
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    a      ( a		,						a=False	)     ->List[Any]:




			'''simple docstring'''


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


def    a      ( a		,						a	)     ->Any:




			'''simple docstring'''


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


def    a      ( a		,						a		,						a=False		,						a="text"	)     ->Tuple:




			'''simple docstring'''


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

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

			SCREAMING_SNAKE_CASE     						= ConfigClass(**checkpoint['''model_args''']	)
			SCREAMING_SNAKE_CASE     						= ModelClass(config=a	)
			SCREAMING_SNAKE_CASE     						= GenerationConfigClass()

			SCREAMING_SNAKE_CASE     						= model_generation_config
			SCREAMING_SNAKE_CASE     						= checkpoint['''model''']
			# fixup checkpoint
			SCREAMING_SNAKE_CASE     						= '''_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     						= k[len(a	) :]
									for old_layer_name in new_layer_name_dict:
												SCREAMING_SNAKE_CASE     						= new_k.replace(a		,						new_layer_name_dict[old_layer_name]	)

									SCREAMING_SNAKE_CASE     						= state_dict.pop(a	)

			SCREAMING_SNAKE_CASE     						= set(state_dict.keys()	) - set(model.state_dict().keys()	)
			SCREAMING_SNAKE_CASE     						= {k for k in extra_keys if not k.endswith('''.attn.bias'''	)}
			SCREAMING_SNAKE_CASE     						= set(model.state_dict().keys()	) - set(state_dict.keys()	)
			SCREAMING_SNAKE_CASE     						= {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     						= model.num_parameters(exclude_embeddings=a	)
			SCREAMING_SNAKE_CASE     						= 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		,						a=False		,						a="text"	)     ->int:




			'''simple docstring'''


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

			SCREAMING_SNAKE_CASE     						= '''cpu'''  # do conversion on cpu

			SCREAMING_SNAKE_CASE     						= _get_ckpt_path(a		,						use_small=a	)
			SCREAMING_SNAKE_CASE     						= _load_model(a		,						a		,						model_type=a		,						use_small=a	)

			# load bark initial model
			SCREAMING_SNAKE_CASE     						= _bark_load_model(a		,						'''cpu'''		,						model_type=a		,						use_small=a	)

			if model_type == "text":
						SCREAMING_SNAKE_CASE     						= 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     						= 5
			SCREAMING_SNAKE_CASE     						= 10

			if model_type in ["text", "coarse"]:
						SCREAMING_SNAKE_CASE     						= torch.randint(256		,						(batch_size, sequence_length)		,						dtype=torch.int	)
						SCREAMING_SNAKE_CASE     						= bark_model(a	)[0]

						SCREAMING_SNAKE_CASE     						= model(a	)

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

			else:
						SCREAMING_SNAKE_CASE     						= 3
						SCREAMING_SNAKE_CASE     						= 8
						SCREAMING_SNAKE_CASE     						= torch.randint(256		,						(batch_size, sequence_length, n_codes_total)		,						dtype=torch.int	)

						SCREAMING_SNAKE_CASE     						= model(a		,						a	)
						SCREAMING_SNAKE_CASE     						= bark_model(a		,						a	)

						SCREAMING_SNAKE_CASE     						= 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		,						a		,						a		,						a		,						a		,						a		,						)     ->Optional[int]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= os.path.join(a		,						a	)

			SCREAMING_SNAKE_CASE     						= BarkSemanticConfig.from_pretrained(os.path.join(a		,						'''config.json'''	)	)
			SCREAMING_SNAKE_CASE     						= BarkCoarseConfig.from_pretrained(os.path.join(a		,						'''config.json'''	)	)
			SCREAMING_SNAKE_CASE     						= BarkFineConfig.from_pretrained(os.path.join(a		,						'''config.json'''	)	)
			SCREAMING_SNAKE_CASE     						= EncodecConfig.from_pretrained('''facebook/encodec_24khz'''	)

			SCREAMING_SNAKE_CASE     						= BarkSemanticModel.from_pretrained(a	)
			SCREAMING_SNAKE_CASE     						= BarkCoarseModel.from_pretrained(a	)
			SCREAMING_SNAKE_CASE     						= BarkFineModel.from_pretrained(a	)
			SCREAMING_SNAKE_CASE     						= EncodecModel.from_pretrained('''facebook/encodec_24khz'''	)

			SCREAMING_SNAKE_CASE     						= BarkConfig.from_sub_model_configs(
			    a		,						a		,						a		,						a	)

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

			SCREAMING_SNAKE_CASE     						= BarkModel(a	)

			SCREAMING_SNAKE_CASE     						= semantic
			SCREAMING_SNAKE_CASE     						= coarseAcoustic
			SCREAMING_SNAKE_CASE     						= fineAcoustic
			SCREAMING_SNAKE_CASE     						= codec

			SCREAMING_SNAKE_CASE     						= bark_generation_config

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


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 __future__ import annotations

from decimal import Decimal

from numpy import array

def lowercase    (  SCREAMING_SNAKE_CASE )			->							list[list[float]]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(SCREAMING_SNAKE_CASE ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
						# Calculate the determinant of the matrix
						SCREAMING_SNAKE_CASE_								=						float(
						    d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
						if determinant == 0:
									raise ValueError('This matrix has no inverse.' )

						# Creates a copy of the matrix with swapped positions of the elements
						SCREAMING_SNAKE_CASE_								=						[[0.0, 0.0], [0.0, 0.0]]
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						matrix[1][1], matrix[0][0]
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						-matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(SCREAMING_SNAKE_CASE ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(SCREAMING_SNAKE_CASE ) == 3
			    and len(matrix[0] ) == 3
			    and len(matrix[1] ) == 3
			    and len(matrix[2] ) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						SCREAMING_SNAKE_CASE_								=						float(
						    (
						        (d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
						        + (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
						        + (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
						    )
						    - (
						        (d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
						        + (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
						        + (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
						    ) )
						if determinant == 0:
									raise ValueError('This matrix has no inverse.' )

						# Creating cofactor matrix
						SCREAMING_SNAKE_CASE_								=						[
						    [d(0.0 ), d(0.0 ), d(0.0 )],
						    [d(0.0 ), d(0.0 ), d(0.0 )],
						    [d(0.0 ), d(0.0 ), d(0.0 )],
						]
						SCREAMING_SNAKE_CASE_								=						(d(matrix[1][1] ) * d(matrix[2][2] )) - (
						    d(matrix[1][2] ) * d(matrix[2][1] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[1][0] ) * d(matrix[2][1] )) - (
						    d(matrix[1][1] ) * d(matrix[2][0] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][0] ) * d(matrix[2][2] )) - (
						    d(matrix[0][2] ) * d(matrix[2][0] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][1] ) * d(matrix[1][2] )) - (
						    d(matrix[0][2] ) * d(matrix[1][1] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][0] ) * d(matrix[1][1] )) - (
						    d(matrix[0][1] ) * d(matrix[1][0] )
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						SCREAMING_SNAKE_CASE_								=						array(SCREAMING_SNAKE_CASE )
						for i in range(3 ):
									for j in range(3 ):
												SCREAMING_SNAKE_CASE_								=						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						SCREAMING_SNAKE_CASE_								=						array(SCREAMING_SNAKE_CASE )
						for i in range(3 ):
									for j in range(3 ):
												inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE )

        # Calculate the inverse of the matrix
						return [[float(d(SCREAMING_SNAKE_CASE ) ) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError('Please provide a matrix of size 2x2 or 3x3.' )
