






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()
snake_case_							=			logging.get_logger(__name__)

set_seed(7_7_0)


snake_case_							=			{
    """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""",
}


snake_case_							=			{
    """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""",
    },
}

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


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=False		)   ->     Union[str, Any]:
		lowerCamelCase   : Tuple								=model_type
		if use_small:
				key += "_small"
		return os.path.join(_lowerCamelCase							,       REMOTE_MODEL_PATHS[key]['''file_name''']		)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     int:
		os.makedirs(_lowerCamelCase							,       exist_ok=_lowerCamelCase		)
		hf_hub_download(repo_id=_lowerCamelCase							,       filename=_lowerCamelCase							,       local_dir=_lowerCamelCase		)


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

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

		lowerCamelCase   : Union[str, Any]								=ConfigClass(**checkpoint['''model_args''']		)
		lowerCamelCase   : Optional[int]								=ModelClass(config=_lowerCamelCase		)
		lowerCamelCase   : Optional[int]								=GenerationConfigClass()

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

						lowerCamelCase   : Optional[int]								=state_dict.pop(_lowerCamelCase		)

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

		return model


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_="text"		)   ->     Any:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		lowerCamelCase   : Any								='''cpu'''  # do conversion on cpu

		lowerCamelCase   : str								=_get_ckpt_path(_lowerCamelCase							,       use_small=_lowerCamelCase		)
		lowerCamelCase   : Union[str, Any]								=_load_model(_lowerCamelCase							,       _lowerCamelCase							,       model_type=_lowerCamelCase							,       use_small=_lowerCamelCase		)

		# load bark initial model
		lowerCamelCase   : Any								=_bark_load_model(_lowerCamelCase							,       '''cpu'''							,       model_type=_lowerCamelCase							,       use_small=_lowerCamelCase		)

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

		if model.num_parameters(exclude_embeddings=_lowerCamelCase		) != 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   : Union[str, Any]								=5
		lowerCamelCase   : int								=1_0

		if model_type in ["text", "coarse"]:
				lowerCamelCase   : Optional[Any]								=torch.randint(2_5_6							,       (batch_size, sequence_length)							,       dtype=torch.int		)
				lowerCamelCase   : Tuple								=bark_model(_lowerCamelCase		)[0]

				lowerCamelCase   : Union[str, Any]								=model(_lowerCamelCase		)

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

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

				lowerCamelCase   : Union[str, Any]								=model(_lowerCamelCase							,       _lowerCamelCase		)
				lowerCamelCase   : List[str]								=bark_model(_lowerCamelCase							,       _lowerCamelCase		)

				lowerCamelCase   : 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(_lowerCamelCase		).mkdir(exist_ok=_lowerCamelCase		)
		model.save_pretrained(_lowerCamelCase		)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       )   ->     Tuple:
		lowerCamelCase   : str								=os.path.join(_lowerCamelCase							,       _lowerCamelCase		)

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

		lowerCamelCase   : Any								=BarkSemanticModel.from_pretrained(_lowerCamelCase		)
		lowerCamelCase   : Union[str, Any]								=BarkCoarseModel.from_pretrained(_lowerCamelCase		)
		lowerCamelCase   : int								=BarkFineModel.from_pretrained(_lowerCamelCase		)
		lowerCamelCase   : Any								=EncodecModel.from_pretrained('''facebook/encodec_24khz'''		)

		lowerCamelCase   : List[Any]								=BarkConfig.from_sub_model_configs(
		    _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase		)

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

		lowerCamelCase   : str								=BarkModel(_lowerCamelCase		)

		lowerCamelCase   : Any								=semantic
		lowerCamelCase   : Tuple								=coarseAcoustic
		lowerCamelCase   : Optional[int]								=fineAcoustic
		lowerCamelCase   : str								=codec

		lowerCamelCase   : Any								=bark_generation_config

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


if __name__ == "__main__":
				snake_case_							=			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.''')

				snake_case_							=			parser.parse_args()

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








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = False							,       )   ->     tuple[int, float, str]:
		lowerCamelCase   : Union[str, Any]								=cipher_alphabet or [chr(SCREAMING_SNAKE_CASE_		) for i in range(9_7							,       1_2_3		)]

