


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()
_lowercase       :      Any	=	logging.get_logger(__name__)

set_seed(770)


_lowercase       :      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",
}


_lowercase       :      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",
    },
}

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





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     int=False						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Any						) ->   Any:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     str=False							,					UpperCamelCase__:     Tuple="text"						) ->   List[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				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Optional[int]=False							,					UpperCamelCase__:     Tuple="text"						) ->   List[str]:





	"""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								=				10

	if model_type in ["text", "coarse"]:
		A								=				torch.randint(2_56							,					(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_56							,					(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				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     List[str]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     str							,					UpperCamelCase__:     int							,					UpperCamelCase__:     str							,					) ->   Union[str, 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__":
				_lowercase       :      Dict	=	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.")

				_lowercase       :      Union[str, Any]	=	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 typing import Any






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__				,   a__ = 0   )	->				None:
					A					,			A								=				row, column
					A								=				[[default_value for c in range(a__   )] for r in range(a__   )]

				def __str__(						self   )	->				str:
					A								=				f'Matrix consist of {self.row} rows and {self.column} columns\n'

					# Make string identifier
					A								=				0
					for row_vector in self.array:
						for obj in row_vector:
							A								=				max(a__				,   len(str(a__   )   )   )
					A								=				f'%{max_element_length}s'

					# Make string and return
					def single_line(a__   ) -> str:
						nonlocal string_format_identifier
						A								=				"""["""
						line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
						line += "]"
						return line

					s += "\n".join(single_line(a__   ) for row_vector in self.array   )
					return s

				def __repr__(						self   )	->				str:
					return str(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				bool:
					if not (isinstance(a__				,   (list, tuple)   ) and len(a__   ) == 2):
						return False
					elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
						return False
					else:
						return True

				def __getitem__(						self				,   a__   )	->				Any:
					assert self.validate_indicies(a__   )
					return self.array[loc[0]][loc[1]]

				def __setitem__(						self				,   a__				,   a__   )	->				None:
					assert self.validate_indicies(a__   )
					A								=				value

				def __add__(						self				,   a__   )	->				Matrix:
					assert isinstance(a__				,   a__   )
					assert self.row == another.row and self.column == another.column

					# Add
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c] + another[r, c]
					return result

				def __neg__(						self   )	->				Matrix:
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				-self[r, c]
					return result

				def __sub__(						self				,   a__   )	->				Matrix:
					return self + (-another)

				def __mul__(						self				,   a__   )	->				Matrix:

					if isinstance(a__				,   (int, float)   ):  # Scalar multiplication
						A								=				Matrix(self.row				,   self.column   )
						for r in range(self.row   ):
							for c in range(self.column   ):
								A								=				self[r, c] * another
						return result
					elif isinstance(a__				,   a__   ):  # Matrix multiplication
						assert self.column == another.row
						A								=				Matrix(self.row				,   another.column   )
						for r in range(self.row   ):
							for c in range(another.column   ):
								for i in range(self.column   ):
									result[r, c] += self[r, i] * another[i, c]
						return result
					else:
						A								=				f'Unsupported type given for another ({type(a__   )})'
						raise TypeError(a__   )

				def      _UpperCAmelCase					(						self   )	->				Matrix:
					A								=				Matrix(self.column				,   self.row   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c]
					return result

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Any:
					assert isinstance(a__				,   a__   ) and isinstance(a__				,   a__   )
					assert self.row == self.column == u.row == v.row  # u, v should be column vector
					assert u.column == v.column == 1  # u, v should be column vector

					# Calculate
					A								=				v.transpose()
					A								=				(v_t * self * u)[0, 0] + 1
					if numerator_factor == 0:
						return None  # It's not invertable
					return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					A								=				Matrix(3							,					3							,					0						)
					for i in range(3						):
						A								=				1
					print(f'a^(-1) is {ainv}'						)
					# u, v
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				1, 2, -3
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				4, -2, 5
					print(f'u is {u}'						)
					print(f'v is {v}'						)
					print(f'uv^T is {u * v.transpose()}'						)
					# Sherman Morrison
					print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(UpperCamelCase__							,					UpperCamelCase__						)}'						)





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					import doctest

					doctest.testmod()
				testa()



