

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()
a		=logging.get_logger(__name__)

set_seed(770)


a		={
    """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""",
}


a		={
    """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""",
    },
}

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






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__=False			)  ->     Optional[Any]:
	__lowerCamelCase				:				int					       = model_type
	if use_small:
		key += "_small"
	return os.path.join(snake_case__	, REMOTE_MODEL_PATHS[key]['file_name']			)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Optional[int]:
	os.makedirs(snake_case__	, exist_ok=snake_case__			)
	hf_hub_download(repo_id=snake_case__	, filename=snake_case__	, local_dir=snake_case__			)






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

	# convert Bark model arguments to HF Bark model arguments
	__lowerCamelCase				:				int					       = model_args.pop('n_head'			)
	__lowerCamelCase				:				Tuple					       = model_args.pop('n_embd'			)
	__lowerCamelCase				:				Dict					       = model_args.pop('n_layer'			)

	__lowerCamelCase				:				List[Any]					       = ConfigClass(**checkpoint['model_args']			)
	__lowerCamelCase				:				Tuple					       = ModelClass(config=snake_case__			)
	__lowerCamelCase				:				Dict					       = GenerationConfigClass()

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

			__lowerCamelCase				:				Dict					       = state_dict.pop(snake_case__			)

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

	return model






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__=False	, lowerCamelCase__="text"			)  ->     int:
	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	__lowerCamelCase				:				Optional[Any]					       = 'cpu'  # do conversion on cpu

	__lowerCamelCase				:				int					       = _get_ckpt_path(snake_case__	, use_small=snake_case__			)
	__lowerCamelCase				:				Tuple					       = _load_model(snake_case__	, snake_case__	, model_type=snake_case__	, use_small=snake_case__			)

	# load bark initial model
	__lowerCamelCase				:				Dict					       = _bark_load_model(snake_case__	, 'cpu'	, model_type=snake_case__	, use_small=snake_case__			)

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

	if model.num_parameters(exclude_embeddings=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
	__lowerCamelCase				:				List[Any]					       = 5
	__lowerCamelCase				:				List[Any]					       = 1_0

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

		__lowerCamelCase				:				List[Any]					       = model(snake_case__			)

		# take last logits
		__lowerCamelCase				:				List[Any]					       = output_new_model_total.logits[:, [-1], :]

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

		__lowerCamelCase				:				str					       = model(snake_case__	, snake_case__			)
		__lowerCamelCase				:				Dict					       = bark_model(snake_case__	, snake_case__			)

		__lowerCamelCase				:				Optional[int]					       = 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(snake_case__			).mkdir(exist_ok=snake_case__			)
	model.save_pretrained(snake_case__			)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, )  ->     List[Any]:
	__lowerCamelCase				:				List[str]					       = os.path.join(snake_case__	, snake_case__			)

	__lowerCamelCase				:				Any					       = BarkSemanticConfig.from_pretrained(os.path.join(snake_case__	, 'config.json'			)			)
	__lowerCamelCase				:				int					       = BarkCoarseConfig.from_pretrained(os.path.join(snake_case__	, 'config.json'			)			)
	__lowerCamelCase				:				Any					       = BarkFineConfig.from_pretrained(os.path.join(snake_case__	, 'config.json'			)			)
	__lowerCamelCase				:				Any					       = EncodecConfig.from_pretrained('facebook/encodec_24khz'			)

	__lowerCamelCase				:				Optional[Any]					       = BarkSemanticModel.from_pretrained(snake_case__			)
	__lowerCamelCase				:				Optional[Any]					       = BarkCoarseModel.from_pretrained(snake_case__			)
	__lowerCamelCase				:				int					       = BarkFineModel.from_pretrained(snake_case__			)
	__lowerCamelCase				:				Dict					       = EncodecModel.from_pretrained('facebook/encodec_24khz'			)

	__lowerCamelCase				:				List[Any]					       = BarkConfig.from_sub_model_configs(
	    snake_case__	, snake_case__	, snake_case__	, snake_case__			)

	__lowerCamelCase				:				Optional[Any]					       = BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config	, coarseAcoustic.generation_config	, fineAcoustic.generation_config			)

	__lowerCamelCase				:				Optional[int]					       = BarkModel(snake_case__			)

	__lowerCamelCase				:				Dict					       = semantic
	__lowerCamelCase				:				int					       = coarseAcoustic
	__lowerCamelCase				:				Optional[int]					       = fineAcoustic
	__lowerCamelCase				:				List[Any]					       = codec

	__lowerCamelCase				:				Optional[Any]					       = bark_generation_config

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


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

					a		=parser.parse_args()

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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  "canine"
       def __init__(			self,			SCREAMING_SNAKE_CASE_=768,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=3072,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-12,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=0XE000,			SCREAMING_SNAKE_CASE_=0XE001,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=128,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	max_position_embeddings
          UpperCamelCase    :  Tuple							=	hidden_size
          UpperCamelCase    :  Union[str, Any]							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  Tuple							=	intermediate_size
          UpperCamelCase    :  List[str]							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[int]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	initializer_range
          UpperCamelCase    :  Tuple							=	type_vocab_size
          UpperCamelCase    :  Any							=	layer_norm_eps

          # Character config:
          UpperCamelCase    :  List[Any]							=	downsampling_rate
          UpperCamelCase    :  Optional[int]							=	upsampling_kernel_size
          UpperCamelCase    :  Tuple							=	num_hash_functions
          UpperCamelCase    :  Union[str, Any]							=	num_hash_buckets
          UpperCamelCase    :  str							=	local_transformer_stride



