






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()
snake_case__   :						List[str]													=   logging.get_logger(__name__)


snake_case__   :						int													=   """https://openaipublic.azureedge.net/jukebox/models/"""
snake_case__   :						List[Any]													=   {
    """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      _snake_case     (__lowercase):
   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      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase):
   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(__lowercase):
         UpperCamelCase_						=  re_encoder_block_conv_in.match(__lowercase)
         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(__lowercase     ,   __lowercase)

      elif re_encoder_block_resnet.fullmatch(__lowercase):
         UpperCamelCase_						=  re_encoder_block_resnet.match(__lowercase)
         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(__lowercase     ,   __lowercase)

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

      # rename vqvae.decoder keys
      elif re_decoder_block_conv_out.fullmatch(__lowercase):
         UpperCamelCase_						=  re_decoder_block_conv_out.match(__lowercase)
         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(__lowercase     ,   __lowercase)

      elif re_decoder_block_resnet.fullmatch(__lowercase):
         UpperCamelCase_						=  re_decoder_block_resnet.match(__lowercase)
         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(__lowercase     ,   __lowercase)

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

      elif re_prior_cond_resnet.fullmatch(__lowercase):
         UpperCamelCase_						=  re_prior_cond_resnet.match(__lowercase)
         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(__lowercase     ,   __lowercase)

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

      # keep original key
      else:
         UpperCamelCase_						=  original_key

      UpperCamelCase_						=  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:
         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      _snake_case     (__lowercase=None     ,   __lowercase=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=__lowercase)
         os.makedirs(f"""{pytorch_dump_folder_path}/"""     ,   exist_ok=__lowercase)
         open(f"""{pytorch_dump_folder_path}/{file.split("/")[-1]}"""     ,   'wb').write(r.content)

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

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

   UpperCamelCase_						=  []
   UpperCamelCase_						=  {}
   for i, dict_name in enumerate(__lowercase):
      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(__lowercase     ,   model.state_dict()     ,   __lowercase     ,   __lowercase)
      weight_dict.append(__lowercase)

   UpperCamelCase_						=  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__":
 snake_case__   :						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.""",
 )
 snake_case__   :						int													=   parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)













from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @slow
  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'		,		'prajjwal1/bert-tiny'			)
     UpperCamelCase_						=  BertTokenizer.from_pretrained('bert-base-uncased'			)

     UpperCamelCase_						=  bertabert.config.encoder.vocab_size
     UpperCamelCase_						=  tokenizer.sep_token_id
     UpperCamelCase_						=  tokenizer.cls_token_id
     UpperCamelCase_						=  128

     UpperCamelCase_						=  datasets.load_dataset('cnn_dailymail'		,		'3.0.0'		,		split='train[:1%]'			)
     UpperCamelCase_						=  datasets.load_dataset('cnn_dailymail'		,		'3.0.0'		,		split='validation[:1%]'			)

     UpperCamelCase_						=  train_dataset.select(range(32			)			)
     UpperCamelCase_						=  val_dataset.select(range(16			)			)

     UpperCamelCase_						=  4

     def _map_to_encoder_decoder_inputs(_UpperCAmelCase			):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        UpperCamelCase_						=  tokenizer(batch['article']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		max_length=512			)
        UpperCamelCase_						=  tokenizer(batch['highlights']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		max_length=128			)
        UpperCamelCase_						=  inputs.input_ids
        UpperCamelCase_						=  inputs.attention_mask

        UpperCamelCase_						=  outputs.input_ids
        UpperCamelCase_						=  outputs.input_ids.copy()
        UpperCamelCase_						=  [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
        ]
        UpperCamelCase_						=  outputs.attention_mask

        assert all(len(_UpperCAmelCase			) == 512 for x in inputs.input_ids			)
        assert all(len(_UpperCAmelCase			) == 128 for x in outputs.input_ids			)

        return batch

     def _compute_metrics(_UpperCAmelCase			):
        UpperCamelCase_						=  pred.label_ids
        UpperCamelCase_						=  pred.predictions

        # all unnecessary tokens are removed
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)

        UpperCamelCase_						=  sum([int(pred_str[i] == label_str[i]			) for i in range(len(_UpperCAmelCase			)			)]			) / len(_UpperCAmelCase			)

        return {"accuracy": accuracy}

     # map train dataset
     UpperCamelCase_						=  train_dataset.map(
         _map_to_encoder_decoder_inputs		,		batched=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		remove_columns=['article', 'highlights']		,		)
     train_dataset.set_format(
         type='torch'		,		columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']		,		)

     # same for validation dataset
     UpperCamelCase_						=  val_dataset.map(
         _map_to_encoder_decoder_inputs		,		batched=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		remove_columns=['article', 'highlights']		,		)
     val_dataset.set_format(
         type='torch'		,		columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']		,		)

     UpperCamelCase_						=  self.get_auto_remove_tmp_dir()

     UpperCamelCase_						=  SeqaSeqTrainingArguments(
         output_dir=_UpperCAmelCase		,		per_device_train_batch_size=_UpperCAmelCase		,		per_device_eval_batch_size=_UpperCAmelCase		,		predict_with_generate=_UpperCAmelCase		,		evaluation_strategy='steps'		,		do_train=_UpperCAmelCase		,		do_eval=_UpperCAmelCase		,		warmup_steps=0		,		eval_steps=2		,		logging_steps=2		,		)

     # instantiate trainer
     UpperCamelCase_						=  SeqaSeqTrainer(
         model=_UpperCAmelCase		,		args=_UpperCAmelCase		,		compute_metrics=_compute_metrics		,		train_dataset=_UpperCAmelCase		,		eval_dataset=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase		,		)

     # start training
     trainer.train()





