
"""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()
A__							=   logging.get_logger(__name__)


A__							=   'https://openaipublic.azureedge.net/jukebox/models/'
A__							=   {
    '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		(		_UpperCamelCase						):






  """simple docstring"""



  if key.endswith('''.model.1.bias'''						) and len(key.split('''.'''						)						) > 10:
    _lowercase:			List[str]       =     key.replace('''.model.1.bias''' ,				'''.conv1d_1.bias'''						)
  elif key.endswith('''.model.1.weight'''						) and len(key.split('''.'''						)						) > 10:
    _lowercase:			int       =     key.replace('''.model.1.weight''' ,				'''.conv1d_1.weight'''						)
  elif key.endswith('''.model.3.bias'''						) and len(key.split('''.'''						)						) > 10:
    _lowercase:			str       =     key.replace('''.model.3.bias''' ,				'''.conv1d_2.bias'''						)
  elif key.endswith('''.model.3.weight'''						) and len(key.split('''.'''						)						) > 10:
    _lowercase:			List[str]       =     key.replace('''.model.3.weight''' ,				'''.conv1d_2.weight'''						)

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

  if "prime_prior" in key:
    _lowercase:			Union[str, 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:
    _lowercase:			Dict       =     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:
    _lowercase:			Optional[int]       =     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		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Any       =     {}
  import re

  _lowercase:			Tuple       =     re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''						)
  _lowercase:			Optional[int]       =     re.compile(
      R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''						)
  _lowercase:			Tuple       =     re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''						)

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

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

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

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

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

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

    elif re_decoder_block_proj_in.fullmatch(_SCREAMING_SNAKE_CASE						):
      _lowercase:			Any       =     re_decoder_block_proj_in.match(_SCREAMING_SNAKE_CASE						)
      _lowercase:			List[Any]       =     regex_match.groups()
      _lowercase:			List[Any]       =     f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
      _lowercase:			Union[str, Any]       =     re_decoder_block_proj_in.sub(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE						)

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

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

    elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE						):
      _lowercase:			Optional[Any]       =     re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE						)
      _lowercase:			Tuple       =     regex_match.groups()
      _lowercase:			Optional[Any]       =     f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
      _lowercase:			Optional[Any]       =     re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE						)

    # keep original key
    else:
      _lowercase:			Tuple       =     original_key

    _lowercase:			Tuple       =     replace_key(_SCREAMING_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:
      _lowercase:			List[str]       =     model_state_dict[f'''{key_prefix}.{key}''']
      print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''						)
      _lowercase:			Dict       =     original_key

    _lowercase:			Optional[int]       =     original_key
    _lowercase:			int       =     value

  return new_dict




@torch.no_grad()
def  _lowerCAmelCase		(		_UpperCamelCase=None ,				_UpperCamelCase=None						):






  """simple docstring"""



  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"						)[-1]}'''						):
      _lowercase:			Tuple       =     requests.get(f'''{PREFIX}{file}''' ,				allow_redirects=_SCREAMING_SNAKE_CASE						)
      os.makedirs(f'''{pytorch_dump_folder_path}/''' ,				exist_ok=_SCREAMING_SNAKE_CASE						)
      open(f'''{pytorch_dump_folder_path}/{file.split("/"						)[-1]}''' ,				'''wb'''						).write(r.content						)

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

  _lowercase:			List[Any]       =     JukeboxConfig.from_pretrained(_SCREAMING_SNAKE_CASE						)
  _lowercase:			Tuple       =     JukeboxModel(_SCREAMING_SNAKE_CASE						)

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

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

    _lowercase:			List[str]       =     '''vqvae''' if i == 0 else f'''priors.{3 - i}'''
    _lowercase:			Tuple       =     fix_jukebox_keys(_SCREAMING_SNAKE_CASE ,				model.state_dict() ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE						)
    weight_dict.append(_SCREAMING_SNAKE_CASE						)

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

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

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

  return weight_dict


if __name__ == "__main__":
  A__							=   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.',
  )
  A__							=   parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


"""simple docstring"""




from typing import Any

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  return np.array_equal(_UpperCamelCase ,				matrix.conjugate().T						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			str       =     v.conjugate().T
  _lowercase:			List[Any]       =     v_star.dot(_UpperCamelCase						)
  assert isinstance(_UpperCamelCase ,				np.ndarray						)
  return (v_star_dot.dot(_UpperCamelCase						)) / (v_star.dot(_UpperCamelCase						))




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			int       =     np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]						)
  _lowercase:			str       =     np.array([[1], [2], [3]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  print(rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						)						)

  _lowercase:			Any       =     np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  assert rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						) == float(3						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()
