

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()
lowerCAmelCase__      		=			logging.get_logger(__name__)


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






 """simple docstring"""



 if key.endswith('''.model.1.bias'''   ) and len(key.split('''.'''   )   ) > 10:
  __lowercase													=				key.replace('''.model.1.bias'''  ,       '''.conv1d_1.bias'''   )
 elif key.endswith('''.model.1.weight'''   ) and len(key.split('''.'''   )   ) > 10:
  __lowercase													=				key.replace('''.model.1.weight'''  ,       '''.conv1d_1.weight'''   )
 elif key.endswith('''.model.3.bias'''   ) and len(key.split('''.'''   )   ) > 10:
  __lowercase													=				key.replace('''.model.3.bias'''  ,       '''.conv1d_2.bias'''   )
 elif key.endswith('''.model.3.weight'''   ) and len(key.split('''.'''   )   ) > 10:
  __lowercase													=				key.replace('''.model.3.weight'''  ,       '''.conv1d_2.weight'''   )

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

 if "prime_prior" in key:
  __lowercase													=				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													=				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													=				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 	_A  (					A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				{}
 import re

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

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

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

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

  elif re_encoder_block_proj_out.fullmatch(lowercase__   ):
   __lowercase													=				re_encoder_block_proj_out.match(lowercase__   )
   __lowercase													=				regex_match.groups()
   __lowercase													=				F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
   __lowercase													=				re_encoder_block_proj_out.sub(lowercase__  ,       lowercase__   )

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

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

  elif re_decoder_block_proj_in.fullmatch(lowercase__   ):
   __lowercase													=				re_decoder_block_proj_in.match(lowercase__   )
   __lowercase													=				regex_match.groups()
   __lowercase													=				F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
   __lowercase													=				re_decoder_block_proj_in.sub(lowercase__  ,       lowercase__   )

  # rename prior cond.model to upsampler.upsample_block and resnet
  elif re_prior_cond_conv_out.fullmatch(lowercase__   ):
   __lowercase													=				re_prior_cond_conv_out.match(lowercase__   )
   __lowercase													=				regex_match.groups()
   __lowercase													=				int(groups[1]   ) * 2 + int(groups[2]   ) - 2
   __lowercase													=				F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
   __lowercase													=				re_prior_cond_conv_out.sub(lowercase__  ,       lowercase__   )

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

  elif re_prior_cond_proj_in.fullmatch(lowercase__   ):
   __lowercase													=				re_prior_cond_proj_in.match(lowercase__   )
   __lowercase													=				regex_match.groups()
   __lowercase													=				F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
   __lowercase													=				re_prior_cond_proj_in.sub(lowercase__  ,       lowercase__   )

  # keep original key
  else:
   __lowercase													=				original_key

  __lowercase													=				replace_key(lowercase__   )

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

  __lowercase													=				original_key
  __lowercase													=				value

 return new_dict


@torch.no_grad()
def 	_A  (					A__=None  ,       A__=None   ):






 """simple docstring"""



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

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

 __lowercase													=				JukeboxConfig.from_pretrained(lowercase__   )
 __lowercase													=				JukeboxModel(lowercase__   )

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

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

  __lowercase													=				'''vqvae''' if i == 0 else F"priors.{3 - i}"
  __lowercase													=				fix_jukebox_keys(lowercase__  ,       model.state_dict()  ,       lowercase__  ,       lowercase__   )
  weight_dict.append(lowercase__   )

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

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

 print(F"Saving model {model_name} to {pytorch_dump_folder_path}"   )
 model.save_pretrained(lowercase__   )

 return weight_dict


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




'''simple docstring'''






from collections.abc import Callable

import numpy as np


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				int(np.ceil((x_end - xa) / step_size   )   )
 __lowercase													=				np.zeros((n + 1,)   )
 __lowercase													=				ya
 __lowercase													=				xa

 for k in range(A__   ):
  __lowercase													=				y[k] + step_size * ode_func(A__  ,       y[k]   )
  x += step_size

 return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()

