'''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__     :	Optional[Any]				=			logging.get_logger(__name__)


UpperCamelCase__     :	Dict				=			'https://openaipublic.azureedge.net/jukebox/models/'
UpperCamelCase__     :	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(   _A					:				Tuple    ):
   '''simple docstring'''
   if key.endswith('''.model.1.bias'''    ) and len(key.split('''.'''    )    ) > 10:
      UpperCAmelCase__							:				Optional[Any]   				=  key.replace('''.model.1.bias'''  ,			'''.conv1d_1.bias'''    )
   elif key.endswith('''.model.1.weight'''    ) and len(key.split('''.'''    )    ) > 10:
      UpperCAmelCase__							:				List[Any]   				=  key.replace('''.model.1.weight'''  ,			'''.conv1d_1.weight'''    )
   elif key.endswith('''.model.3.bias'''    ) and len(key.split('''.'''    )    ) > 10:
      UpperCAmelCase__							:				Union[str, Any]   				=  key.replace('''.model.3.bias'''  ,			'''.conv1d_2.bias'''    )
   elif key.endswith('''.model.3.weight'''    ) and len(key.split('''.'''    )    ) > 10:
      UpperCAmelCase__							:				Optional[Any]   				=  key.replace('''.model.3.weight'''  ,			'''.conv1d_2.weight'''    )

   if "conditioner_blocks.0." in key:
      UpperCAmelCase__							:				Any   				=  key.replace('''conditioner_blocks.0'''  ,			'''conditioner_blocks'''    )

   if "prime_prior" in key:
      UpperCAmelCase__							:				int   				=  key.replace('''prime_prior'''  ,			'''encoder'''    )

   if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
      UpperCAmelCase__							:				Union[str, Any]   				=  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:
      UpperCAmelCase__							:				List[Any]   				=  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(   _A					:				Any  ,			_A					:				Optional[int]  ,			_A					:				List[Any]  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  {}
   import re

   UpperCAmelCase__							:				Union[str, Any]   				=  re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''    )
   UpperCAmelCase__							:				Union[str, Any]   				=  re.compile(
       R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''    )
   UpperCAmelCase__							:				Optional[int]   				=  re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''    )

   UpperCAmelCase__							:				Optional[int]   				=  re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''    )
   UpperCAmelCase__							:				Dict   				=  re.compile(
       R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''    )
   UpperCAmelCase__							:				List[str]   				=  re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''    )

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

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

      elif re_encoder_block_proj_out.fullmatch(_A    ):
         UpperCAmelCase__							:				List[Any]   				=  re_encoder_block_proj_out.match(_A    )
         UpperCAmelCase__							:				Union[str, Any]   				=  regex_match.groups()
         UpperCAmelCase__							:				Any   				=  F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
         UpperCAmelCase__							:				Dict   				=  re_encoder_block_proj_out.sub(_A  ,			_A    )

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

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

      elif re_decoder_block_proj_in.fullmatch(_A    ):
         UpperCAmelCase__							:				Union[str, Any]   				=  re_decoder_block_proj_in.match(_A    )
         UpperCAmelCase__							:				int   				=  regex_match.groups()
         UpperCAmelCase__							:				Optional[int]   				=  F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
         UpperCAmelCase__							:				Union[str, Any]   				=  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    ):
         UpperCAmelCase__							:				List[str]   				=  re_prior_cond_conv_out.match(_A    )
         UpperCAmelCase__							:				Dict   				=  regex_match.groups()
         UpperCAmelCase__							:				Any   				=  int(groups[1]    ) * 2 + int(groups[2]    ) - 2
         UpperCAmelCase__							:				int   				=  F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
         UpperCAmelCase__							:				Dict   				=  re_prior_cond_conv_out.sub(_A  ,			_A    )

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

      elif re_prior_cond_proj_in.fullmatch(_A    ):
         UpperCAmelCase__							:				Dict   				=  re_prior_cond_proj_in.match(_A    )
         UpperCAmelCase__							:				List[Any]   				=  regex_match.groups()
         UpperCAmelCase__							:				str   				=  F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
         UpperCAmelCase__							:				Optional[int]   				=  re_prior_cond_proj_in.sub(_A  ,			_A    )

      # keep original key
      else:
         UpperCAmelCase__							:				Union[str, Any]   				=  original_key

      UpperCAmelCase__							:				Any   				=  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:
         UpperCAmelCase__							:				Tuple   				=  model_state_dict[F'''{key_prefix}.{key}''']
         print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''    )
         UpperCAmelCase__							:				List[Any]   				=  original_key

      UpperCAmelCase__							:				Optional[Any]   				=  original_key
      UpperCAmelCase__							:				Dict   				=  value

   return new_dict







@torch.no_grad()
def __UpperCamelCase(   _A					:				str=None  ,			_A					:				Union[str, Any]=None    ):
   '''simple docstring'''
   for file in MODEL_MAPPING[model_name]:
      if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/'    )[-1]}'''    ):
         UpperCAmelCase__							:				List[str]   				=  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    )

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

   UpperCAmelCase__							:				int   				=  JukeboxConfig.from_pretrained(_A    )
   UpperCAmelCase__							:				Tuple   				=  JukeboxModel(_A    )

   UpperCAmelCase__							:				Optional[int]   				=  []
   UpperCAmelCase__							:				List[Any]   				=  {}
   for i, dict_name in enumerate(_A    ):
      UpperCAmelCase__							:				Union[str, Any]   				=  torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split('/'    )[-1]}'''    )['''model''']

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

      UpperCAmelCase__							:				Dict   				=  '''vqvae''' if i == 0 else F'''priors.{3 - i}'''
      UpperCAmelCase__							:				Optional[Any]   				=  fix_jukebox_keys(_A  ,			model.state_dict()  ,			_A  ,			_A    )
      weight_dict.append(_A    )

   UpperCAmelCase__							:				List[Any]   				=  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__":
  UpperCamelCase__     :	str				=			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__     :	Any				=			parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





'''simple docstring'''

import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__     :	int				=			logging.get_logger(__name__)

UpperCamelCase__     :	Optional[Any]				=			{'vocab_file': 'vocab.json'}

UpperCamelCase__     :	Tuple				=			{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

UpperCamelCase__     :	List[Any]				=			{'mgp-str': 27}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Dict      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      int      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_="[GO]"					,lowerCamelCase_="[GO]"					,lowerCamelCase_="[s]"					,lowerCamelCase_="[GO]"					,**lowerCamelCase_							)			->			str:



         '''simple docstring'''



         super().__init__(
             unk_token=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,**lowerCamelCase_					,)

         with open(lowerCamelCase_					,encoding='''utf-8'''							) as vocab_handle:
            UpperCAmelCase__							:				Optional[Any]   				=  json.load(lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  {v: k for k, v in self.vocab.items()}




      @property
      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         return len(self.vocab							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         return dict(self.vocab					,**self.added_tokens_encoder							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  []
         for s in text:
            char_tokens.extend(lowerCamelCase_							)
         return char_tokens




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.vocab.get(lowerCamelCase_					,self.vocab.get(self.unk_token							)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         return self.decoder.get(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_							)							)
            return
         UpperCAmelCase__							:				Union[str, Any]   				=  os.path.join(
             lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

         with open(lowerCamelCase_					,'''w'''					,encoding='''utf-8'''							) as f:
            f.write(json.dumps(self.vocab					,indent=2					,sort_keys=lowerCamelCase_					,ensure_ascii=lowerCamelCase_							) + '''\n'''							)

         return (vocab_file,)




