
"""simple docstring"""






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

set_seed(770)


lowerCamelCase__ :       Union[str, 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",
}


lowerCamelCase__ :       List[str]						     =			{
    "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__ :       Optional[Any]						     =			os.path.dirname(os.path.abspath(__file__))
lowerCamelCase__ :       int						     =			os.path.join(os.path.expanduser("~"), ".cache")
lowerCamelCase__ :       Dict						     =			os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")





def __A						(   a_ :							List[Any]		,		a_ :							Dict=False     )->							Any:




		'''simple docstring'''






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





def __A						(   a_ :							List[Any]		,		a_ :							Any     )->							List[Any]:




		'''simple docstring'''






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





def __A						(   a_ :							Dict		,		a_ :							int		,		a_ :							Dict=False		,		a_ :							Dict="text"     )->							str:




		'''simple docstring'''






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

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

		SCREAMING_SNAKE_CASE       :     str				= ConfigClass(**checkpoint['''model_args''']     )
		SCREAMING_SNAKE_CASE       :     List[str]				= ModelClass(config=a_     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= GenerationConfigClass()

		SCREAMING_SNAKE_CASE       :     Optional[Any]				= model_generation_config
		SCREAMING_SNAKE_CASE       :     int				= checkpoint['model']
		# fixup checkpoint
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= '_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
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= k[len(a_     ) :]
						for old_layer_name in new_layer_name_dict:
								SCREAMING_SNAKE_CASE       :     List[Any]				= new_k.replace(a_		,		new_layer_name_dict[old_layer_name]     )

						SCREAMING_SNAKE_CASE       :     Optional[int]				= state_dict.pop(a_     )

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




		'''simple docstring'''






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

		SCREAMING_SNAKE_CASE       :     List[str]				= 'cpu'  # do conversion on cpu

		SCREAMING_SNAKE_CASE       :     str				= _get_ckpt_path(a_		,		use_small=a_     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= _load_model(a_		,		a_		,		model_type=a_		,		use_small=a_     )

		# load bark initial model
		SCREAMING_SNAKE_CASE       :     Optional[int]				= _bark_load_model(a_		,		'''cpu'''		,		model_type=a_		,		use_small=a_     )

		if model_type == "text":
				SCREAMING_SNAKE_CASE       :     str				= 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
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 5
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10

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

				SCREAMING_SNAKE_CASE       :     Optional[int]				= model(a_     )

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

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

				SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(a_		,		a_     )
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= bark_model(a_		,		a_     )

				SCREAMING_SNAKE_CASE       :     List[str]				= 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 __A						(   a_ :							List[str]		,		a_ :							Dict		,		a_ :							Optional[int]		,		a_ :							int		,		a_ :							List[str]		,		a_ :							str		,		)->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= os.path.join(a_		,		a_     )

		SCREAMING_SNAKE_CASE       :     List[Any]				= BarkSemanticConfig.from_pretrained(os.path.join(a_		,		'''config.json'''     )     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= BarkCoarseConfig.from_pretrained(os.path.join(a_		,		'''config.json'''     )     )
		SCREAMING_SNAKE_CASE       :     Any				= BarkFineConfig.from_pretrained(os.path.join(a_		,		'''config.json'''     )     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= EncodecConfig.from_pretrained('''facebook/encodec_24khz'''     )

		SCREAMING_SNAKE_CASE       :     List[str]				= BarkSemanticModel.from_pretrained(a_     )
		SCREAMING_SNAKE_CASE       :     Tuple				= BarkCoarseModel.from_pretrained(a_     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= BarkFineModel.from_pretrained(a_     )
		SCREAMING_SNAKE_CASE       :     Optional[int]				= EncodecModel.from_pretrained('''facebook/encodec_24khz'''     )

		SCREAMING_SNAKE_CASE       :     List[Any]				= BarkConfig.from_sub_model_configs(
		    a_		,		a_		,		a_		,		a_     )

		SCREAMING_SNAKE_CASE       :     Optional[int]				= BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config		,		coarseAcoustic.generation_config		,		fineAcoustic.generation_config     )

		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= BarkModel(a_     )

		SCREAMING_SNAKE_CASE       :     List[Any]				= semantic
		SCREAMING_SNAKE_CASE       :     int				= coarseAcoustic
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= fineAcoustic
		SCREAMING_SNAKE_CASE       :     Any				= codec

		SCREAMING_SNAKE_CASE       :     Dict				= bark_generation_config

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


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

					lowerCamelCase__ :       Optional[int]						     =			parser.parse_args()

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







"""simple docstring"""






from __future__ import annotations

from fractions import Fraction





def __A						(   a_ :							int		,		a_ :							int     )->							bool:




		'''simple docstring'''






		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)





def __A						(   a_ :							int     )->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
		SCREAMING_SNAKE_CASE       :     List[str]				= 11
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= int('''1''' + '''0''' * digit_len     )
		for num in range(a_		,		a_     ):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(a_		,		a_     ):
										solutions.append(F"{num}/{den}"     )
						den += 1
				num += 1
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10
		return solutions





def __A						(   a_ :							int = 2     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 1.0
		for fraction in fraction_list(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= Fraction(a_     )
				result *= frac.denominator / frac.numerator
		return int(a_     )


if __name__ == "__main__":
					print(solution())





