

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()
lowerCAmelCase__								=		logging.get_logger(__name__)

set_seed(7_7_0)


lowerCAmelCase__								=		{
    """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""",
}


lowerCAmelCase__								=		{
    """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""",
    },
}

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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Tuple=False	)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		List[Any]			=						model_type
	if use_small:
		key += "_small"
	return os.path.join(UpperCAmelCase_							,       REMOTE_MODEL_PATHS[key]['file_name']	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Any	)						->				Any:




	'''simple docstring'''




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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Optional[Any]=False							,       UpperCAmelCase_							:     Dict="text"	)						->				Optional[int]:




	'''simple docstring'''




	if model_type == "text":
		_UpperCamelCase   :		Union[str, Any]			=						BarkSemanticModel
		_UpperCamelCase   :		int			=						BarkSemanticConfig
		_UpperCamelCase   :		str			=						BarkSemanticGenerationConfig
	elif model_type == "coarse":
		_UpperCamelCase   :		Union[str, Any]			=						BarkCoarseModel
		_UpperCamelCase   :		List[Any]			=						BarkCoarseConfig
		_UpperCamelCase   :		Optional[Any]			=						BarkCoarseGenerationConfig
	elif model_type == "fine":
		_UpperCamelCase   :		Dict			=						BarkFineModel
		_UpperCamelCase   :		Optional[int]			=						BarkFineConfig
		_UpperCamelCase   :		Optional[Any]			=						BarkFineGenerationConfig
	else:
		raise NotImplementedError()
	_UpperCamelCase   :		Optional[int]			=						F'''{model_type}_small''' if use_small else model_type
	_UpperCamelCase   :		List[Any]			=						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']	)
	_UpperCamelCase   :		Tuple			=						torch.load(UpperCAmelCase_							,       map_location=UpperCAmelCase_	)
	# this is a hack
	_UpperCamelCase   :		Dict			=						checkpoint['model_args']
	if "input_vocab_size" not in model_args:
		_UpperCamelCase   :		List[str]			=						model_args['vocab_size']
		_UpperCamelCase   :		Any			=						model_args['vocab_size']
		del model_args["vocab_size"]

	# convert Bark model arguments to HF Bark model arguments
	_UpperCamelCase   :		Union[str, Any]			=						model_args.pop('n_head'	)
	_UpperCamelCase   :		Tuple			=						model_args.pop('n_embd'	)
	_UpperCamelCase   :		int			=						model_args.pop('n_layer'	)

	_UpperCamelCase   :		str			=						ConfigClass(**checkpoint['model_args']	)
	_UpperCamelCase   :		str			=						ModelClass(config=UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[Any]			=						GenerationConfigClass()

	_UpperCamelCase   :		str			=						model_generation_config
	_UpperCamelCase   :		List[Any]			=						checkpoint['model']
	# fixup checkpoint
	_UpperCamelCase   :		List[Any]			=						'_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
			_UpperCamelCase   :		List[Any]			=						k[len(UpperCAmelCase_	) :]
			for old_layer_name in new_layer_name_dict:
				_UpperCamelCase   :		List[Any]			=						new_k.replace(UpperCAmelCase_							,       new_layer_name_dict[old_layer_name]	)

			_UpperCamelCase   :		Union[str, Any]			=						state_dict.pop(UpperCAmelCase_	)

	_UpperCamelCase   :		int			=						set(state_dict.keys()	) - set(model.state_dict().keys()	)
	_UpperCamelCase   :		Tuple			=						{k for k in extra_keys if not k.endswith('.attn.bias'	)}
	_UpperCamelCase   :		Optional[Any]			=						set(model.state_dict().keys()	) - set(state_dict.keys()	)
	_UpperCamelCase   :		str			=						{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_	)
	_UpperCamelCase   :		List[Any]			=						model.num_parameters(exclude_embeddings=UpperCAmelCase_	)
	_UpperCamelCase   :		List[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 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Optional[Any]=False							,       UpperCAmelCase_							:     int="text"	)						->				Dict:




	'''simple docstring'''




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

	_UpperCamelCase   :		Tuple			=						'cpu'  # do conversion on cpu

	_UpperCamelCase   :		Dict			=						_get_ckpt_path(UpperCAmelCase_							,       use_small=UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						_load_model(UpperCAmelCase_							,       UpperCAmelCase_							,       model_type=UpperCAmelCase_							,       use_small=UpperCAmelCase_	)

