





'''simple docstring'''

import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map





def 		UpperCamelCase							(				_lowerCamelCase   :   Union[dict, list, tuple, torch.Tensor]			):
    A__							=     []
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        for v in tree.values():
            shapes.extend(_fetch_dims(_lowerCamelCase			)			)
    elif isinstance(_lowerCamelCase		,			(list, tuple)			):
        for t in tree:
            shapes.extend(_fetch_dims(_lowerCamelCase			)			)
    elif isinstance(_lowerCamelCase		,			torch.Tensor			):
        shapes.append(tree.shape			)
    else:
        raise ValueError("Not supported"			)

    return shapes





@torch.jit.ignore
def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   Tuple[int, ...]			):
    A__							=     []
    for d in reversed(_lowerCamelCase			):
        idx.append(flat_idx % d			)
        A__							=     flat_idx // d

    return tuple(reversed(_lowerCamelCase			)			)





@torch.jit.ignore
def 		UpperCamelCase							(				_lowerCamelCase   :   Sequence[int]		,			_lowerCamelCase   :   Sequence[int]		,			_lowerCamelCase   :   Sequence[int]		,			_lowerCamelCase   :   Optional[Sequence[bool]] = None		,			_lowerCamelCase   :   Optional[Sequence[bool]] = None		,			):

    # start_edges and end_edges both indicate whether, starting from any given
    # dimension, the start/end index is at the top/bottom edge of the
    # corresponding tensor, modeled as a tree
    def reduce_edge_list(_lowerCamelCase   :   List[bool]			) -> None:
        A__							=     True
        for i in range(len(_lowerCamelCase			)			):
            A__							=     -1 * (i + 1)
            l[reversed_idx] &= tally
            A__							=     l[reversed_idx]

    if start_edges is None:
        A__							=     [s == 0 for s in start]
        reduce_edge_list(_lowerCamelCase			)
    if end_edges is None:
        A__							=     [e == (d - 1) for e, d in zip(_lowerCamelCase		,			_lowerCamelCase			)]
        reduce_edge_list(_lowerCamelCase			)

    # Base cases. Either start/end are empty and we're done, or the final,
    # one-dimensional tensor can be simply sliced
    if len(_lowerCamelCase			) == 0:
        return [()]
    elif len(_lowerCamelCase			) == 1:
        return [(slice(start[0]		,			end[0] + 1			),)]

    A__							=     []
    A__							=     []

    # Dimensions common to start and end can be selected directly
    for s, e in zip(_lowerCamelCase		,			_lowerCamelCase			):
        if s == e:
            path_list.append(slice(_lowerCamelCase		,			s + 1			)			)
        else:
            break

    A__							=     tuple(_lowerCamelCase			)
    A__							=     len(_lowerCamelCase			)

    # start == end, and we're done
    if divergence_idx == len(_lowerCamelCase			):
        return [path]

    def upper() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        A__							=     start[divergence_idx]
        return tuple(
            path + (slice(_lowerCamelCase		,			sdi + 1			),) + s
            for s in _get_minimal_slice_set(
                start[divergence_idx + 1 :]		,			[d - 1 for d in dims[divergence_idx + 1 :]]		,			dims[divergence_idx + 1 :]		,			start_edges=start_edges[divergence_idx + 1 :]		,			end_edges=[True for _ in end_edges[divergence_idx + 1 :]]		,			)			)

    def lower() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        A__							=     end[divergence_idx]
        return tuple(
            path + (slice(_lowerCamelCase		,			edi + 1			),) + s
            for s in _get_minimal_slice_set(
                [0 for _ in start[divergence_idx + 1 :]]		,			end[divergence_idx + 1 :]		,			dims[divergence_idx + 1 :]		,			start_edges=[True for _ in start_edges[divergence_idx + 1 :]]		,			end_edges=end_edges[divergence_idx + 1 :]		,			)			)

    # If both start and end are at the edges of the subtree rooted at
    # divergence_idx, we can just select the whole subtree at once
    if start_edges[divergence_idx] and end_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx]		,			end[divergence_idx] + 1			),)			)
    # If just start is at the edge, we can grab almost all of the subtree,
    # treating only the ragged bottom edge as an edge case
    elif start_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx]		,			end[divergence_idx]			),)			)
        slices.extend(lower()			)
    # Analogous to the previous case, but the top is ragged this time
    elif end_edges[divergence_idx]:
        slices.extend(upper()			)
        slices.append(path + (slice(start[divergence_idx] + 1		,			end[divergence_idx] + 1			),)			)
    # If both sides of the range are ragged, we need to handle both sides
    # separately. If there's contiguous meat in between them, we can index it
    # in one big chunk
    else:
        slices.extend(upper()			)
        A__							=     end[divergence_idx] - start[divergence_idx]
        if middle_ground > 1:
            slices.append(path + (slice(start[divergence_idx] + 1		,			end[divergence_idx]			),)			)
        slices.extend(lower()			)

    return slices





@torch.jit.ignore
def 		UpperCamelCase							(				_lowerCamelCase   :   torch.Tensor		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    A__							=     t.shape[:no_batch_dims]
    A__							=     list(_flat_idx_to_idx(_lowerCamelCase		,			_lowerCamelCase			)			)
    # _get_minimal_slice_set is inclusive
    A__							=     list(_flat_idx_to_idx(flat_end - 1		,			_lowerCamelCase			)			)

    # Get an ordered list of slices to perform
    A__							=     _get_minimal_slice_set(
        _lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			)

    A__							=     [t[s] for s in slices]

    return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]			) for s in sliced_tensors]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   Callable		,			_lowerCamelCase   :   Dict[str, Any]		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   bool = False		,			_lowerCamelCase   :   Any = None		,			_lowerCamelCase   :   bool = False		,			):
    if not (len(_lowerCamelCase			) > 0):
        raise ValueError("Must provide at least one input"			)

    A__							=     [shape[:no_batch_dims] for shape in _fetch_dims(_lowerCamelCase			)]
    A__							=     tuple([max(_lowerCamelCase			) for s in zip(*_lowerCamelCase			)]			)

    def _prep_inputs(_lowerCamelCase   :   torch.Tensor			) -> torch.Tensor:
        if not low_mem:
            if not sum(t.shape[:no_batch_dims]			) == no_batch_dims:
                A__							=     t.expand(orig_batch_dims + t.shape[no_batch_dims:]			)
            A__							=     t.reshape(-1		,			*t.shape[no_batch_dims:]			)
        else:
            A__							=     t.expand(orig_batch_dims + t.shape[no_batch_dims:]			)
        return t

    A__							=     tensor_tree_map(_prep_inputs		,			_lowerCamelCase			)
    A__							=     None
    if _out is not None:
        A__							=     tensor_tree_map(lambda _lowerCamelCase			: t.view([-1] + list(t.shape[no_batch_dims:]			)			)		,			_out			)

    A__							=     1
    for d in orig_batch_dims:
        flat_batch_dim *= d

    A__							=     flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

    def _select_chunk(_lowerCamelCase   :   torch.Tensor			) -> torch.Tensor:
        return t[i : i + chunk_size] if t.shape[0] != 1 else t

    A__							=     0
    A__							=     prepped_outputs
    for _ in range(_lowerCamelCase			):
        # Chunk the input
        if not low_mem:
            A__							=     _select_chunk
        else:
            A__							=     partial(
                _chunk_slice		,			flat_start=_lowerCamelCase		,			flat_end=min(_lowerCamelCase		,			i + chunk_size			)		,			no_batch_dims=len(_lowerCamelCase			)		,			)

        A__							=     tensor_tree_map(_lowerCamelCase		,			_lowerCamelCase			)

        # Run the layer on the chunk
        A__							=     layer(**_lowerCamelCase			)

        # Allocate space for the output
        if out is None:
            A__							=     tensor_tree_map(lambda _lowerCamelCase			: t.new_zeros((flat_batch_dim,) + t.shape[1:]			)		,			_lowerCamelCase			)

        # Put the chunk in its pre-allocated space
        if isinstance(_lowerCamelCase		,			_lowerCamelCase			):

            def assign(_lowerCamelCase   :   dict		,			_lowerCamelCase   :   dict			) -> None:
                for k, v in da.items():
                    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
                        assign(_lowerCamelCase		,			da[k]			)
                    else:
                        if _add_into_out:
                            v[i : i + chunk_size] += da[k]
                        else:
                            A__							=     da[k]

            assign(_lowerCamelCase		,			_lowerCamelCase			)
        elif isinstance(_lowerCamelCase		,			_lowerCamelCase			):
            for xa, xa in zip(_lowerCamelCase		,			_lowerCamelCase			):
                if _add_into_out:
                    xa[i : i + chunk_size] += xa
                else:
                    A__							=     xa
        elif isinstance(_lowerCamelCase		,			torch.Tensor			):
            if _add_into_out:
                out[i : i + chunk_size] += output_chunk
            else:
                A__							=     output_chunk
        else:
            raise ValueError("Not supported"			)

        i += chunk_size

    A__							=     tensor_tree_map(lambda _lowerCamelCase			: t.view(orig_batch_dims + t.shape[1:]			)		,			_lowerCamelCase			)

    return out






class   UpperCAmelCase      :
 def __init__(							self :str		,			lowercase_ :int = 5_12		,			)->				str:
     A__							=     max_chunk_size
     A__							=     None
     A__							=     None
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :Callable		,			lowercase_ :tuple		,			lowercase_ :int				)->				int:
     logging.info("Tuning chunk size..."				)

     if min_chunk_size >= self.max_chunk_size:
         return min_chunk_size

     A__							=     [2**l for l in range(int(math.log(self.max_chunk_size		,			2				)				) + 1				)]
     A__							=     [c for c in candidates if c > min_chunk_size]
     A__							=     [min_chunk_size] + candidates
     candidates[-1] += 4

     def test_chunk_size(lowercase_ :int				) -> bool:
         try:
             with torch.no_grad():
                 fn(*lowercase_		,			chunk_size=lowercase_				)
             return True
         except RuntimeError:
             return False

     A__							=     0
     A__							=     len(lowercase_				) - 1
     while i > min_viable_chunk_size_index:
         A__							=     test_chunk_size(candidates[i]				)
         if not viable:
             A__							=     (min_viable_chunk_size_index + i) // 2
         else:
             A__							=     i
             A__							=     (i + len(lowercase_				) - 1) // 2

     return candidates[min_viable_chunk_size_index]
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Iterable		,			lowercase_ :Iterable				)->				bool:
     A__							=     True
     for aa, aa in zip(lowercase_		,			lowercase_				):
         assert type(lowercase_				) == type(lowercase_				)
         if isinstance(lowercase_		,			(list, tuple)				):
             consistent &= self._compare_arg_caches(lowercase_		,			lowercase_				)
         elif isinstance(lowercase_		,			lowercase_				):
             A__							=     [v for _, v in sorted(aa.items()		,			key=lambda lowercase_				: x[0]				)]
             A__							=     [v for _, v in sorted(aa.items()		,			key=lambda lowercase_				: x[0]				)]
             consistent &= self._compare_arg_caches(lowercase_		,			lowercase_				)
         else:
             consistent &= aa == aa

     return consistent



 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :Callable		,			lowercase_ :tuple		,			lowercase_ :int		,			)->				int:
     A__							=     True
     A__							=     tree_map(lambda lowercase_				: a.shape if isinstance(lowercase_		,			torch.Tensor				) else a		,			lowercase_		,			lowercase_				)
     if self.cached_arg_data is not None:
         # If args have changed shape/value, we need to re-tune
         assert len(self.cached_arg_data				) == len(lowercase_				)
         A__							=     self._compare_arg_caches(self.cached_arg_data		,			lowercase_				)
     else:
         # Otherwise, we can reuse the precomputed value
         A__							=     False

     if not consistent:
         A__							=     self._determine_favorable_chunk_size(
             lowercase_		,			lowercase_		,			lowercase_		,			)
         A__							=     arg_data

     assert self.cached_chunk_size is not None

     return self.cached_chunk_size










'''simple docstring'''

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


__lowerCAmelCase				:      int						      ="https://openaipublic.azureedge.net/jukebox/models/"
__lowerCAmelCase				:      Any						      ={
    "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   :   str			):
    if key.endswith(".model.1.bias"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.1.bias"		,			".conv1d_1.bias"			)
    elif key.endswith(".model.1.weight"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.1.weight"		,			".conv1d_1.weight"			)
    elif key.endswith(".model.3.bias"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.3.bias"		,			".conv1d_2.bias"			)
    elif key.endswith(".model.3.weight"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.3.weight"		,			".conv1d_2.weight"			)

    if "conditioner_blocks.0." in key:
        A__							=     key.replace("conditioner_blocks.0"		,			"conditioner_blocks"			)

    if "prime_prior" in key:
        A__							=     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__							=     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__							=     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   :   Optional[Any]		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   str			):
    A__							=     {}
    import re

    A__							=     re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"			)
    A__							=     re.compile(
        r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
    A__							=     re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"			)

    A__							=     re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"			)
    A__							=     re.compile(
        r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
    A__							=     re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"			)

    A__							=     re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"			)
    A__							=     re.compile(
        r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
    A__							=     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__							=     re_encoder_block_conv_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[2]			) * 2 + int(groups[3]			)
            A__							=     F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
            A__							=     re_encoder_block_conv_in.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_encoder_block_resnet.fullmatch(_lowerCamelCase			):
            A__							=     re_encoder_block_resnet.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[2]			) * 2 + int(groups[3]			)
            A__							=     {"1": 1, "3": 2}[groups[-2]]
            A__							=     F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
            A__							=     F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            A__							=     prefix + resnet_block
            A__							=     re_encoder_block_resnet.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase			):
            A__							=     re_encoder_block_proj_out.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
            A__							=     re_encoder_block_proj_out.sub(_lowerCamelCase		,			_lowerCamelCase			)

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase			):
            A__							=     re_decoder_block_conv_out.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[2]			) * 2 + int(groups[3]			) - 2
            A__							=     F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
            A__							=     re_decoder_block_conv_out.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_decoder_block_resnet.fullmatch(_lowerCamelCase			):
            A__							=     re_decoder_block_resnet.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[2]			) * 2 + int(groups[3]			) - 2
            A__							=     {"1": 1, "3": 2}[groups[-2]]
            A__							=     F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
            A__							=     F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            A__							=     prefix + resnet_block
            A__							=     re_decoder_block_resnet.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase			):
            A__							=     re_decoder_block_proj_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
            A__							=     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__							=     re_prior_cond_conv_out.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[1]			) * 2 + int(groups[2]			) - 2
            A__							=     F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
            A__							=     re_prior_cond_conv_out.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_prior_cond_resnet.fullmatch(_lowerCamelCase			):
            A__							=     re_prior_cond_resnet.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[1]			) * 2 + int(groups[2]			) - 2
            A__							=     {"1": 1, "3": 2}[groups[-2]]
            A__							=     F"conditioner_blocks.upsampler.upsample_block.{block_index}."
            A__							=     F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            A__							=     prefix + resnet_block
            A__							=     re_prior_cond_resnet.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase			):
            A__							=     re_prior_cond_proj_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
            A__							=     re_prior_cond_proj_in.sub(_lowerCamelCase		,			_lowerCamelCase			)

        # keep original key
        else:
            A__							=     original_key

        A__							=     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__							=     model_state_dict[F"{key_prefix}.{key}"]
            print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"			)
            A__							=     original_key

        A__							=     original_key
        A__							=     value

    return new_dict





@torch.no_grad()
def 		UpperCamelCase							(				_lowerCamelCase   :   str=None		,			_lowerCamelCase   :   Dict=None			):
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'			)[-1]}"			):
            A__							=     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__							=     MODEL_MAPPING[model_name.split("/"			)[-1]]

    A__							=     JukeboxConfig.from_pretrained(_lowerCamelCase			)
    A__							=     JukeboxModel(_lowerCamelCase			)

    A__							=     []
    A__							=     {}
    for i, dict_name in enumerate(_lowerCamelCase			):
        A__							=     torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'			)[-1]}"			)["model"]

        A__							=     {}
        for k in old_dic.keys():
            if k.endswith(".b"			):
                A__							=     old_dic[k]
            elif k.endswith(".w"			):
                A__							=     old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                A__							=     old_dic[k]
            else:
                A__							=     old_dic[k]

        A__							=     "vqvae" if i == 0 else F"priors.{3 - i}"
        A__							=     fix_jukebox_keys(_lowerCamelCase		,			model.state_dict()		,			_lowerCamelCase		,			_lowerCamelCase			)
        weight_dict.append(_lowerCamelCase			)

    A__							=     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__":
  __lowerCAmelCase				:      Dict						      =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.",
  )
  __lowerCAmelCase				:      int						      =parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



