


from __future__ import annotations

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






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__ = None		):
        if components is None:
            A			:	Tuple										=       []
        A			:	Optional[int]										=       list(_UpperCAmelCase		)






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






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






    def __add__(	self,		lowerCamelCase__		):
        A			:	str										=       len(self		)
        if size == len(_UpperCAmelCase		):
            A			:	Optional[Any]										=       [self.__components[i] + other.component(_UpperCAmelCase		) for i in range(_UpperCAmelCase		)]
            return Vector(_UpperCAmelCase		)
        else:
            raise Exception("""must have the same size"""		)






    def __sub__(	self,		lowerCamelCase__		):
        A			:	str										=       len(self		)
        if size == len(_UpperCAmelCase		):
            A			:	str										=       [self.__components[i] - other.component(_UpperCAmelCase		) for i in range(_UpperCAmelCase		)]
            return Vector(_UpperCAmelCase		)
        else:  # error case
            raise Exception("""must have the same size"""		)






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






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






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






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






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if isinstance(_UpperCAmelCase,		_UpperCAmelCase		) and -len(self.__components		) <= i < len(self.__components		):
            return self.__components[i]
        else:
            raise Exception("""index out of range"""		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        assert -len(self.__components		) <= pos < len(self.__components		)
        A			:	Dict										=       value






    def       _lowerCAmelCase (	self		):
        if len(self.__components		) == 0:
            raise Exception("""Vector is empty"""		)
        A			:	Tuple										=       [c**2 for c in self.__components]
        return math.sqrt(sum(_UpperCAmelCase		)		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = False		):
        A			:	Dict										=       self * other
        A			:	Optional[int]										=       self.euclidean_length() * other.euclidean_length()
        if deg:
            return math.degrees(math.acos(num / den		)		)
        else:
            return math.acos(num / den		)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Vector:



    """simple docstring"""






    assert isinstance(lowerCAmelCase_				,					lowerCAmelCase_		)
    return Vector([0] * dimension		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Vector:



    """simple docstring"""






    assert isinstance(lowerCAmelCase_				,					lowerCAmelCase_		) and (isinstance(lowerCAmelCase_				,					lowerCAmelCase_		))
    A			:	List[str]										=       [0] * dimension
    A			:	Optional[Any]										=       1
    return Vector(lowerCAmelCase_		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Vector:



    """simple docstring"""






    assert (
        isinstance(lowerCAmelCase_				,					lowerCAmelCase_		)
        and isinstance(lowerCAmelCase_				,					lowerCAmelCase_		)
        and (isinstance(lowerCAmelCase_				,					(int, float)		))
    )
    return x * scalar + y




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Vector:



    """simple docstring"""






    random.seed(lowerCAmelCase_		)
    A			:	int										=       [random.randint(lowerCAmelCase_				,					lowerCAmelCase_		) for _ in range(lowerCAmelCase_		)]
    return Vector(lowerCAmelCase_		)






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	str										=       matrix
        A			:	int										=       w
        A			:	Any										=       h






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






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






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






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






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






    def       _lowerCAmelCase (	self		):
        return self.__height






    def       _lowerCAmelCase (	self		):
        return self.__width






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        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       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if 0 <= x < self.__height and 0 <= y < self.__width:
            A			:	Union[str, Any]										=       value
        else:
            raise Exception("""change_component: indices out of bounds"""		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        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(_UpperCAmelCase		)		):
            A			:	Tuple										=       minor[i][:y] + minor[i][y + 1 :]
        return Matrix(_UpperCAmelCase,		self.__width - 1,		self.__height - 1		).determinant()






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        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(_UpperCAmelCase,		_UpperCAmelCase		)
        else:
            raise Exception("""Indices out of bounds"""		)






    def       _lowerCAmelCase (	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,		_UpperCAmelCase		) for y in range(self.__width		)
            ]
            return sum(_UpperCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Matrix:



    """simple docstring"""






    A			:	list[list[float]]										=       [[0] * n for _ in range(lowerCAmelCase_		)]
    return Matrix(lowerCAmelCase_				,					lowerCAmelCase_				,					lowerCAmelCase_		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Matrix:



    """simple docstring"""






    random.seed(lowerCAmelCase_		)
    A			:	list[list[float]]										=       [
        [random.randint(lowerCAmelCase_				,					lowerCAmelCase_		) for _ in range(lowerCAmelCase_		)] for _ in range(lowerCAmelCase_		)
    ]
    return Matrix(lowerCAmelCase_				,					lowerCAmelCase_				,					lowerCAmelCase_		)







