






import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
UpperCamelCase    							=      sys.version_info >= (3, 10)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Any=None					,							__lowerCAmelCase							:					Optional[int]=None							)     ->  Tuple:
   return field(default_factory=lambda: default					,							metadata=__lowerCAmelCase							)







@dataclass
class 		_A						:
  lowercase_						:			int
  lowercase_						:			float
  lowercase_						:			str
  lowercase_						:			bool







@dataclass
class 		_A						:
  lowercase_						:			int						=					42
  lowercase_						:			str						=					field(default='''toto'''	,				metadata={'''help''': '''help message'''} )







@dataclass
class 		_A						:
  lowercase_						:			bool						=					False
  lowercase_						:			bool						=					True
  lowercase_						:			Optional[bool]						=					None







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			List[Any]						=					'''titi'''
  lowercase_						:			Optional[int]						=					'''toto'''







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Any						=					'''titi'''
  lowercase_						:			Optional[Any]						=					'''toto'''
  lowercase_						:			Tuple						=					42







@dataclass
class 		_A						:
  lowercase_						:			BasicEnum						=					"toto"

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					BasicEnum(self.foo			)







@dataclass
class 		_A						:
  lowercase_						:			MixedTypeEnum						=					"toto"

  def    a					(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					MixedTypeEnum(self.foo			)







@dataclass
class 		_A						:
  lowercase_						:			Optional[int]						=					None
  lowercase_						:			Optional[float]						=					field(default=UpperCAmelCase_	,				metadata={'''help''': '''help message'''} )
  lowercase_						:			Optional[str]						=					None
  lowercase_						:			Optional[List[str]]						=					list_field(default=[] )
  lowercase_						:			Optional[List[int]]						=					list_field(default=[] )







@dataclass
class 		_A						:
  lowercase_						:			List[int]						=					list_field(default=[] )
  lowercase_						:			List[int]						=					list_field(default=[1, 2, 3] )
  lowercase_						:			List[str]						=					list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
  lowercase_						:			List[float]						=					list_field(default=[0.1, 0.2, 0.3] )







@dataclass
class 		_A						:
  lowercase_						:			List[int]						=					field()
  lowercase_						:			str						=					field()
  lowercase_						:			BasicEnum						=					field()

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					BasicEnum(self.required_enum			)







@dataclass
class 		_A						:
  lowercase_						:			int
  lowercase_						:			"BasicEnum"						=					field()
  lowercase_						:			"Optional[bool]"						=					None
  lowercase_						:			"str"						=					field(default='''toto'''	,				metadata={'''help''': '''help message'''} )
  lowercase_						:			"List[str]"						=					list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )


if is_python_no_less_than_3_10:







       @dataclass
       class 		_A						:
         lowercase_						:			bool						=					False
         lowercase_						:			bool						=					True
         lowercase_						:			bool | None						=					None







       @dataclass
       class 		_A						:
         lowercase_						:			int | None						=					None
         lowercase_						:			float | None						=					field(default=UpperCAmelCase_	,				metadata={'''help''': '''help message'''} )
         lowercase_						:			str | None						=					None
         lowercase_						:			list[str] | None						=					list_field(default=[] )
         lowercase_						:			list[int] | None						=					list_field(default=[] )







class 		_A						( unittest.TestCase ):

  def    a					(    self						:	Optional[Any]   ,				lowerCamelCase__						:	argparse.ArgumentParser   ,				lowerCamelCase__						:	argparse.ArgumentParser			):
     """simple docstring"""

     self.assertEqual(len(a._actions			)   ,				len(b._actions			)			)
     for x, y in zip(a._actions   ,				b._actions			):
        __UpperCamelCase	:			Dict					=					{k: v for k, v in vars(lowerCamelCase__			).items() if k != """container"""}
        __UpperCamelCase	:			Dict					=					{k: v for k, v in vars(lowerCamelCase__			).items() if k != """container"""}

        # Choices with mixed type have custom function as "type"
        # So we need to compare results directly for equality
        if xx.get("""choices"""   ,				lowerCamelCase__			) and yy.get("""choices"""   ,				lowerCamelCase__			):
           for expected_choice in yy["choices"] + xx["choices"]:
              self.assertEqual(xx["""type"""](lowerCamelCase__			)   ,				yy["""type"""](lowerCamelCase__			)			)
           del xx["type"], yy["type"]

        self.assertEqual(lowerCamelCase__   ,				lowerCamelCase__			)

  def    a					(    self						:	List[str]			):
     """simple docstring"""

     __UpperCamelCase	:			List[str]					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			List[str]					=					argparse.ArgumentParser()
     expected.add_argument("""--foo"""   ,				type=lowerCamelCase__   ,				required=lowerCamelCase__			)
     expected.add_argument("""--bar"""   ,				type=lowerCamelCase__   ,				required=lowerCamelCase__			)
     expected.add_argument("""--baz"""   ,				type=lowerCamelCase__   ,				required=lowerCamelCase__			)
     expected.add_argument("""--flag"""   ,				type=lowerCamelCase__   ,				default=lowerCamelCase__   ,				const=lowerCamelCase__   ,				nargs="""?"""			)
     self.argparsersEqual(lowerCamelCase__   ,				lowerCamelCase__			)

     __UpperCamelCase	:			int					=					["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
     ((__UpperCamelCase)	,     )	:			Union[str, Any]					=					parser.parse_args_into_dataclasses(lowerCamelCase__   ,				look_for_args_file=lowerCamelCase__			)
     self.assertFalse(example.flag			)

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			List[str]					=					argparse.ArgumentParser()
     expected.add_argument("""--foo"""   ,				default=42   ,				type=lowerCamelCase__			)
     expected.add_argument("""--baz"""   ,				default="""toto"""   ,				type=lowerCamelCase__   ,				help="""help message"""			)
     self.argparsersEqual(lowerCamelCase__   ,				lowerCamelCase__			)

  def    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					argparse.ArgumentParser()
     expected.add_argument("""--foo"""   ,				type=lowerCamelCase__   ,				default=lowerCamelCase__   ,				const=lowerCamelCase__   ,				nargs="""?"""			)
     expected.add_argument("""--baz"""   ,				type=lowerCamelCase__   ,				default=lowerCamelCase__   ,				const=lowerCamelCase__   ,				nargs="""?"""			)
     # A boolean no_* argument always has to come after its "default: True" regular counter-part
     # and its default must be set to False
     expected.add_argument("""--no_baz"""   ,				action="""store_false"""   ,				default=lowerCamelCase__   ,				dest="""baz"""			)
     expected.add_argument("""--opt"""   ,				type=lowerCamelCase__   ,				default=lowerCamelCase__			)

     __UpperCamelCase	:			str					=					[WithDefaultBoolExample]
     if is_python_no_less_than_3_10:
        dataclass_types.append(lowerCamelCase__			)

     for dataclass_type in dataclass_types:
        __UpperCamelCase	:			List[str]					=					HfArgumentParser(lowerCamelCase__			)
        self.argparsersEqual(lowerCamelCase__   ,				lowerCamelCase__			)

        __UpperCamelCase	:			int					=					parser.parse_args([]			)
        self.assertEqual(lowerCamelCase__   ,				Namespace(foo=lowerCamelCase__   ,				baz=lowerCamelCase__   ,				opt=lowerCamelCase__			)			)

        __UpperCamelCase	:			Optional[Any]					=					parser.parse_args(["""--foo""", """--no_baz"""]			)
        self.assertEqual(lowerCamelCase__   ,				Namespace(foo=lowerCamelCase__   ,				baz=lowerCamelCase__   ,				opt=lowerCamelCase__			)			)

        __UpperCamelCase	:			str					=					parser.parse_args(["""--foo""", """--baz"""]			)
        self.assertEqual(lowerCamelCase__   ,				Namespace(foo=lowerCamelCase__   ,				baz=lowerCamelCase__   ,				opt=lowerCamelCase__			)			)

        __UpperCamelCase	:			Dict					=					parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""]			)
        self.assertEqual(lowerCamelCase__   ,				Namespace(foo=lowerCamelCase__   ,				baz=lowerCamelCase__   ,				opt=lowerCamelCase__			)			)

        __UpperCamelCase	:			int					=					parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""]			)
        self.assertEqual(lowerCamelCase__   ,				Namespace(foo=lowerCamelCase__   ,				baz=lowerCamelCase__   ,				opt=lowerCamelCase__			)			)

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			Optional[int]					=					argparse.ArgumentParser()
     expected.add_argument(
         """--foo"""   ,				default="""toto"""   ,				choices=["""titi""", """toto""", 42]   ,				type=make_choice_type_function(["""titi""", """toto""", 42]			)   ,				)
     self.argparsersEqual(lowerCamelCase__   ,				lowerCamelCase__			)

     __UpperCamelCase	:			List[Any]					=					parser.parse_args([]			)
     self.assertEqual(args.foo   ,				"""toto"""			)
     __UpperCamelCase	:			List[Any]					=					parser.parse_args_into_dataclasses([]			)[0]
     self.assertEqual(enum_ex.foo   ,				MixedTypeEnum.toto			)

     __UpperCamelCase	:			Tuple					=					parser.parse_args(["""--foo""", """titi"""]			)
     self.assertEqual(args.foo   ,				"""titi"""			)
     __UpperCamelCase	:			Tuple					=					parser.parse_args_into_dataclasses(["""--foo""", """titi"""]			)[0]
     self.assertEqual(enum_ex.foo   ,				MixedTypeEnum.titi			)

     __UpperCamelCase	:			Optional[Any]					=					parser.parse_args(["""--foo""", """42"""]			)
     self.assertEqual(args.foo   ,				42			)
     __UpperCamelCase	:			Dict					=					parser.parse_args_into_dataclasses(["""--foo""", """42"""]			)[0]
     self.assertEqual(enum_ex.foo   ,				MixedTypeEnum.fourtytwo			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     @dataclass
     class 		_A						:
       lowercase_						:			Literal["titi", "toto", 42]						=					"toto"







     __UpperCamelCase	:			List[Any]					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					argparse.ArgumentParser()
     expected.add_argument(
         """--foo"""   ,				default="""toto"""   ,				choices=("""titi""", """toto""", 42)   ,				type=make_choice_type_function(["""titi""", """toto""", 42]			)   ,				)
     self.argparsersEqual(lowerCamelCase__   ,				lowerCamelCase__			)

     __UpperCamelCase	:			str					=					parser.parse_args([]			)
     self.assertEqual(args.foo   ,				"""toto"""			)

     __UpperCamelCase	:			List[Any]					=					parser.parse_args(["""--foo""", """titi"""]			)
     self.assertEqual(args.foo   ,				"""titi"""			)

     __UpperCamelCase	:			str					=					parser.parse_args(["""--foo""", """42"""]			)
     self.assertEqual(args.foo   ,				42			)

  def    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[Any]					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			List[str]					=					argparse.ArgumentParser()
     expected.add_argument("""--foo_int"""   ,				nargs="""+"""   ,				default=[]   ,				type=lowerCamelCase__			)
     expected.add_argument("""--bar_int"""   ,				nargs="""+"""   ,				default=[1, 2, 3]   ,				type=lowerCamelCase__			)
     expected.add_argument("""--foo_str"""   ,				nargs="""+"""   ,				default=["""Hallo""", """Bonjour""", """Hello"""]   ,				type=lowerCamelCase__			)
     expected.add_argument("""--foo_float"""   ,				nargs="""+"""   ,				default=[0.1, 0.2, 0.3]   ,				type=lowerCamelCase__			)

     self.argparsersEqual(lowerCamelCase__   ,				lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					parser.parse_args([]			)
     self.assertEqual(
         lowerCamelCase__   ,				Namespace(foo_int=[]   ,				bar_int=[1, 2, 3]   ,				foo_str=["""Hallo""", """Bonjour""", """Hello"""]   ,				foo_float=[0.1, 0.2, 0.3]			)   ,				)

