
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()
_SCREAMING_SNAKE_CASE    :      Any          =			logging.get_logger(__name__)

set_seed(7_7_0)


_SCREAMING_SNAKE_CASE    :      Optional[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',
}


_SCREAMING_SNAKE_CASE    :      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',
    },
}

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						Union[str, Any]=False       ) ->       Optional[Any]:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						Dict       ) ->       Any:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Any							,						__UpperCamelCase :						int=False							,						__UpperCamelCase :						Optional[int]="text"       ) ->       Union[str, Any]:

				"""simple docstring"""







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

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

				A__  :	Any         =							ConfigClass(**checkpoint['''model_args''']       )
				A__  :	Tuple         =							ModelClass(config=__UpperCamelCase       )
				A__  :	Any         =							GenerationConfigClass()

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

												A__  :	Optional[int]         =							state_dict.pop(__UpperCamelCase       )

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

				return model

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Optional[Any]=False							,						__UpperCamelCase :						Tuple="text"       ) ->       int:

				"""simple docstring"""







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

				A__  :	str         =							'''cpu'''  # do conversion on cpu

				A__  :	int         =							_get_ckpt_path(__UpperCamelCase							,						use_small=__UpperCamelCase       )
				A__  :	Optional[int]         =							_load_model(__UpperCamelCase							,						__UpperCamelCase							,						model_type=__UpperCamelCase							,						use_small=__UpperCamelCase       )

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

				if model_type == "text":
								A__  :	Optional[int]         =							bark_model['''model''']

				if model.num_parameters(exclude_embeddings=__UpperCamelCase       ) != 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__  :	Optional[Any]         =							5
				A__  :	Dict         =							10

				if model_type in ["text", "coarse"]:
								A__  :	Tuple         =							torch.randint(2_56							,						(batch_size, sequence_length)							,						dtype=torch.int       )
								A__  :	Tuple         =							bark_model(__UpperCamelCase       )[0]

								A__  :	int         =							model(__UpperCamelCase       )

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

				else:
								A__  :	str         =							3
								A__  :	List[str]         =							8
								A__  :	List[Any]         =							torch.randint(2_56							,						(batch_size, sequence_length, n_codes_total)							,						dtype=torch.int       )

								A__  :	List[Any]         =							model(__UpperCamelCase							,						__UpperCamelCase       )
								A__  :	Union[str, Any]         =							bark_model(__UpperCamelCase							,						__UpperCamelCase       )

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict							,						__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Union[str, Any]							,						__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Dict							,						__UpperCamelCase :						Optional[int]							,						) ->       Dict:

				"""simple docstring"""







				A__  :	int         =							os.path.join(__UpperCamelCase							,						__UpperCamelCase       )

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

				A__  :	Dict         =							BarkSemanticModel.from_pretrained(__UpperCamelCase       )
				A__  :	str         =							BarkCoarseModel.from_pretrained(__UpperCamelCase       )
				A__  :	Optional[int]         =							BarkFineModel.from_pretrained(__UpperCamelCase       )
				A__  :	int         =							EncodecModel.from_pretrained('''facebook/encodec_24khz'''       )

				A__  :	str         =							BarkConfig.from_sub_model_configs(
				    __UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				A__  :	Any         =							BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config							,						coarseAcoustic.generation_config							,						fineAcoustic.generation_config       )

				A__  :	List[Any]         =							BarkModel(__UpperCamelCase       )

				A__  :	List[Any]         =							semantic
				A__  :	Tuple         =							coarseAcoustic
				A__  :	Union[str, Any]         =							fineAcoustic
				A__  :	Any         =							codec

				A__  :	Union[str, Any]         =							bark_generation_config

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


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE    :      int          =			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.')

			_SCREAMING_SNAKE_CASE    :      Tuple          =			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_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


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

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

					A__  :	Optional[Any]         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	Union[str, Any]         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	List[Any]         =							add_prefix_space
									A__  :	Any         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

					if len(UpperCamelCase__		) > self.model_max_length:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids