






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 						__UpperCAmelCase			(     a_							,     a_=False):
							snake_case_				=							model_type
							if use_small:
														key += "_small"
							return os.path.join(a_							,     REMOTE_MODEL_PATHS[key]['file_name'])





def 						__UpperCAmelCase			(     a_							,     a_):
							os.makedirs(a_							,     exist_ok=a_)
							hf_hub_download(repo_id=a_							,     filename=a_							,     local_dir=a_)





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

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

							snake_case_				=							ConfigClass(**checkpoint['model_args'])
							snake_case_				=							ModelClass(config=a_)
							snake_case_				=							GenerationConfigClass()

							snake_case_				=							model_generation_config
							snake_case_				=							checkpoint['model']
							# fixup checkpoint
							snake_case_				=							'_orig_mod.'
							for k, v in list(state_dict.items()):
														if k.startswith(a_):
																					# replace part of the key with corresponding layer name in HF implementation
																					snake_case_				=							k[len(a_) :]
																					for old_layer_name in new_layer_name_dict:
																												snake_case_				=							new_k.replace(a_							,     new_layer_name_dict[old_layer_name])

																					snake_case_				=							state_dict.pop(a_)

							snake_case_				=							set(state_dict.keys()) - set(model.state_dict().keys())
							snake_case_				=							{k for k in extra_keys if not k.endswith('.attn.bias')}
							snake_case_				=							set(model.state_dict().keys()) - set(state_dict.keys())
							snake_case_				=							{k for k in missing_keys if not k.endswith('.attn.bias')}
							if len(a_) != 0:
														raise ValueError(f'''extra keys found: {extra_keys}''')
							if len(a_) != 0:
														raise ValueError(f'''missing keys: {missing_keys}''')
							model.load_state_dict(a_							,     strict=a_)
							snake_case_				=							model.num_parameters(exclude_embeddings=a_)
							snake_case_				=							checkpoint['best_val_loss'].item()
							logger.info(f'''model loaded: {round(n_params/1E6							,     1)}M params, {round(a_							,     3)} loss''')
							model.eval()
							model.to(a_)
							del checkpoint, state_dict

							return model





def 						__UpperCAmelCase			(     a_							,     a_=False							,     a_="text"):
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							snake_case_				=							'cpu'  # do conversion on cpu

							snake_case_				=							_get_ckpt_path(a_							,     use_small=a_)
							snake_case_				=							_load_model(a_							,     a_							,     model_type=a_							,     use_small=a_)

							# load bark initial model
							snake_case_				=							_bark_load_model(a_							,     'cpu'							,     model_type=a_							,     use_small=a_)

							if model_type == "text":
														snake_case_				=							bark_model['model']

							if model.num_parameters(exclude_embeddings=a_) != 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
							snake_case_				=							5
							snake_case_				=							10

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

														snake_case_				=							model(a_)

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

							else:
														snake_case_				=							3
														snake_case_				=							8
														snake_case_				=							torch.randint(2_56							,     (batch_size, sequence_length, n_codes_total)							,     dtype=torch.int)

														snake_case_				=							model(a_							,     a_)
														snake_case_				=							bark_model(a_							,     a_)

														snake_case_				=							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(a_).mkdir(exist_ok=a_)
							model.save_pretrained(a_)





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_							,     a_							,     a_							,     ):
							snake_case_				=							os.path.join(a_							,     a_)

							snake_case_				=							BarkSemanticConfig.from_pretrained(os.path.join(a_							,     'config.json'))
							snake_case_				=							BarkCoarseConfig.from_pretrained(os.path.join(a_							,     'config.json'))
							snake_case_				=							BarkFineConfig.from_pretrained(os.path.join(a_							,     'config.json'))
							snake_case_				=							EncodecConfig.from_pretrained('facebook/encodec_24khz')

							snake_case_				=							BarkSemanticModel.from_pretrained(a_)
							snake_case_				=							BarkCoarseModel.from_pretrained(a_)
							snake_case_				=							BarkFineModel.from_pretrained(a_)
							snake_case_				=							EncodecModel.from_pretrained('facebook/encodec_24khz')

							snake_case_				=							BarkConfig.from_sub_model_configs(
							    a_							,     a_							,     a_							,     a_)

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

							snake_case_				=							BarkModel(a_)

							snake_case_				=							semantic
							snake_case_				=							coarseAcoustic
							snake_case_				=							fineAcoustic
							snake_case_				=							codec

							snake_case_				=							bark_generation_config

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


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)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase						:   Any			  =     {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   int			  =     ['''MvpTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   str			  =     [
				    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''MvpForCausalLM''',
				    '''MvpForConditionalGeneration''',
				    '''MvpForQuestionAnswering''',
				    '''MvpForSequenceClassification''',
				    '''MvpModel''',
				    '''MvpPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
				from .tokenization_mvp import MvpTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mvp_fast import MvpTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mvp import (
								    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MvpForCausalLM,
								    MvpForConditionalGeneration,
								    MvpForQuestionAnswering,
								    MvpForSequenceClassification,
								    MvpModel,
								    MvpPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase						:   List[str]			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




