



"""simple docstring"""






from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike






class 			__snake_case			(      _lowercase):




  def __init__(					self	:		Tuple		,   __lowerCAmelCase	:		Optional[NestedDataStructureLike[PathLike]] = None		,   __lowerCAmelCase	:		Optional[NamedSplit] = None		,   __lowerCAmelCase	:		Optional[Features] = None		,   __lowerCAmelCase	:		str = None		,   __lowerCAmelCase	:		bool = False		,   __lowerCAmelCase	:		bool = False		,   __lowerCAmelCase	:		Optional[int] = None		,   **__lowerCAmelCase	:		Any		,   ):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					path_or_paths
         _lowerCamelCase		:							int            =					split if split or isinstance(__lowerCAmelCase		,   __lowerCAmelCase						) else '''train'''
         _lowerCamelCase		:							List[str]            =					features
         _lowerCamelCase		:							str            =					cache_dir
         _lowerCamelCase		:							List[str]            =					keep_in_memory
         _lowerCamelCase		:							List[str]            =					streaming
         _lowerCamelCase		:							int            =					num_proc
         _lowerCamelCase		:							List[str]            =					kwargs




  @abstractmethod
  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         pass






class 			__snake_case			(      _lowercase):




  def __init__(					self	:		int		,   __lowerCAmelCase	:		Optional[Features] = None		,   __lowerCAmelCase	:		str = None		,   __lowerCAmelCase	:		bool = False		,   __lowerCAmelCase	:		bool = False		,   __lowerCAmelCase	:		Optional[int] = None		,   **__lowerCAmelCase	:		Optional[int]		,   ):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					features
         _lowerCamelCase		:							List[str]            =					cache_dir
         _lowerCamelCase		:							str            =					keep_in_memory
         _lowerCamelCase		:							Optional[int]            =					streaming
         _lowerCamelCase		:							Optional[int]            =					num_proc
         _lowerCamelCase		:							List[str]            =					kwargs




  @abstractmethod
  def 		SCREAMING_SNAKE_CASE							(					self	:		Any						):






         """simple docstring"""






         pass






'''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()
UpperCAmelCase_			:  str										=   logging.get_logger(__name__)


UpperCAmelCase_			:  Dict										=   'https://openaipublic.azureedge.net/jukebox/models/'
UpperCAmelCase_			:  Tuple										=   {
    '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       _lowercase							(					UpperCamelCase__							:       str      ):
 if key.endswith('.model.1.bias'      ) and len(key.split('.'      )      ) > 10:
  __A	:		str	  =      key.replace('.model.1.bias',					'.conv1d_1.bias'      )
 elif key.endswith('.model.1.weight'      ) and len(key.split('.'      )      ) > 10:
  __A	:		Dict	  =      key.replace('.model.1.weight',					'.conv1d_1.weight'      )
 elif key.endswith('.model.3.bias'      ) and len(key.split('.'      )      ) > 10:
  __A	:		Optional[int]	  =      key.replace('.model.3.bias',					'.conv1d_2.bias'      )
 elif key.endswith('.model.3.weight'      ) and len(key.split('.'      )      ) > 10:
  __A	:		str	  =      key.replace('.model.3.weight',					'.conv1d_2.weight'      )

 if "conditioner_blocks.0." in key:
  __A	:		Tuple	  =      key.replace('conditioner_blocks.0',					'conditioner_blocks'      )

 if "prime_prior" in key:
  __A	:		str	  =      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	:		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	:		str	  =      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       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       Optional[Any]      ):
 __A	:		Union[str, Any]	  =      {}
 import re

 __A	:		List[Any]	  =      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'      )
 __A	:		Dict	  =      re.compile(
     r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
 __A	:		Optional[int]	  =      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'      )

 __A	:		List[str]	  =      re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'      )
 __A	:		str	  =      re.compile(
     r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
 __A	:		str	  =      re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'      )

 __A	:		Optional[int]	  =      re.compile(r'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'      )
 __A	:		Union[str, Any]	  =      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(UpperCamelCase__      ):
   __A	:		Any	  =      re_encoder_block_conv_in.match(UpperCamelCase__      )
   __A	:		int	  =      regex_match.groups()
   __A	:		List[Any]	  =      int(groups[2]      ) * 2 + int(groups[3]      )
   __A	:		Dict	  =      f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
   __A	:		int	  =      re_encoder_block_conv_in.sub(UpperCamelCase__,					UpperCamelCase__      )

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

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

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

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

  elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__      ):
   __A	:		int	  =      re_decoder_block_proj_in.match(UpperCamelCase__      )
   __A	:		List[str]	  =      regex_match.groups()
   __A	:		Tuple	  =      f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
   __A	:		Optional[Any]	  =      re_decoder_block_proj_in.sub(UpperCamelCase__,					UpperCamelCase__      )

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

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

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

  # keep original key
  else:
   __A	:		List[Any]	  =      original_key

  __A	:		int	  =      replace_key(UpperCamelCase__      )

  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	:		Optional[Any]	  =      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	:		Optional[int]	  =      original_key
  __A	:		Optional[Any]	  =      value

 return new_dict


@torch.no_grad()
def       _lowercase							(					UpperCamelCase__							:       Union[str, Any]=None,					UpperCamelCase__							:       Any=None      ):
 for file in MODEL_MAPPING[model_name]:
  if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'      )[-1]}"""      ):
   __A	:		Any	  =      requests.get(f"""{PREFIX}{file}""",					allow_redirects=UpperCamelCase__      )
   os.makedirs(f"""{pytorch_dump_folder_path}/""",					exist_ok=UpperCamelCase__      )
   open(f"""{pytorch_dump_folder_path}/{file.split('/'      )[-1]}""",					'wb'      ).write(r.content      )

 __A	:		str	  =      MODEL_MAPPING[model_name.split('/'      )[-1]]

 __A	:		Optional[Any]	  =      JukeboxConfig.from_pretrained(UpperCamelCase__      )
 __A	:		Tuple	  =      JukeboxModel(UpperCamelCase__      )

 __A	:		List[Any]	  =      []
 __A	:		int	  =      {}
 for i, dict_name in enumerate(UpperCamelCase__      ):
  __A	:		List[Any]	  =      torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split('/'      )[-1]}"""      )['model']

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

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

 __A	:		Union[str, Any]	  =      weight_dict.pop(0      )
 model.vqvae.load_state_dict(UpperCamelCase__      )
 for i in range(len(UpperCamelCase__      )      ):
  model.priors[i].load_state_dict(weight_dict[2 - i]      )

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

 print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""      )
 model.save_pretrained(UpperCamelCase__      )

 return weight_dict


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






