


'''simple docstring'''






import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__A :  Tuple    							=				"sshleifer/mar_enro_6_3_student"






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







				"""simple docstring"""



				def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
						super().setUp()

						lowerCAmelCase_      :				Any      =       cached_path(
						    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""   ,						extract_compressed_file=lowerCamelCase   ,						)
						lowerCAmelCase_      :				Optional[Any]      =       F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'

				@slow
				@require_torch_gpu
				def __lowercase       (       self   :  str		)       ->  str:
						MarianMTModel.from_pretrained(lowerCamelCase		)





				@slow
				@require_torch_gpu
				def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
						lowerCAmelCase_      :				str      =       {
						    """$MAX_LEN""": 64,
						    """$BS""": 64,
						    """$GAS""": 1,
						    """$ENRO_DIR""": self.data_dir,
						    """facebook/mbart-large-cc25""": MARIAN_MODEL,
						    # "val_check_interval=0.25": "val_check_interval=1.0",
						    """--learning_rate=3e-5""": """--learning_rate 3e-4""",
						    """--num_train_epochs 6""": """--num_train_epochs 1""",
						}

						# Clean up bash script
						lowerCAmelCase_      :				Dict      =       (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""		)[1].strip()
						lowerCAmelCase_      :				Optional[int]      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
						for k, v in env_vars_to_replace.items():
								lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
						lowerCAmelCase_      :				Optional[Any]      =       self.get_auto_remove_tmp_dir()

						# bash_script = bash_script.replace("--fp16 ", "")
						lowerCAmelCase_      :				Tuple      =       F'\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        '.split()
						# XXX: args.gpus > 1 : handle multi_gpu in the future

						lowerCAmelCase_      :				Tuple      =       ["""finetune.py"""] + bash_script.split() + args
						with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
								lowerCAmelCase_      :				Optional[Any]      =       argparse.ArgumentParser()
								lowerCAmelCase_      :				Any      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
								lowerCAmelCase_      :				List[str]      =       SummarizationModule.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
								lowerCAmelCase_      :				Tuple      =       parser.parse_args()
								lowerCAmelCase_      :				Dict      =       main(lowerCamelCase		)

						# Check metrics
						lowerCAmelCase_      :				int      =       load_json(model.metrics_save_path		)
						lowerCAmelCase_      :				Optional[Any]      =       metrics["""val"""][0]
						lowerCAmelCase_      :				Tuple      =       metrics["""val"""][-1]
						self.assertEqual(len(metrics["""val"""]		)   ,						(args.max_epochs / args.val_check_interval)		)
						assert isinstance(last_step_stats[F'val_avg_{model.val_metric}']   ,						lowerCamelCase		)

						self.assertGreater(last_step_stats["""val_avg_gen_time"""]   ,						0.01		)
						# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
						self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]   ,						1.0		)

						# test learning requirements:

						# 1. BLEU improves over the course of training by more than 2 pts
						self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]   ,						2		)

						# 2. BLEU finishes above 17
						self.assertGreater(last_step_stats["""val_avg_bleu"""]   ,						17		)

						# 3. test BLEU and val BLEU within ~1.1 pt.
						self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]		)   ,						1.1		)

						# check lightning ckpt can be loaded and has a reasonable statedict
						lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
						lowerCAmelCase_      :				Any      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
						lowerCAmelCase_      :				Union[str, Any]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
						lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
						lowerCAmelCase_      :				List[str]      =       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
						assert expected_key in ckpt["state_dict"]
						assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

						# TODO: turn on args.do_predict when PL bug fixed.
						if args.do_predict:
								lowerCAmelCase_      :				List[str]      =       {os.path.basename(lowerCamelCase		) for p in contents}
								assert "test_generations.txt" in contents
								assert "test_results.txt" in contents
								# assert len(metrics["val"]) ==  desired_n_evals
								assert len(metrics["""test"""]		) == 1







class        __snake_case	(       _SCREAMING_SNAKE_CASE):







				"""simple docstring"""



				@timeout_decorator.timeout(6_00		)
				@slow
				@require_torch_gpu
				def __lowercase       (       self   :  Optional[Any]		)       ->  Dict:
						lowerCAmelCase_      :				List[str]      =       F'{self.test_file_dir_str}/test_data/wmt_en_ro'
						lowerCAmelCase_      :				Dict      =       {
						    """--fp16_opt_level=O1""": """""",
						    """$MAX_LEN""": 1_28,
						    """$BS""": 16,
						    """$GAS""": 1,
						    """$ENRO_DIR""": data_dir,
						    """$m""": """sshleifer/student_marian_en_ro_6_1""",
						    """val_check_interval=0.25""": """val_check_interval=1.0""",
						}

						# Clean up bash script
						lowerCAmelCase_      :				int      =       (
						    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""		)[1].strip()
						)
						lowerCAmelCase_      :				str      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
						lowerCAmelCase_      :				Tuple      =       bash_script.replace("""--fp16 """   ,						""" """		)

						for k, v in env_vars_to_replace.items():
								lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
						lowerCAmelCase_      :				int      =       self.get_auto_remove_tmp_dir()
						lowerCAmelCase_      :				Optional[Any]      =       bash_script.replace("""--fp16"""   ,						""""""		)
						lowerCAmelCase_      :				Dict      =       6
						lowerCAmelCase_      :				List[Any]      =       (
						    ["""distillation.py"""]
						    + bash_script.split()
						    + [
						        F'--output_dir={output_dir}',
						        """--gpus=1""",
						        """--learning_rate=1e-3""",
						        F'--num_train_epochs={epochs}',
						        """--warmup_steps=10""",
						        """--val_check_interval=1.0""",
						        """--do_predict""",
						    ]
						)
						with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
								lowerCAmelCase_      :				Dict      =       argparse.ArgumentParser()
								lowerCAmelCase_      :				int      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
								lowerCAmelCase_      :				List[str]      =       SummarizationDistiller.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
								lowerCAmelCase_      :				List[Any]      =       parser.parse_args()
								# assert args.gpus == gpus THIS BREAKS for multi_gpu

								lowerCAmelCase_      :				str      =       distill_main(lowerCamelCase		)