     __UpperCamelCase	:			str					=					parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split()			)
     self.assertEqual(lowerCamelCase__   ,				Namespace(foo_int=[1]   ,				bar_int=[2, 3]   ,				foo_str=["""a""", """b""", """c"""]   ,				foo_float=[0.1, 0.7]			)			)

  def    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					argparse.ArgumentParser()
     expected.add_argument("""--foo"""   ,				default=lowerCamelCase__   ,				type=lowerCamelCase__			)
     expected.add_argument("""--bar"""   ,				default=lowerCamelCase__   ,				type=lowerCamelCase__   ,				help="""help message"""			)
     expected.add_argument("""--baz"""   ,				default=lowerCamelCase__   ,				type=lowerCamelCase__			)
     expected.add_argument("""--ces"""   ,				nargs="""+"""   ,				default=[]   ,				type=lowerCamelCase__			)
     expected.add_argument("""--des"""   ,				nargs="""+"""   ,				default=[]   ,				type=lowerCamelCase__			)

     __UpperCamelCase	:			Optional[int]					=					[OptionalExample]
     if is_python_no_less_than_3_10:
        dataclass_types.append(lowerCamelCase__			)

     for dataclass_type in dataclass_types:
        __UpperCamelCase	:			Any					=					HfArgumentParser(lowerCamelCase__			)

        self.argparsersEqual(lowerCamelCase__   ,				lowerCamelCase__			)

        __UpperCamelCase	:			List[Any]					=					parser.parse_args([]			)
        self.assertEqual(lowerCamelCase__   ,				Namespace(foo=lowerCamelCase__   ,				bar=lowerCamelCase__   ,				baz=lowerCamelCase__   ,				ces=[]   ,				des=[]			)			)

        __UpperCamelCase	:			int					=					parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split()			)
        self.assertEqual(lowerCamelCase__   ,				Namespace(foo=12   ,				bar=3.14   ,				baz="""42"""   ,				ces=["""a""", """b""", """c"""]   ,				des=[1, 2, 3]			)			)

  def    a					(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			List[str]					=					argparse.ArgumentParser()
     expected.add_argument("""--required_list"""   ,				nargs="""+"""   ,				type=lowerCamelCase__   ,				required=lowerCamelCase__			)
     expected.add_argument("""--required_str"""   ,				type=lowerCamelCase__   ,				required=lowerCamelCase__			)
     expected.add_argument(
         """--required_enum"""   ,				type=make_choice_type_function(["""titi""", """toto"""]			)   ,				choices=["""titi""", """toto"""]   ,				required=lowerCamelCase__   ,				)
     self.argparsersEqual(lowerCamelCase__   ,				lowerCamelCase__			)

  def    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			List[str]					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			List[str]					=					argparse.ArgumentParser()
     expected.add_argument("""--foo"""   ,				type=lowerCamelCase__   ,				required=lowerCamelCase__			)
     expected.add_argument(
         """--required_enum"""   ,				type=make_choice_type_function(["""titi""", """toto"""]			)   ,				choices=["""titi""", """toto"""]   ,				required=lowerCamelCase__   ,				)
     expected.add_argument("""--opt"""   ,				type=lowerCamelCase__   ,				default=lowerCamelCase__			)
     expected.add_argument("""--baz"""   ,				default="""toto"""   ,				type=lowerCamelCase__   ,				help="""help message"""			)
     expected.add_argument("""--foo_str"""   ,				nargs="""+"""   ,				default=["""Hallo""", """Bonjour""", """Hello"""]   ,				type=lowerCamelCase__			)
     self.argparsersEqual(lowerCamelCase__   ,				lowerCamelCase__			)

  def    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			Any					=					{
         """foo""": 12,
         """bar""": 3.14,
         """baz""": """42""",
         """flag""": True,
     }

     __UpperCamelCase	:			List[Any]					=					parser.parse_dict(lowerCamelCase__			)[0]
     __UpperCamelCase	:			Union[str, Any]					=					BasicExample(**lowerCamelCase__			)
     self.assertEqual(lowerCamelCase__   ,				lowerCamelCase__			)