import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:Union[str, Any]					=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:Union[str, Any]					=						"""https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    if key.endswith(""".model.1.bias"""		) and len(key.split("""."""		)		) > 10:
        A			:	Optional[int]										=       key.replace(""".model.1.bias"""				,					""".conv1d_1.bias"""		)
    elif key.endswith(""".model.1.weight"""		) and len(key.split("""."""		)		) > 10:
        A			:	Any										=       key.replace(""".model.1.weight"""				,					""".conv1d_1.weight"""		)
    elif key.endswith(""".model.3.bias"""		) and len(key.split("""."""		)		) > 10:
        A			:	str										=       key.replace(""".model.3.bias"""				,					""".conv1d_2.bias"""		)
    elif key.endswith(""".model.3.weight"""		) and len(key.split("""."""		)		) > 10:
        A			:	Optional[Any]										=       key.replace(""".model.3.weight"""				,					""".conv1d_2.weight"""		)

    if "conditioner_blocks.0." in key:
        A			:	List[str]										=       key.replace("""conditioner_blocks.0"""				,					"""conditioner_blocks"""		)

    if "prime_prior" in key:
        A			:	Tuple										=       key.replace("""prime_prior"""				,					"""encoder"""		)

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        A			:	List[str]										=       key.replace(""".emb."""				,					"""."""		)

    if key.endswith("""k"""		):  # replace vqvae.X.k with vqvae.X.codebook
        return key.replace(""".k"""				,					""".codebook"""		)
    if "y_emb." in key:
        return key.replace("""y_emb."""				,					"""metadata_embedding."""		)

    if "x_emb.emb." in key:
        A			:	Optional[int]										=       key.replace("""0.x_emb.emb"""				,					"""embed_tokens"""		)

    if "prime_state_ln" in key:
        return key.replace("""prime_state_ln"""				,					"""encoder.final_layer_norm"""		)
    if ".ln" in key:
        return key.replace(""".ln"""				,					""".layer_norm"""		)
    if "_ln" in key:
        return key.replace("""_ln"""				,					"""_layer_norm"""		)

    if "prime_state_proj" in key:
        return key.replace("""prime_state_proj"""				,					"""encoder.proj_in"""		)
    if "prime_x_out" in key:
        return key.replace("""prime_x_out"""				,					"""encoder.lm_head"""		)
    if "prior.x_out" in key:
        return key.replace("""x_out"""				,					"""fc_proj_out"""		)
    if "x_emb" in key:
        return key.replace("""x_emb"""				,					"""embed_tokens"""		)

    return key




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	List[str]										=       {}
    import re

    A			:	Any										=       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""		)
    A			:	str										=       re.compile(
        R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""		)
    A			:	Union[str, Any]										=       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""		)

    A			:	List[Any]										=       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""		)
    A			:	Optional[Any]										=       re.compile(
        R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""		)
    A			:	List[str]										=       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""		)

    A			:	Optional[Any]										=       re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""		)
    A			:	Tuple										=       re.compile(
        R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""		)
    A			:	List[Any]										=       re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"""		)

    for original_key, value in state_dict.items():
        # rename vqvae.encoder keys
        if re_encoder_block_conv_in.fullmatch(_lowerCAmelCase		):
            A			:	Optional[Any]										=       re_encoder_block_conv_in.match(_lowerCAmelCase		)
            A			:	Tuple										=       regex_match.groups()
            A			:	str										=       int(groups[2]		) * 2 + int(groups[3]		)
            A			:	int										=       f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
            A			:	Any										=       re_encoder_block_conv_in.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_encoder_block_resnet.fullmatch(_lowerCAmelCase		):
            A			:	Optional[int]										=       re_encoder_block_resnet.match(_lowerCAmelCase		)
            A			:	str										=       regex_match.groups()
            A			:	Optional[int]										=       int(groups[2]		) * 2 + int(groups[3]		)
            A			:	Any										=       {"""1""": 1, """3""": 2}[groups[-2]]
            A			:	int										=       f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
            A			:	List[str]										=       f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
            A			:	str										=       prefix + resnet_block
            A			:	str										=       re_encoder_block_resnet.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_encoder_block_proj_out.fullmatch(_lowerCAmelCase		):
            A			:	List[str]										=       re_encoder_block_proj_out.match(_lowerCAmelCase		)
            A			:	List[Any]										=       regex_match.groups()
            A			:	List[Any]										=       f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
            A			:	Optional[int]										=       re_encoder_block_proj_out.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_lowerCAmelCase		):
            A			:	Union[str, Any]										=       re_decoder_block_conv_out.match(_lowerCAmelCase		)
            A			:	Dict										=       regex_match.groups()
            A			:	Union[str, Any]										=       int(groups[2]		) * 2 + int(groups[3]		) - 2
            A			:	Optional[int]										=       f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
            A			:	int										=       re_decoder_block_conv_out.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_decoder_block_resnet.fullmatch(_lowerCAmelCase		):
            A			:	Optional[int]										=       re_decoder_block_resnet.match(_lowerCAmelCase		)
            A			:	List[Any]										=       regex_match.groups()
            A			:	List[str]										=       int(groups[2]		) * 2 + int(groups[3]		) - 2
            A			:	str										=       {"""1""": 1, """3""": 2}[groups[-2]]
            A			:	Optional[int]										=       f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
            A			:	Union[str, Any]										=       f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
            A			:	Tuple										=       prefix + resnet_block
            A			:	Tuple										=       re_decoder_block_resnet.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_decoder_block_proj_in.fullmatch(_lowerCAmelCase		):
            A			:	Optional[Any]										=       re_decoder_block_proj_in.match(_lowerCAmelCase		)
            A			:	Any										=       regex_match.groups()
            A			:	Optional[Any]										=       f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
            A			:	Dict										=       re_decoder_block_proj_in.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(_lowerCAmelCase		):
            A			:	Optional[int]										=       re_prior_cond_conv_out.match(_lowerCAmelCase		)
            A			:	List[Any]										=       regex_match.groups()
            A			:	List[Any]										=       int(groups[1]		) * 2 + int(groups[2]		) - 2
            A			:	Tuple										=       f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
            A			:	List[str]										=       re_prior_cond_conv_out.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_prior_cond_resnet.fullmatch(_lowerCAmelCase		):
            A			:	Any										=       re_prior_cond_resnet.match(_lowerCAmelCase		)
            A			:	Any										=       regex_match.groups()
            A			:	Tuple										=       int(groups[1]		) * 2 + int(groups[2]		) - 2
            A			:	Optional[Any]										=       {"""1""": 1, """3""": 2}[groups[-2]]
            A			:	Tuple										=       f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
            A			:	List[str]										=       f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
            A			:	Dict										=       prefix + resnet_block
            A			:	Union[str, Any]										=       re_prior_cond_resnet.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_prior_cond_proj_in.fullmatch(_lowerCAmelCase		):
            A			:	List[Any]										=       re_prior_cond_proj_in.match(_lowerCAmelCase		)
            A			:	Optional[int]										=       regex_match.groups()
            A			:	Tuple										=       f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
            A			:	Optional[int]										=       re_prior_cond_proj_in.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        # keep original key
        else:
            A			:	str										=       original_key