						# Check metrics
						lowerCAmelCase_      :				Union[str, Any]      =       load_json(model.metrics_save_path		)
						lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][0]
						lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][-1]
						assert len(metrics["""val"""]		) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

						assert last_step_stats["val_avg_gen_time"] >= 0.01

						assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
						assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
						assert isinstance(last_step_stats[F'val_avg_{model.val_metric}']   ,						lowerCamelCase		)

						# check lightning ckpt can be loaded and has a reasonable statedict
						lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
						lowerCAmelCase_      :				Dict      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
						lowerCAmelCase_      :				Optional[int]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
						lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
						lowerCAmelCase_      :				Tuple      =       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
						assert expected_key in ckpt["state_dict"]
						assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

						# TODO: turn on args.do_predict when PL bug fixed.
						if args.do_predict:
								lowerCAmelCase_      :				Union[str, Any]      =       {os.path.basename(lowerCamelCase		) for p in contents}
								assert "test_generations.txt" in contents
								assert "test_results.txt" in contents
								# assert len(metrics["val"]) ==  desired_n_evals
								assert len(metrics["""test"""]		) == 1







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


__A :  str    							=				"https://openaipublic.azureedge.net/jukebox/models/"
__A :  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     UpperCamelCase_      (   A__ :       int ):
  '''simple docstring'''





  if key.endswith(""".model.1.bias""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				Union[str, Any]      =       key.replace(""".model.1.bias"""							,   """.conv1d_1.bias""" )
  elif key.endswith(""".model.1.weight""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				Union[str, Any]      =       key.replace(""".model.1.weight"""							,   """.conv1d_1.weight""" )
  elif key.endswith(""".model.3.bias""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				Union[str, Any]      =       key.replace(""".model.3.bias"""							,   """.conv1d_2.bias""" )
  elif key.endswith(""".model.3.weight""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				int      =       key.replace(""".model.3.weight"""							,   """.conv1d_2.weight""" )

  if "conditioner_blocks.0." in key:
    lowerCAmelCase_      :				Any      =       key.replace("""conditioner_blocks.0"""							,   """conditioner_blocks""" )

  if "prime_prior" in key:
    lowerCAmelCase_      :				str      =       key.replace("""prime_prior"""							,   """encoder""" )

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    lowerCAmelCase_      :				int      =       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:
    lowerCAmelCase_      :				List[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     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Optional[Any]							,   A__ :       Tuple							,   A__ :       Any ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       {}
  import re

  lowerCAmelCase_      :				Union[str, Any]      =       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
  lowerCAmelCase_      :				str      =       re.compile(
      R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
  lowerCAmelCase_      :				Dict      =       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )

  lowerCAmelCase_      :				int      =       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
  lowerCAmelCase_      :				Optional[int]      =       re.compile(
      R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
  lowerCAmelCase_      :				Union[str, Any]      =       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )

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

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

    elif re_encoder_block_proj_out.fullmatch(A__ ):
      lowerCAmelCase_      :				int      =       re_encoder_block_proj_out.match(A__ )
      lowerCAmelCase_      :				Tuple      =       regex_match.groups()
      lowerCAmelCase_      :				Optional[Any]      =       f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
      lowerCAmelCase_      :				str      =       re_encoder_block_proj_out.sub(A__							,   A__ )

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

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

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

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

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

    elif re_prior_cond_proj_in.fullmatch(A__ ):
      lowerCAmelCase_      :				List[str]      =       re_prior_cond_proj_in.match(A__ )
      lowerCAmelCase_      :				Optional[Any]      =       regex_match.groups()
      lowerCAmelCase_      :				Any      =       f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
      lowerCAmelCase_      :				List[str]      =       re_prior_cond_proj_in.sub(A__							,   A__ )

    # keep original key
    else:
      lowerCAmelCase_      :				Optional[Any]      =       original_key

    lowerCAmelCase_      :				Optional[Any]      =       replace_key(A__ )

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

    lowerCAmelCase_      :				Dict      =       original_key
    lowerCAmelCase_      :				Optional[int]      =       value

  return new_dict







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Optional[Any]=None							,   A__ :       str=None ):
  '''simple docstring'''





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

  lowerCAmelCase_      :				Optional[int]      =       MODEL_MAPPING[model_name.split("""/""" )[-1]]

  lowerCAmelCase_      :				List[str]      =       JukeboxConfig.from_pretrained(A__ )
  lowerCAmelCase_      :				Dict      =       JukeboxModel(A__ )

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

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

    lowerCAmelCase_      :				Union[str, Any]      =       """vqvae""" if i == 0 else f'priors.{3 - i}'
    lowerCAmelCase_      :				str      =       fix_jukebox_keys(A__							,   model.state_dict()							,   A__							,   A__ )
    weight_dict.append(A__ )

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

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

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

  return weight_dict


if __name__ == "__main__":
  __A :  str    							=				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 :  Optional[Any]    							=				parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



