


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()
lowercase__				     =		logging.get_logger(__name__)


lowercase__				     =		'''https://openaipublic.azureedge.net/jukebox/models/'''
lowercase__				     =		{
    '''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		: List[str]				)      ->     Tuple:







     '''simple docstring'''






     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		: str      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: Optional[Any]				)      ->     Tuple:







     '''simple docstring'''






     _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		: int=None      ,  __lowerCamelCase		: int=None				)      ->     List[str]:







     '''simple docstring'''






     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__":
   lowercase__				     =		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.",
   )
   lowercase__				     =		parser.parse_args()
   convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





'''simple docstring'''

from __future__ import annotations

import requests






def __UpperCamelCase   (			__lowerCamelCase		: str				)      ->     dict:







     '''simple docstring'''






     _a						=		F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
     return requests.get(__lowerCamelCase				).json()






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     list[dict]:







     '''simple docstring'''






     _a						=		"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
     _a						=		requests.get(__lowerCamelCase				).json()[:max_stories]
     return [get_hackernews_story(__lowerCamelCase				) for story_id in story_ids]






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     str:







     '''simple docstring'''






     _a						=		hackernews_top_stories(__lowerCamelCase				)
     return "\n".join("* [{title}]({url})".format(**__lowerCamelCase				) for story in stories				)


if __name__ == "__main__":
   print(hackernews_top_stories_as_markdown())

