Union[str, Any]			  =		ConfigClass(**checkpoint['''model_args''']	)
    __UpperCamelCase     :str			  =		ModelClass(config=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		GenerationConfigClass()

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

            __UpperCamelCase     :List[str]			  =		state_dict.pop(SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :Tuple			  =		set(state_dict.keys()	) - set(model.state_dict().keys()	)
    __UpperCamelCase     :int			  =		{k for k in extra_keys if not k.endswith('''.attn.bias'''	)}
    __UpperCamelCase     :int			  =		set(model.state_dict().keys()	) - set(state_dict.keys()	)
    __UpperCamelCase     :int			  =		{k for k in missing_keys if not k.endswith('''.attn.bias'''	)}
    if len(SCREAMING_SNAKE_CASE	) != 0:
        raise ValueError(f"""extra keys found: {extra_keys}"""	)
    if len(SCREAMING_SNAKE_CASE	) != 0:
        raise ValueError(f"""missing keys: {missing_keys}"""	)
    model.load_state_dict(SCREAMING_SNAKE_CASE  ,			strict=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Tuple			  =		checkpoint['''best_val_loss'''].item()
    logger.info(f"""model loaded: {round(n_params/1e6  ,			1	)}M params, {round(SCREAMING_SNAKE_CASE  ,			3	)} loss"""	)
    model.eval()
    model.to(SCREAMING_SNAKE_CASE	)
    del checkpoint, state_dict

    return model



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE="text"	):
    '''simple docstring'''







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

    __UpperCamelCase     :List[Any]			  =		'''cpu'''  # do conversion on cpu

    __UpperCamelCase     :List[Any]			  =		_get_ckpt_path(SCREAMING_SNAKE_CASE  ,			use_small=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :int			  =		_load_model(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			model_type=SCREAMING_SNAKE_CASE  ,			use_small=SCREAMING_SNAKE_CASE	)

    # load bark initial model
    __UpperCamelCase     :Optional[Any]			  =		_bark_load_model(SCREAMING_SNAKE_CASE  ,			'''cpu'''  ,			model_type=SCREAMING_SNAKE_CASE  ,			use_small=SCREAMING_SNAKE_CASE	)

    if model_type == "text":
        __UpperCamelCase     :Dict			  =		bark_model['''model''']

    if model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE	) != 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     :List[str]			  =		5
    __UpperCamelCase     :List[str]			  =		10

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

        __UpperCamelCase     :Optional[int]			  =		model(SCREAMING_SNAKE_CASE	)

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

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

        __UpperCamelCase     :Tuple			  =		model(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :str			  =		bark_model(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        __UpperCamelCase     :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(SCREAMING_SNAKE_CASE	).mkdir(exist_ok=SCREAMING_SNAKE_CASE	)
    model.save_pretrained(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :Union[str, Any]			  =		BarkSemanticModel.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Dict			  =		BarkCoarseModel.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		BarkFineModel.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		EncodecModel.from_pretrained('''facebook/encodec_24khz'''	)

    __UpperCamelCase     :Tuple			  =		BarkConfig.from_sub_model_configs(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :Union[str, Any]			  =		BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config  ,			coarseAcoustic.generation_config  ,			fineAcoustic.generation_config	)

    __UpperCamelCase     :int			  =		BarkModel(SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :List[str]			  =		semantic
    __UpperCamelCase     :Any			  =		coarseAcoustic
    __UpperCamelCase     :Tuple			  =		fineAcoustic
    __UpperCamelCase     :List[Any]			  =		codec

    __UpperCamelCase     :int			  =		bark_generation_config

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


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

    __lowercase							 =					parser.parse_args()

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






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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''tokenizer_file''': '''tokenizer.json'''}

__lowercase							 =					{
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						int						=      VOCAB_FILES_NAMES
    a__	:						Tuple						=      PRETRAINED_VOCAB_FILES_MAP
    a__	:						List[str]						=      ["""input_ids""", """attention_mask"""]
    a__	:						int						=      None







    def __init__(	self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase="<unk>" , __lowercase="<s>" , __lowercase="</s>" , __lowercase="<pad>" , __lowercase=False , __lowercase=False , **__lowercase , )       ->	List[str]:
        super().__init__(
            __lowercase , __lowercase , tokenizer_file=__lowercase , unk_token=__lowercase , bos_token=__lowercase , eos_token=__lowercase , pad_token=__lowercase , add_prefix_space=__lowercase , clean_up_tokenization_spaces=__lowercase , **__lowercase , )
        __UpperCamelCase     :int			  =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space''' , __lowercase) != add_prefix_space:
            __UpperCamelCase     :Any			  =		getattr(__lowercase , pre_tok_state.pop('''type'''))
            __UpperCamelCase     :str			  =		add_prefix_space
            __UpperCamelCase     :List[str]			  =		pre_tok_class(**__lowercase)

        __UpperCamelCase     :Tuple			  =		add_prefix_space







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :Tuple			  =		kwargs.get('''is_split_into_words''' , __lowercase)
        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

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







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :List[str]			  =		kwargs.get('''is_split_into_words''' , __lowercase)

        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	Tuple[str]:
        __UpperCamelCase     :Optional[Any]			  =		self._tokenizer.model.save(__lowercase , name=__lowercase)
        return tuple(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[int]:
        __UpperCamelCase     :str			  =		[]
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(__lowercase , add_special_tokens=__lowercase) + [self.eos_token_id])

        if len(__lowercase) > self.model_max_length:
            __UpperCamelCase     :Any			  =		input_ids[-self.model_max_length :]
        return input_ids