		# If the argument is None or the user provided an empty dictionary
		if not frequencies_dict:
				# Frequencies of letters in the english language (how much they show up)
				lowerCamelCase   : str								={
				    '''a''': 0.0_8_4_9_7,
				    '''b''': 0.0_1_4_9_2,
				    '''c''': 0.0_2_2_0_2,
				    '''d''': 0.0_4_2_5_3,
				    '''e''': 0.1_1_1_6_2,
				    '''f''': 0.0_2_2_2_8,
				    '''g''': 0.0_2_0_1_5,
				    '''h''': 0.0_6_0_9_4,
				    '''i''': 0.0_7_5_4_6,
				    '''j''': 0.0_0_1_5_3,
				    '''k''': 0.0_1_2_9_2,
				    '''l''': 0.0_4_0_2_5,
				    '''m''': 0.0_2_4_0_6,
				    '''n''': 0.0_6_7_4_9,
				    '''o''': 0.0_7_5_0_7,
				    '''p''': 0.0_1_9_2_9,
				    '''q''': 0.0_0_0_9_5,
				    '''r''': 0.0_7_5_8_7,
				    '''s''': 0.0_6_3_2_7,
				    '''t''': 0.0_9_3_5_6,
				    '''u''': 0.0_2_7_5_8,
				    '''v''': 0.0_0_9_7_8,
				    '''w''': 0.0_2_5_6_0,
				    '''x''': 0.0_0_1_5_0,
				    '''y''': 0.0_1_9_9_4,
				    '''z''': 0.0_0_0_7_7,
				}
		else:
				# Custom frequencies dictionary
				lowerCamelCase   : Tuple								=frequencies_dict

		if not case_sensitive:
				lowerCamelCase   : Any								=ciphertext.lower()

		# Chi squared statistic values
		lowerCamelCase   : dict[int, tuple[float, str]]								={}

		# cycle through all of the shifts
		for shift in range(len(SCREAMING_SNAKE_CASE_		)		):
				lowerCamelCase   : int								=''''''

				# decrypt the message with the shift
				for letter in ciphertext:
						try:
								# Try to index the letter in the alphabet
								lowerCamelCase   : Dict								=(alphabet_letters.index(letter.lower()		) - shift) % len(
								    SCREAMING_SNAKE_CASE_		)
								decrypted_with_shift += (
								    alphabet_letters[new_key].upper()
								    if case_sensitive and letter.isupper()
								    else alphabet_letters[new_key]
								)
						except ValueError:
								# Append the character if it isn't in the alphabet
								decrypted_with_shift += letter

				lowerCamelCase   : str								=0.0

				# Loop through each letter in the decoded message with the shift
				for letter in decrypted_with_shift:
						if case_sensitive:
								lowerCamelCase   : Union[str, Any]								=letter.lower()
								if letter in frequencies:
										# Get the amount of times the letter occurs in the message
										lowerCamelCase   : List[str]								=decrypted_with_shift.lower().count(SCREAMING_SNAKE_CASE_		)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										lowerCamelCase   : Optional[Any]								=frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										lowerCamelCase   : Tuple								=((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value
						else:
								if letter.lower() in frequencies:
										# Get the amount of times the letter occurs in the message
										lowerCamelCase   : Optional[int]								=decrypted_with_shift.count(SCREAMING_SNAKE_CASE_		)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										lowerCamelCase   : Union[str, Any]								=frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										lowerCamelCase   : int								=((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
				lowerCamelCase   : Dict								=(
				    chi_squared_statistic,
				    decrypted_with_shift,
				)

		# Get the most likely cipher by finding the cipher with the smallest chi squared
		# statistic
		def chi_squared_statistic_values_sorting_key(SCREAMING_SNAKE_CASE_		) -> tuple[float, str]:
				return chi_squared_statistic_values[key]

		lowerCamelCase   : int								=min(
		    SCREAMING_SNAKE_CASE_							,       key=SCREAMING_SNAKE_CASE_							,       )

		# Get all the data from the most likely cipher (key, decoded message)
		(
		    (
		    lowerCamelCase
		)		,   (
		    lowerCamelCase
		)		,   
		)   : Any								=chi_squared_statistic_values[most_likely_cipher]

		# Return the data on the most likely shift
		return (
		    most_likely_cipher,
		    most_likely_cipher_chi_squared_value,
		    decoded_most_likely_cipher,
		)