        A			:	List[str]										=       replace_key(_lowerCAmelCase		)

        if f'''{key_prefix}.{key}''' not in model_state_dict or key is None:
            print(f'''failed converting {original_key} to {key}, does not match'''		)

        # handle missmatched shape
        elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
            A			:	str										=       model_state_dict[f'''{key_prefix}.{key}''']
            print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''		)
            A			:	Union[str, Any]										=       original_key

        A			:	Union[str, Any]										=       original_key
        A			:	List[str]										=       value

    return new_dict




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase=None				,					_lowerCAmelCase=None		)	->  Tuple:



    """simple docstring"""






    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"		)[-1]}'''		):
            A			:	Optional[Any]										=       requests.get(f'''{PREFIX}{file}'''				,					allow_redirects=_lowerCAmelCase		)
            os.makedirs(f'''{pytorch_dump_folder_path}/'''				,					exist_ok=_lowerCAmelCase		)
            open(f'''{pytorch_dump_folder_path}/{file.split("/"		)[-1]}'''				,					"""wb"""		).write(r.content		)

    A			:	Optional[int]										=       MODEL_MAPPING[model_name.split("""/"""		)[-1]]

    A			:	List[Any]										=       JukeboxConfig.from_pretrained(_lowerCAmelCase		)
    A			:	Dict										=       JukeboxModel(_lowerCAmelCase		)

    A			:	str										=       []
    A			:	Optional[int]										=       {}
    for i, dict_name in enumerate(_lowerCAmelCase		):
        A			:	str										=       torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/"		)[-1]}'''		)["""model"""]

        A			:	Optional[int]										=       {}
        for k in old_dic.keys():
            if k.endswith(""".b"""		):
                A			:	Dict										=       old_dic[k]
            elif k.endswith(""".w"""		):
                A			:	Optional[Any]										=       old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                A			:	Union[str, Any]										=       old_dic[k]
            else:
                A			:	Optional[int]										=       old_dic[k]

        A			:	List[str]										=       """vqvae""" if i == 0 else f'''priors.{3 - i}'''
        A			:	List[Any]										=       fix_jukebox_keys(_lowerCAmelCase				,					model.state_dict()				,					_lowerCAmelCase				,					_lowerCAmelCase		)
        weight_dict.append(_lowerCAmelCase		)

    A			:	List[Any]										=       weight_dict.pop(0		)
    model.vqvae.load_state_dict(_lowerCAmelCase		)
    for i in range(len(_lowerCAmelCase		)		):
        model.priors[i].load_state_dict(weight_dict[2 - i]		)

    Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
    with open(f'''{pytorch_dump_folder_path}/mapping.json'''				,					"""w"""		) as txtfile:
        json.dump(_lowerCAmelCase				,					_lowerCAmelCase		)

    print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
    model.save_pretrained(_lowerCAmelCase		)

    return weight_dict


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:List[Any]					=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default="""jukebox-5b-lyrics""",
         type=str,
         help="""Name of the model you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default="""jukebox-5b-lyrics-converted""",
         type=str,
         help="""Path to the output PyTorch model directory.""",
     )
     SCREAMING_SNAKE_CASE_:int					=						parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


