





'''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()
lowercase__   :				Union[str, Any]      =   logging.get_logger(__name__)


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

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

     if "prime_prior" in key:
          __A						:			List[Any]								=	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						:			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:
          __A						:			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 	_lowerCAmelCase      (  __snake_case  :			Optional[Any]			,							__snake_case  :			List[str]			,							__snake_case  :			Tuple			,							__snake_case  :			Tuple      )					->					Any:
     __A						:			List[str]								=	{}
     import re

     __A						:			str								=	re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'      )
     __A						:			Tuple								=	re.compile(
         r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
     __A						:			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						:			Tuple								=	re.compile(
         r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
     __A						:			Dict								=	re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'      )

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

          elif re_encoder_block_resnet.fullmatch(__snake_case      ):
               __A						:			int								=	re_encoder_block_resnet.match(__snake_case      )
               __A						:			Any								=	regex_match.groups()
               __A						:			List[Any]								=	int(groups[2]      ) * 2 + int(groups[3]      )
               __A						:			Optional[Any]								=	{'1': 1, '3': 2}[groups[-2]]
               __A						:			Any								=	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						:			Tuple								=	prefix + resnet_block
               __A						:			Dict								=	re_encoder_block_resnet.sub(__snake_case			,							__snake_case      )

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

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

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

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

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

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

          elif re_prior_cond_proj_in.fullmatch(__snake_case      ):
               __A						:			int								=	re_prior_cond_proj_in.match(__snake_case      )
               __A						:			Tuple								=	regex_match.groups()
               __A						:			int								=	f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
               __A						:			Dict								=	re_prior_cond_proj_in.sub(__snake_case			,							__snake_case      )

          # keep original key
          else:
               __A						:			Any								=	original_key

          __A						:			Any								=	replace_key(__snake_case      )

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

          __A						:			Dict								=	original_key
          __A						:			List[Any]								=	value

     return new_dict






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

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

     __A						:			Any								=	JukeboxConfig.from_pretrained(__snake_case      )
     __A						:			Tuple								=	JukeboxModel(__snake_case      )

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

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

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

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

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

     print(f'Saving model {model_name} to {pytorch_dump_folder_path}'      )
     model.save_pretrained(__snake_case      )

     return weight_dict


if __name__ == "__main__":
  lowercase__   :				Optional[int]      =   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__   :				int      =   parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






'''simple docstring'''




from __future__ import annotations






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]      )					->					None:
     create_state_space_tree(__snake_case			,							[]			,							0			,							[0 for i in range(len(__snake_case      )      )]      )






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]			,							__snake_case  :			list[int | str]			,							__snake_case  :			int			,							__snake_case  :			list[int]			,							)					->					None:

     if index == len(__snake_case      ):
          print(__snake_case      )
          return

     for i in range(len(__snake_case      )      ):
          if not index_used[i]:
               current_sequence.append(sequence[i]      )
               __A						:			Any								=	True
               create_state_space_tree(__snake_case			,							__snake_case			,							index + 1			,							__snake_case      )
               current_sequence.pop()
               __A						:			Any								=	False
lowercase__   :				list[int | str]      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

lowercase__   :				list[int | str]      =   ["A", "B", "C"]







generate_all_permutations(sequence_a)