	# load bark initial model
	_UpperCamelCase   :		Tuple			=						_bark_load_model(UpperCAmelCase_							,       'cpu'							,       model_type=UpperCAmelCase_							,       use_small=UpperCAmelCase_	)

	if model_type == "text":
		_UpperCamelCase   :		Any			=						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
	_UpperCamelCase   :		Optional[int]			=						5
	_UpperCamelCase   :		Optional[int]			=						1_0

	if model_type in ["text", "coarse"]:
		_UpperCamelCase   :		Dict			=						torch.randint(2_5_6							,       (batch_size, sequence_length)							,       dtype=torch.int	)
		_UpperCamelCase   :		Union[str, Any]			=						bark_model(UpperCAmelCase_	)[0]

		_UpperCamelCase   :		List[Any]			=						model(UpperCAmelCase_	)

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

	else:
		_UpperCamelCase   :		List[str]			=						3
		_UpperCamelCase   :		List[str]			=						8
		_UpperCamelCase   :		Optional[int]			=						torch.randint(2_5_6							,       (batch_size, sequence_length, n_codes_total)							,       dtype=torch.int	)

		_UpperCamelCase   :		int			=						model(UpperCAmelCase_							,       UpperCAmelCase_	)
		_UpperCamelCase   :		int			=						bark_model(UpperCAmelCase_							,       UpperCAmelCase_	)

		_UpperCamelCase   :		List[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 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Optional[int]							,       )						->				List[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		List[Any]			=						os.path.join(UpperCAmelCase_							,       UpperCAmelCase_	)

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

	_UpperCamelCase   :		int			=						BarkSemanticModel.from_pretrained(UpperCAmelCase_	)
	_UpperCamelCase   :		Dict			=						BarkCoarseModel.from_pretrained(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						BarkFineModel.from_pretrained(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						EncodecModel.from_pretrained('facebook/encodec_24khz'	)

	_UpperCamelCase   :		List[str]			=						BarkConfig.from_sub_model_configs(
	    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

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

	_UpperCamelCase   :		Tuple			=						BarkModel(UpperCAmelCase_	)

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

	_UpperCamelCase   :		Dict			=						bark_generation_config

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


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

							lowerCAmelCase__								=		parser.parse_args()

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







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		[
    ["""attention""", """attn"""],
    ["""encoder_attention""", """encoder_attn"""],
    ["""q_lin""", """q_proj"""],
    ["""k_lin""", """k_proj"""],
    ["""v_lin""", """v_proj"""],
    ["""out_lin""", """out_proj"""],
    ["""norm_embeddings""", """layernorm_embedding"""],
    ["""position_embeddings""", """embed_positions"""],
    ["""embeddings""", """embed_tokens"""],
    ["""ffn.lin""", """fc"""],
]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Optional[int]:




	'''simple docstring'''




	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		_UpperCamelCase   :		List[Any]			=						k.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

	if k.startswith('encoder'	):
		_UpperCamelCase   :		Optional[Any]			=						k.replace('.attn'							,       '.self_attn'	)
		_UpperCamelCase   :		Optional[int]			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'final_layer_norm'	)
	elif k.startswith('decoder'	):
		_UpperCamelCase   :		Any			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'encoder_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm3'							,       'final_layer_norm'	)
	return k






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		_UpperCamelCase   :		Optional[int]			=						sd.pop(UpperCAmelCase_	)
		_UpperCamelCase   :		str			=						k.replace('layernorm_embedding'							,       'layer_norm'	)
		assert new_k not in sd
		_UpperCamelCase   :		Tuple			=						v


lowerCAmelCase__								=		["""START"""]






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		int			=						model['model']
	_UpperCamelCase   :		List[Any]			=						BlenderbotConfig.from_json_file(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						BlenderbotForConditionalGeneration(UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						m.model.state_dict().keys()
	_UpperCamelCase   :		Union[str, Any]			=						[]
	_UpperCamelCase   :		int			=						{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		_UpperCamelCase   :		Optional[int]			=						rename_state_dict_key(UpperCAmelCase_	)
		if new_k not in valid_keys:
			failures.append([k, new_k]	)
		else:
			_UpperCamelCase   :		int			=						v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCAmelCase_	)
	m.model.load_state_dict(UpperCAmelCase_							,       strict=UpperCAmelCase_	)
	m.half()
	m.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
							lowerCAmelCase__								=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""--src_path""", type=str, help="""like blenderbot-model.bin""")
							parser.add_argument("""--save_dir""", default="""hf_blenderbot""", type=str, help="""Where to save converted model.""")
							parser.add_argument(
							    """--hf_config_json""", default="""blenderbot-3b-config.json""", type=str, help="""Path to config to use"""
							)
							lowerCAmelCase__								=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




