

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()
__A			:					str						=			logging.get_logger(__name__)


__A			:					str						=			'https://openaipublic.azureedge.net/jukebox/models/'
__A			:					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 	__a     (      A__			:  Union[str, Any]       ):
    if key.endswith(".model.1.bias"       ) and len(key.split("."       )       ) > 10:
        SCREAMING_SNAKE_CASE										=   key.replace(".model.1.bias"			,  ".conv1d_1.bias"       )
    elif key.endswith(".model.1.weight"       ) and len(key.split("."       )       ) > 10:
        SCREAMING_SNAKE_CASE										=   key.replace(".model.1.weight"			,  ".conv1d_1.weight"       )
    elif key.endswith(".model.3.bias"       ) and len(key.split("."       )       ) > 10:
        SCREAMING_SNAKE_CASE										=   key.replace(".model.3.bias"			,  ".conv1d_2.bias"       )
    elif key.endswith(".model.3.weight"       ) and len(key.split("."       )       ) > 10:
        SCREAMING_SNAKE_CASE										=   key.replace(".model.3.weight"			,  ".conv1d_2.weight"       )

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

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

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        SCREAMING_SNAKE_CASE										=   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:
        SCREAMING_SNAKE_CASE										=   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 	__a     (      A__			:  List[str]			,  A__			:  Optional[Any]			,  A__			:  List[Any]			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   {}
    import re

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

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

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

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

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

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

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

        elif re_decoder_block_proj_in.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_decoder_block_proj_in.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   re_decoder_block_proj_in.sub(A__			,  A__       )

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(A__       ):
            SCREAMING_SNAKE_CASE										=   re_prior_cond_conv_out.match(A__       )
            SCREAMING_SNAKE_CASE										=   regex_match.groups()
            SCREAMING_SNAKE_CASE										=   int(groups[1]       ) * 2 + int(groups[2]       ) - 2
            SCREAMING_SNAKE_CASE										=   F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
            SCREAMING_SNAKE_CASE										=   re_prior_cond_conv_out.sub(A__			,  A__       )

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

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

        # keep original key
        else:
            SCREAMING_SNAKE_CASE										=   original_key

        SCREAMING_SNAKE_CASE										=   replace_key(A__       )

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

        SCREAMING_SNAKE_CASE										=   original_key
        SCREAMING_SNAKE_CASE										=   value

    return new_dict





@torch.no_grad()
def 	__a     (      A__			:  Tuple=None			,  A__			:  List[Any]=None       ):
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'       )[-1]}"       ):
            SCREAMING_SNAKE_CASE										=   requests.get(F"{PREFIX}{file}"			,  allow_redirects=A__       )
            os.makedirs(F"{pytorch_dump_folder_path}/"			,  exist_ok=A__       )
            open(F"{pytorch_dump_folder_path}/{file.split('/'       )[-1]}"			,  "wb"       ).write(r.content       )

    SCREAMING_SNAKE_CASE										=   MODEL_MAPPING[model_name.split("/"       )[-1]]

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

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

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

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

    SCREAMING_SNAKE_CASE										=   weight_dict.pop(0       )
    model.vqvae.load_state_dict(A__       )
    for i in range(len(A__       )       ):
        model.priors[i].load_state_dict(weight_dict[2 - i]       )

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

    print(F"Saving model {model_name} to {pytorch_dump_folder_path}"       )
    model.save_pretrained(A__       )

    return weight_dict


if __name__ == "__main__":
    __A			:					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.',
    )
    __A			:					List[Any]						=			parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)

"""simple docstring"""

from __future__ import annotations

from collections.abc import Iterator
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       value
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None

class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       tree




      def      a_   (				self ,							lowercase_				)    ->       int:
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
          )




      def __iter__(				self				)    ->       Iterator[int]:
          yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



