





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()
UpperCAmelCase						:   int			  =     logging.get_logger(__name__)

set_seed(7_70)


UpperCAmelCase						:   Optional[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''',
}


UpperCAmelCase						:   List[Any]			  =     {
    '''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''',
    },
}

UpperCAmelCase						:   Tuple			  =     os.path.dirname(os.path.abspath(__file__))
UpperCAmelCase						:   str			  =     os.path.join(os.path.expanduser('''~'''), '''.cache''')
UpperCAmelCase						:   Dict			  =     os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')







def  _SCREAMING_SNAKE_CASE				(   a					,						a=False  )      ->      Any:
					__A	:						Dict       =				model_type
					if use_small:
										key += "_small"
					return os.path.join(a					,						REMOTE_MODEL_PATHS[key]['file_name']  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      List[str]:
					os.makedirs(a					,						exist_ok=a  )
					hf_hub_download(repo_id=a					,						filename=a					,						local_dir=a  )







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

					# convert Bark model arguments to HF Bark model arguments
					__A	:						List[Any]       =				model_args.pop('n_head'  )
					__A	:						Union[str, Any]       =				model_args.pop('n_embd'  )
					__A	:						Union[str, Any]       =				model_args.pop('n_layer'  )

					__A	:						str       =				ConfigClass(**checkpoint['model_args']  )
					__A	:						Optional[int]       =				ModelClass(config=a  )
					__A	:						int       =				GenerationConfigClass()

					__A	:						Optional[int]       =				model_generation_config
					__A	:						Optional[int]       =				checkpoint['model']
					# fixup checkpoint
					__A	:						Union[str, 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
															__A	:						Union[str, Any]       =				k[len(a  ) :]
															for old_layer_name in new_layer_name_dict:
																				__A	:						Tuple       =				new_k.replace(a					,						new_layer_name_dict[old_layer_name]  )

															__A	:						List[Any]       =				state_dict.pop(a  )

					__A	:						Any       =				set(state_dict.keys()  ) - set(model.state_dict().keys()  )
					__A	:						Any       =				{k for k in extra_keys if not k.endswith('.attn.bias'  )}
					__A	:						Optional[int]       =				set(model.state_dict().keys()  ) - set(state_dict.keys()  )
					__A	:						Union[str, 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  )
					__A	:						str       =				model.num_parameters(exclude_embeddings=a  )
					__A	:						Union[str, 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  _SCREAMING_SNAKE_CASE				(   a					,						a=False					,						a="text"  )      ->      Tuple:
					if model_type not in ("text", "coarse", "fine"):
										raise NotImplementedError()

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

					__A	:						Optional[int]       =				_get_ckpt_path(a					,						use_small=a  )
					__A	:						List[str]       =				_load_model(a					,						a					,						model_type=a					,						use_small=a  )

					# load bark initial model
					__A	:						str       =				_bark_load_model(a					,						'cpu'					,						model_type=a					,						use_small=a  )

					if model_type == "text":
										__A	:						Union[str, Any]       =				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
					__A	:						Tuple       =				5
					__A	:						Optional[Any]       =				10

					if model_type in ["text", "coarse"]:
										__A	:						Optional[int]       =				torch.randint(2_56					,						(batch_size, sequence_length)					,						dtype=torch.int  )
										__A	:						List[str]       =				bark_model(a  )[0]

										__A	:						List[str]       =				model(a  )

										# take last logits
										__A	:						List[str]       =				output_new_model_total.logits[:, [-1], :]

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

										__A	:						int       =				model(a					,						a  )
										__A	:						Union[str, Any]       =				bark_model(a					,						a  )

										__A	:						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  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						a					,						a					,						)      ->      Optional[int]:
					__A	:						int       =				os.path.join(a					,						a  )

					__A	:						Optional[int]       =				BarkSemanticConfig.from_pretrained(os.path.join(a					,						'config.json'  )  )
					__A	:						Optional[int]       =				BarkCoarseConfig.from_pretrained(os.path.join(a					,						'config.json'  )  )
					__A	:						Optional[int]       =				BarkFineConfig.from_pretrained(os.path.join(a					,						'config.json'  )  )
					__A	:						List[Any]       =				EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

					__A	:						Any       =				BarkSemanticModel.from_pretrained(a  )
					__A	:						List[Any]       =				BarkCoarseModel.from_pretrained(a  )
					__A	:						Dict       =				BarkFineModel.from_pretrained(a  )
					__A	:						Optional[Any]       =				EncodecModel.from_pretrained('facebook/encodec_24khz'  )

					__A	:						int       =				BarkConfig.from_sub_model_configs(
					    a					,						a					,						a					,						a  )

					__A	:						int       =				BarkGenerationConfig.from_sub_model_configs(
					    semantic.generation_config					,						coarseAcoustic.generation_config					,						fineAcoustic.generation_config  )

					__A	:						Union[str, Any]       =				BarkModel(a  )

					__A	:						Optional[Any]       =				semantic
					__A	:						Tuple       =				coarseAcoustic
					__A	:						List[Any]       =				fineAcoustic
					__A	:						Tuple       =				codec

					__A	:						List[Any]       =				bark_generation_config

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


if __name__ == "__main__":
				UpperCAmelCase						:   Optional[Any]			  =     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.''')

				UpperCAmelCase						:   str			  =     parser.parse_args()

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











from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A = None     ):
									if components is None:
														__A	:						int       =				[]
									__A	:						Tuple       =				list(_A     )



				def __len__(			self     ):
									return len(self.__components     )



				def __str__(			self     ):
									return "(" + ",".join(map(_A     ,					self.__components     )     ) + ")"



				def __add__(			self     ,					_A     ):
									__A	:						Optional[int]       =				len(self     )
									if size == len(_A     ):
														__A	:						Any       =				[self.__components[i] + other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:
														raise Exception('must have the same size'     )



				def __sub__(			self     ,					_A     ):
									__A	:						Tuple       =				len(self     )
									if size == len(_A     ):
														__A	:						Union[str, Any]       =				[self.__components[i] - other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:  # error case
														raise Exception('must have the same size'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					(float, int)     ):
														__A	:						str       =				[c * other for c in self.__components]
														return Vector(_A     )
									elif isinstance(_A     ,					_A     ) and len(self     ) == len(_A     ):
														__A	:						Union[str, Any]       =				len(self     )
														__A	:						Dict       =				[self.__components[i] * other.component(_A     ) for i in range(_A     )]
														return sum(_A     )
									else:  # error case
														raise Exception('invalid operand!'     )



				def 					UpperCAmelCase_		(			self     ):
									return Vector(self.__components     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ) and -len(self.__components     ) <= i < len(self.__components     ):
														return self.__components[i]
									else:
														raise Exception('index out of range'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									assert -len(self.__components     ) <= pos < len(self.__components     )
									__A	:						Optional[int]       =				value



				def 					UpperCAmelCase_		(			self     ):
									if len(self.__components     ) == 0:
														raise Exception('Vector is empty'     )
									__A	:						Optional[Any]       =				[c**2 for c in self.__components]
									return math.sqrt(sum(_A     )     )





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = False     ):
									__A	:						Optional[Any]       =				self * other
									__A	:						Optional[Any]       =				self.euclidean_length() * other.euclidean_length()
									if deg:
														return math.degrees(math.acos(num / den     )     )
									else:
														return math.acos(num / den     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Vector:
					assert isinstance(a					,						a  )
					return Vector([0] * dimension  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Vector:
					assert isinstance(a					,						a  ) and (isinstance(a					,						a  ))
					__A	:						Optional[Any]       =				[0] * dimension
					__A	:						Tuple       =				1
					return Vector(a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					assert (
					    isinstance(a					,						a  )
					    and isinstance(a					,						a  )
					    and (isinstance(a					,						(int, float)  ))
					)
					return x * scalar + y







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					random.seed(a  )
					__A	:						str       =				[random.randint(a					,						a  ) for _ in range(a  )]
					return Vector(a  )





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[Any]       =				matrix
									__A	:						Dict       =				w
									__A	:						Optional[int]       =				h



				def __str__(			self     ):
									__A	:						Tuple       =				''
									for i in range(self.__height     ):
														ans += "|"
														for j in range(self.__width     ):
																			if j < self.__width - 1:
																								ans += str(self.__matrix[i][j]     ) + ","
																			else:
																								ans += str(self.__matrix[i][j]     ) + "|\n"
									return ans



				def __add__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Optional[Any]       =				[]
														for i in range(self.__height     ):
																			__A	:						Optional[Any]       =				[
																			    self.__matrix[i][j] + other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrix must have the same dimension!'     )



				def __sub__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Tuple       =				[]
														for i in range(self.__height     ):
																			__A	:						str       =				[
																			    self.__matrix[i][j] - other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrices must have the same dimension!'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ):  # matrix-vector
														if len(_A     ) == self.__width:
																			__A	:						List[Any]       =				zero_vector(self.__height     )
																			for i in range(self.__height     ):
																								__A	:						List[str]       =				[
																								    self.__matrix[i][j] * other.component(_A     )
																								    for j in range(self.__width     )
																								]
																								ans.change_component(_A     ,					sum(_A     )     )
																			return ans
														else:
																			raise Exception(
																			    'vector must have the same size as the '
																			    'number of columns of the matrix!'     )
									elif isinstance(_A     ,					(int, float)     ):  # matrix-scalar
														__A	:						List[str]       =				[
														    [self.__matrix[i][j] * other for j in range(self.__width     )]
														    for i in range(self.__height     )
														]
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									return None



				def 					UpperCAmelCase_		(			self     ):
									return self.__height



				def 					UpperCAmelCase_		(			self     ):
									return self.__width



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														__A	:						int       =				value
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									__A	:						List[str]       =				self.__matrix[:x] + self.__matrix[x + 1 :]
									for i in range(len(_A     )     ):
														__A	:						Optional[int]       =				minor[i][:y] + minor[i][y + 1 :]
									return Matrix(_A     ,					self.__width - 1     ,					self.__height - 1     ).determinant()



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(_A     ,					_A     )
									else:
														raise Exception('Indices out of bounds'     )





				def 					UpperCAmelCase_		(			self     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if self.__height < 1:
														raise Exception('Matrix has no element'     )
									elif self.__height == 1:
														return self.__matrix[0][0]
									elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
									else:
														__A	:						List[str]       =				[
														    self.__matrix[0][y] * self.cofactor(0     ,					_A     ) for y in range(self.__width     )
														]
														return sum(_A     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Matrix:
					__A	:						list[list[float]]       =				[[0] * n for _ in range(a  )]
					return Matrix(a					,						a					,						a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a  )      ->      Matrix:
					random.seed(a  )
					__A	:						list[list[float]]       =				[
					    [random.randint(a					,						a  ) for _ in range(a  )] for _ in range(a  )
					]
					return Matrix(a					,						a					,						a  )




