

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__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Any=False					)	->					Union[str, Any]:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							int					)	->					Optional[int]:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[int]		,				SCREAMING_SNAKE_CASE_:							Dict		,				SCREAMING_SNAKE_CASE_:							str=False		,				SCREAMING_SNAKE_CASE_:							int="text"					)	->					str:




				'''simple docstring'''





				if model_type == "text":
								A__						=	BarkSemanticModel
								A__						=	BarkSemanticConfig
								A__						=	BarkSemanticGenerationConfig
				elif model_type == "coarse":
								A__						=	BarkCoarseModel
								A__						=	BarkCoarseConfig
								A__						=	BarkCoarseGenerationConfig
				elif model_type == "fine":
								A__						=	BarkFineModel
								A__						=	BarkFineConfig
								A__						=	BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				A__						=	F'{model_type}_small' if use_small else model_type
				A__						=	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__						=	torch.load(UpperCamelCase__		,				map_location=UpperCamelCase__					)
				# this is a hack
				A__						=	checkpoint["model_args"]
				if "input_vocab_size" not in model_args:
								A__						=	model_args["vocab_size"]
								A__						=	model_args["vocab_size"]
								del model_args["vocab_size"]

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

				A__						=	ConfigClass(**checkpoint["model_args"]					)
				A__						=	ModelClass(config=UpperCamelCase__					)
				A__						=	GenerationConfigClass()

				A__						=	model_generation_config
				A__						=	checkpoint["model"]
				# fixup checkpoint
				A__						=	"_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__						=	k[len(UpperCamelCase__					) :]
												for old_layer_name in new_layer_name_dict:
																A__						=	new_k.replace(UpperCamelCase__		,				new_layer_name_dict[old_layer_name]					)

												A__						=	state_dict.pop(UpperCamelCase__					)

				A__						=	set(state_dict.keys()					) - set(model.state_dict().keys()					)
				A__						=	{k for k in extra_keys if not k.endswith(".attn.bias"					)}
				A__						=	set(model.state_dict().keys()					) - set(state_dict.keys()					)
				A__						=	{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__						=	model.num_parameters(exclude_embeddings=UpperCamelCase__					)
				A__						=	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__							(		SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]=False		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]="text"					)	->					int:




				'''simple docstring'''





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

				A__						=	"cpu"  # do conversion on cpu

				A__						=	_get_ckpt_path(UpperCamelCase__		,				use_small=UpperCamelCase__					)
				A__						=	_load_model(UpperCamelCase__		,				UpperCamelCase__		,				model_type=UpperCamelCase__		,				use_small=UpperCamelCase__					)

				# load bark initial model
				A__						=	_bark_load_model(UpperCamelCase__		,				"cpu"		,				model_type=UpperCamelCase__		,				use_small=UpperCamelCase__					)

				if model_type == "text":
								A__						=	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__						=	5
				A__						=	1_0

				if model_type in ["text", "coarse"]:
								A__						=	torch.randint(2_5_6		,				(batch_size, sequence_length)		,				dtype=torch.int					)
								A__						=	bark_model(UpperCamelCase__					)[0]

								A__						=	model(UpperCamelCase__					)

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

				else:
								A__						=	3
								A__						=	8
								A__						=	torch.randint(2_5_6		,				(batch_size, sequence_length, n_codes_total)		,				dtype=torch.int					)

								A__						=	model(UpperCamelCase__		,				UpperCamelCase__					)
								A__						=	bark_model(UpperCamelCase__		,				UpperCamelCase__					)

								A__						=	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__							(		SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							Any		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							List[str]		,				SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							List[Any]		,				)	->					List[Any]:




				'''simple docstring'''





				A__						=	os.path.join(UpperCamelCase__		,				UpperCamelCase__					)

				A__						=	BarkSemanticConfig.from_pretrained(os.path.join(UpperCamelCase__		,				"config.json"					)					)
				A__						=	BarkCoarseConfig.from_pretrained(os.path.join(UpperCamelCase__		,				"config.json"					)					)
				A__						=	BarkFineConfig.from_pretrained(os.path.join(UpperCamelCase__		,				"config.json"					)					)
				A__						=	EncodecConfig.from_pretrained("facebook/encodec_24khz"					)

				A__						=	BarkSemanticModel.from_pretrained(UpperCamelCase__					)
				A__						=	BarkCoarseModel.from_pretrained(UpperCamelCase__					)
				A__						=	BarkFineModel.from_pretrained(UpperCamelCase__					)
				A__						=	EncodecModel.from_pretrained("facebook/encodec_24khz"					)

				A__						=	BarkConfig.from_sub_model_configs(
				    UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__					)

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

				A__						=	BarkModel(UpperCamelCase__					)

				A__						=	semantic
				A__						=	coarseAcoustic
				A__						=	fineAcoustic
				A__						=	codec

				A__						=	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)




from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase		:   Any										=       logging.get_logger(__name__)


_UpperCAmelCase		:   Dict										=       {
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_UpperCAmelCase		:   Tuple										=       111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_UpperCAmelCase		:   List[str]										=       0

_UpperCAmelCase		:   Any										=       0xE000
_UpperCAmelCase		:   Dict										=       0xE001
_UpperCAmelCase		:   Optional[int]										=       0xE002
_UpperCAmelCase		:   Tuple										=       0xE003
_UpperCAmelCase		:   Tuple										=       0xE004

# Maps special codepoints to human-readable names.
_UpperCAmelCase		:   Dict[int, str]										=       {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_UpperCAmelCase		:   Dict[str, int]										=       {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: str       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




				def __init__(					self      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=chr(snake_case       )      ,   snake_case=False      ,   snake_case=2048      ,   **snake_case      ,   ):
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else bos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else eos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else sep_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else cls_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else mask_token

											super().__init__(
											    bos_token=snake_case      ,   eos_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   model_max_length=snake_case      ,   **snake_case      ,   )

											# Creates a mapping for looking up the IDs of special symbols.
											snake_case_							=    {}
											for codepoint, name in SPECIAL_CODEPOINTS.items():
																		snake_case_							=    codepoint

											# Creates a mapping for looking up the string forms of special symbol IDs.
											snake_case_							=    {
											    codepoint: name for name, codepoint in self._special_codepoints.items()
											}

											snake_case_							=    UNICODE_VOCAB_SIZE
											snake_case_							=    len(self._special_codepoints       )




				@property
				def     a							(					self       ):
											return self._unicode_vocab_size




				def     a							(					self      ,   snake_case       ):
											return list(snake_case       )




				def     a							(					self      ,   snake_case       ):
											try:
																		return ord(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid token: \'{token}\''''       )




				def     a							(					self      ,   snake_case       ):
											try:
																		if index in SPECIAL_CODEPOINTS:
																									return SPECIAL_CODEPOINTS[index]
																		return chr(snake_case       )
											except TypeError:
																		raise ValueError(F'''invalid id: {index}'''       )




				def     a							(					self      ,   snake_case       ):
											return "".join(snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    cls + token_ids_a + sep
											if token_ids_a is not None:
																		result += token_ids_a + sep
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = False       ):
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case      ,   token_ids_a=snake_case      ,   already_has_special_tokens=snake_case       )

											snake_case_							=    [1] + ([0] * len(snake_case       )) + [1]
											if token_ids_a is not None:
																		result += ([0] * len(snake_case       )) + [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]

											snake_case_							=    len(cls + token_ids_a + sep       ) * [0]
											if token_ids_a is not None:
																		result += len(token_ids_a + sep       ) * [1]
											return result




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											return ()