  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			str					=					{
         """foo""": 12,
         """bar""": 3.14,
         """baz""": """42""",
         """flag""": True,
         """extra""": 42,
     }

     self.assertRaises(lowerCamelCase__   ,				parser.parse_dict   ,				lowerCamelCase__   ,				allow_extra_keys=lowerCamelCase__			)

  def    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			List[str]					=					{
         """foo""": 12,
         """bar""": 3.14,
         """baz""": """42""",
         """flag""": True,
     }
     with tempfile.TemporaryDirectory() as tmp_dir:
        __UpperCamelCase	:			int					=					os.path.join(lowerCamelCase__   ,				"""temp_json"""			)
        os.mkdir(lowerCamelCase__			)
        with open(temp_local_path + """.json"""   ,				"""w+"""			) as f:
           json.dump(lowerCamelCase__   ,				lowerCamelCase__			)
        __UpperCamelCase	:			Any					=					parser.parse_yaml_file(Path(temp_local_path + """.json"""			)			)[0]

     __UpperCamelCase	:			Tuple					=					BasicExample(**lowerCamelCase__			)
     self.assertEqual(lowerCamelCase__   ,				lowerCamelCase__			)

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					HfArgumentParser(lowerCamelCase__			)

     __UpperCamelCase	:			str					=					{
         """foo""": 12,
         """bar""": 3.14,
         """baz""": """42""",
         """flag""": True,
     }
     with tempfile.TemporaryDirectory() as tmp_dir:
        __UpperCamelCase	:			int					=					os.path.join(lowerCamelCase__   ,				"""temp_yaml"""			)
        os.mkdir(lowerCamelCase__			)
        with open(temp_local_path + """.yaml"""   ,				"""w+"""			) as f:
           yaml.dump(lowerCamelCase__   ,				lowerCamelCase__			)
        __UpperCamelCase	:			Tuple					=					parser.parse_yaml_file(Path(temp_local_path + """.yaml"""			)			)[0]
     __UpperCamelCase	:			str					=					BasicExample(**lowerCamelCase__			)
     self.assertEqual(lowerCamelCase__   ,				lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					HfArgumentParser(lowerCamelCase__			)
     self.assertIsNotNone(lowerCamelCase__			)











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    							=      logging.get_logger(__name__)

set_seed(770)


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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[str]=False							)     ->  int:
   __UpperCamelCase	:			Optional[Any]					=					model_type
   if use_small:
      key += "_small"
   return os.path.join(__lowerCAmelCase					,							REMOTE_MODEL_PATHS[key]["""file_name"""]							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[Any]							)     ->  List[Any]:
   os.makedirs(__lowerCAmelCase					,							exist_ok=__lowerCAmelCase							)
   hf_hub_download(repo_id=__lowerCAmelCase					,							filename=__lowerCAmelCase					,							local_dir=__lowerCAmelCase							)


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

   # convert Bark model arguments to HF Bark model arguments
   __UpperCamelCase	:			Tuple					=					model_args.pop("""n_head"""							)
   __UpperCamelCase	:			Tuple					=					model_args.pop("""n_embd"""							)
   __UpperCamelCase	:			Union[str, Any]					=					model_args.pop("""n_layer"""							)

   __UpperCamelCase	:			List[Any]					=					ConfigClass(**checkpoint["""model_args"""]							)
   __UpperCamelCase	:			Optional[int]					=					ModelClass(config=__lowerCAmelCase							)
   __UpperCamelCase	:			Dict					=					GenerationConfigClass()

   __UpperCamelCase	:			Any					=					model_generation_config
   __UpperCamelCase	:			str					=					checkpoint["""model"""]
   # fixup checkpoint
   __UpperCamelCase	:			Dict					=					"""_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
         __UpperCamelCase	:			List[str]					=					k[len(__lowerCAmelCase							) :]
         for old_layer_name in new_layer_name_dict:
            __UpperCamelCase	:			List[Any]					=					new_k.replace(__lowerCAmelCase					,							new_layer_name_dict[old_layer_name]							)

         __UpperCamelCase	:			Optional[Any]					=					state_dict.pop(__lowerCAmelCase							)

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

   __UpperCamelCase	:			str					=					"""cpu"""  # do conversion on cpu

   __UpperCamelCase	:			str					=					_get_ckpt_path(__lowerCAmelCase					,							use_small=__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					_load_model(__lowerCAmelCase					,							__lowerCAmelCase					,							model_type=__lowerCAmelCase					,							use_small=__lowerCAmelCase							)

   # load bark initial model
   __UpperCamelCase	:			Dict					=					_bark_load_model(__lowerCAmelCase					,							"""cpu"""					,							model_type=__lowerCAmelCase					,							use_small=__lowerCAmelCase							)

   if model_type == "text":
      __UpperCamelCase	:			Dict					=					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
   __UpperCamelCase	:			List[str]					=					5
   __UpperCamelCase	:			List[Any]					=					10

   if model_type in ["text", "coarse"]:
      __UpperCamelCase	:			List[Any]					=					torch.randint(256					,							(batch_size, sequence_length)					,							dtype=torch.int							)
      __UpperCamelCase	:			List[str]					=					bark_model(__lowerCAmelCase							)[0]

      __UpperCamelCase	:			Dict					=					model(__lowerCAmelCase							)

      # take last logits
      __UpperCamelCase	:			Union[str, Any]					=					output_new_model_total.logits[:, [-1], :]

   else:
      __UpperCamelCase	:			Optional[int]					=					3
      __UpperCamelCase	:			Tuple					=					8
      __UpperCamelCase	:			int					=					torch.randint(256					,							(batch_size, sequence_length, n_codes_total)					,							dtype=torch.int							)

      __UpperCamelCase	:			Union[str, Any]					=					model(__lowerCAmelCase					,							__lowerCAmelCase							)
      __UpperCamelCase	:			Tuple					=					bark_model(__lowerCAmelCase					,							__lowerCAmelCase							)

      __UpperCamelCase	:			Dict					=					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 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[Any]					,							)     ->  int:
   __UpperCamelCase	:			Optional[int]					=					os.path.join(__lowerCAmelCase					,							__lowerCAmelCase							)

   __UpperCamelCase	:			str					=					BarkSemanticConfig.from_pretrained(os.path.join(__lowerCAmelCase					,							"""config.json"""							)							)
   __UpperCamelCase	:			List[str]					=					BarkCoarseConfig.from_pretrained(os.path.join(__lowerCAmelCase					,							"""config.json"""							)							)
   __UpperCamelCase	:			List[Any]					=					BarkFineConfig.from_pretrained(os.path.join(__lowerCAmelCase					,							"""config.json"""							)							)
   __UpperCamelCase	:			List[str]					=					EncodecConfig.from_pretrained("""facebook/encodec_24khz"""							)

   __UpperCamelCase	:			Optional[int]					=					BarkSemanticModel.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					BarkCoarseModel.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			Union[str, Any]					=					BarkFineModel.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			Union[str, Any]					=					EncodecModel.from_pretrained("""facebook/encodec_24khz"""							)

   __UpperCamelCase	:			Dict					=					BarkConfig.from_sub_model_configs(
       __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

   __UpperCamelCase	:			List[Any]					=					BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config					,							coarseAcoustic.generation_config					,							fineAcoustic.generation_config							)

   __UpperCamelCase	:			Optional[int]					=					BarkModel(__lowerCAmelCase							)

   __UpperCamelCase	:			List[Any]					=					semantic
   __UpperCamelCase	:			Any					=					coarseAcoustic
   __UpperCamelCase	:			Dict					=					fineAcoustic
   __UpperCamelCase	:			List[str]					=					codec

   __UpperCamelCase	:			Optional[Any]					=					bark_generation_config

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


if __name__ == "__main__":
       UpperCamelCase    							=      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    							=      parser.parse_args()

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



