





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    : List[Any]    	=  logging.get_logger(__name__)


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

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

							if "prime_prior" in key:
														_UpperCamelCase              =						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              =						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              =						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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
							_UpperCamelCase              =						{}
							import re

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

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

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

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

														elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase					):
																					_UpperCamelCase              =						re_encoder_block_proj_out.match(lowerCAmelCase					)
																					_UpperCamelCase              =						regex_match.groups()
																					_UpperCamelCase              =						f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
																					_UpperCamelCase              =						re_encoder_block_proj_out.sub(lowerCAmelCase    ,       lowerCAmelCase					)

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

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

														elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase					):
																					_UpperCamelCase              =						re_decoder_block_proj_in.match(lowerCAmelCase					)
																					_UpperCamelCase              =						regex_match.groups()
																					_UpperCamelCase              =						f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
																					_UpperCamelCase              =						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					):
																					_UpperCamelCase              =						re_prior_cond_conv_out.match(lowerCAmelCase					)
																					_UpperCamelCase              =						regex_match.groups()
																					_UpperCamelCase              =						int(groups[1]					) * 2 + int(groups[2]					) - 2
																					_UpperCamelCase              =						f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
																					_UpperCamelCase              =						re_prior_cond_conv_out.sub(lowerCAmelCase    ,       lowerCAmelCase					)

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

														elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase					):
																					_UpperCamelCase              =						re_prior_cond_proj_in.match(lowerCAmelCase					)
																					_UpperCamelCase              =						regex_match.groups()
																					_UpperCamelCase              =						f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
																					_UpperCamelCase              =						re_prior_cond_proj_in.sub(lowerCAmelCase    ,       lowerCAmelCase					)

														# keep original key
														else:
																					_UpperCamelCase              =						original_key

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

														_UpperCamelCase              =						original_key
														_UpperCamelCase              =						value

							return new_dict


@torch.no_grad()
def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase=None    ,       lowerCAmelCase=None					):
							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"					)[-1]}'''					):
																					_UpperCamelCase              =						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					)

							_UpperCamelCase              =						MODEL_MAPPING[model_name.split('''/'''					)[-1]]

							_UpperCamelCase              =						JukeboxConfig.from_pretrained(lowerCAmelCase					)
							_UpperCamelCase              =						JukeboxModel(lowerCAmelCase					)

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

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

														_UpperCamelCase              =						'''vqvae''' if i == 0 else f'''priors.{3 - i}'''
														_UpperCamelCase              =						fix_jukebox_keys(lowerCAmelCase    ,       model.state_dict()    ,       lowerCAmelCase    ,       lowerCAmelCase					)
														weight_dict.append(lowerCAmelCase					)

							_UpperCamelCase              =						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    : Tuple    	=  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    : Any    	=  parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						[]
       if len(lowerCAmelCase					) == 1:
              return [nums.copy()]
       for _ in range(len(lowerCAmelCase					)					):
              _UpperCamelCase              =						nums.pop(0					)
              _UpperCamelCase              =						permute(lowerCAmelCase					)
              for perm in permutations:
                     perm.append(lowerCAmelCase					)
              result.extend(lowerCAmelCase					)
              nums.append(lowerCAmelCase					)
       return result


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):

       def backtrack(lowerCAmelCase					):
              if start == len(lowerCAmelCase					) - 1:
                     output.append(nums[:]					)
              else:
                     for i in range(lowerCAmelCase    ,       len(lowerCAmelCase					)					):
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]
                            backtrack(start + 1					)
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]  # backtrack

       _UpperCamelCase              =						[]
       backtrack(0					)
       return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       lowercase    : Optional[Any]    	=  permutea([1, 2, 3])
       print(res)
       doctest.testmod()






