import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		logging.getLogger(__name__)



def 	__A		(      _A=2				,					_A=3				,					_A=16				,					_A = 10				,					_A = 2       ):



 """simple docstring"""


 def get_dataset(_A       ):
  __a							=	torch.randn(batch_size * n_batches				,					1       )
  return TensorDataset(_A				,					a * x + b + 0.1 * torch.randn(batch_size * n_batches				,					1       )       )

 __a							=	get_dataset(_A       )
 __a							=	get_dataset(_A       )
 __a							=	DataLoader(_A				,					shuffle=_A				,					batch_size=_A				,					num_workers=4       )
 __a							=	DataLoader(_A				,					shuffle=_A				,					batch_size=_A				,					num_workers=4       )
 return (train_dataloader, valid_dataloader)



def 	__A		(      _A				,					_A				,					_A				,					_A				,					_A				,					_A=None       ):



 """simple docstring"""


 __a							=	[]
 for epoch in range(_A       ):
  # Train quickly
  model.train()
  for batch in dataloader:
   __a							,  __a							=	batch
   __a							=	model(_A       )
   __a							=	torch.nn.functional.mse_loss(_A				,					_A       )
   accelerator.backward(_A       )
   optimizer.step()
   optimizer.zero_grad()
  rands.append(random.random()       )  # Introduce some randomness
  if scheduler is not None:
   scheduler.step()
 return rands
class  A_			(							nn.Module			):

  def __init__(		self			:   List[str]							):
   super().__init__()
   __a							=	nn.Parameter(torch.randn(1							)							)
   __a							=	nn.Parameter(torch.randn(1							)							)

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Tuple							):
   return x * self.a + self.b
class  A_			(							unittest.TestCase			):

  def 		_UpperCAmelCase (		self			:   List[str]							):
   with tempfile.TemporaryDirectory() as tmpdir:
    set_seed(42							)
    __a							=	DummyModel()
    __a							=	torch.optim.Adam(params=model.parameters()  ,				lr=1E-3							)
    __a							,  __a							=	dummy_dataloaders()
    __a							=	ProjectConfiguration(total_limit=1  ,				project_dir=__SCREAMING_SNAKE_CASE  ,				automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE							)
    # Train baseline
    __a							=	Accelerator(project_config=__SCREAMING_SNAKE_CASE							)
    __a							,  __a							,  __a							,  __a							=	accelerator.prepare(
        __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    # Save initial
    accelerator.save_state()

    # Save second state
    accelerator.save_state()
    self.assertEqual(len(os.listdir(accelerator.project_dir							)							)  ,				1							)

  def 		_UpperCAmelCase (		self			:   int							):
   with tempfile.TemporaryDirectory() as tmpdir:
    set_seed(42							)
    __a							=	DummyModel()
    __a							=	torch.optim.Adam(params=model.parameters()  ,				lr=1E-3							)
    __a							,  __a							=	dummy_dataloaders()
    # Train baseline
    __a							=	Accelerator()
    __a							,  __a							,  __a							,  __a							=	accelerator.prepare(
        __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    # Save initial
    __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"initial"							)
    accelerator.save_state(__SCREAMING_SNAKE_CASE							)
    ((__a)							,  (__a))							=	model.a.item(), model.b.item()
    __a							=	optimizer.state_dict()
    __a							=	train(3  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    ((__a)							,  (__a))							=	model.a.item(), model.b.item()
    __a							=	optimizer.state_dict()

    # Train partially
    set_seed(42							)
    __a							=	DummyModel()
    __a							=	torch.optim.Adam(params=model.parameters()  ,				lr=1E-3							)
    __a							,  __a							=	dummy_dataloaders()
    __a							=	Accelerator()
    __a							,  __a							,  __a							,  __a							=	accelerator.prepare(
        __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    accelerator.load_state(__SCREAMING_SNAKE_CASE							)
    ((__a)							,  (__a))							=	model.a.item(), model.b.item()
    __a							=	optimizer.state_dict()
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

    __a							=	train(2  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    # Save everything
    __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"checkpoint"							)
    accelerator.save_state(__SCREAMING_SNAKE_CASE							)

    # Load everything back in and make sure all states work
    accelerator.load_state(__SCREAMING_SNAKE_CASE							)
    test_rands += train(1  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    ((__a)							,  (__a))							=	model.a.item(), model.b.item()
    __a							=	optimizer.state_dict()
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   with tempfile.TemporaryDirectory() as tmpdir:
    set_seed(42							)
    __a							=	DummyModel()
    __a							=	torch.optim.Adam(params=model.parameters()  ,				lr=1E-3							)
    __a							,  __a							=	dummy_dataloaders()
    __a							=	ProjectConfiguration(automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE							)

    # Train baseline
    __a							=	Accelerator(project_dir=__SCREAMING_SNAKE_CASE  ,				project_config=__SCREAMING_SNAKE_CASE							)
    __a							,  __a							,  __a							,  __a							=	accelerator.prepare(
        __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    # Save initial
    accelerator.save_state()
    ((__a)							,  (__a))							=	model.a.item(), model.b.item()
    __a							=	optimizer.state_dict()
    __a							=	train(3  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    ((__a)							,  (__a))							=	model.a.item(), model.b.item()
    __a							=	optimizer.state_dict()

    # Train partially
    set_seed(42							)
    __a							=	DummyModel()
    __a							=	torch.optim.Adam(params=model.parameters()  ,				lr=1E-3							)
    __a							,  __a							=	dummy_dataloaders()
    __a							=	ProjectConfiguration(iteration=1  ,				automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE							)
    __a							=	Accelerator(project_dir=__SCREAMING_SNAKE_CASE  ,				project_config=__SCREAMING_SNAKE_CASE							)
    __a							,  __a							,  __a							,  __a							=	accelerator.prepare(
        __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    accelerator.load_state(os.path.join(__SCREAMING_SNAKE_CASE  ,				"checkpoints"  ,				"checkpoint_0"							)							)
    ((__a)							,  (__a))							=	model.a.item(), model.b.item()
    __a							=	optimizer.state_dict()
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

    __a							=	train(2  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    # Save everything
    accelerator.save_state()

    # Load everything back in and make sure all states work
    accelerator.load_state(os.path.join(__SCREAMING_SNAKE_CASE  ,				"checkpoints"  ,				"checkpoint_1"							)							)
    test_rands += train(1  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    ((__a)							,  (__a))							=	model.a.item(), model.b.item()
    __a							=	optimizer.state_dict()
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	torch.tensor([1, 2, 3]							)
   __a							=	torch.tensor([2, 3, 4]							)
   __a							=	DummyModel()
   __a							=	torch.optim.Adam(net.parameters()							)
   __a							=	Accelerator()
   with self.assertRaises(__SCREAMING_SNAKE_CASE							) as ve:
    accelerator.register_for_checkpointing(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	str(ve.exception							)
   self.assertTrue("Item at index 0" in message							)
   self.assertTrue("Item at index 1" in message							)
   self.assertFalse("Item at index 2" in message							)
   self.assertFalse("Item at index 3" in message							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   with tempfile.TemporaryDirectory() as tmpdir:
    set_seed(42							)
    __a							=	DummyModel()
    __a							=	torch.optim.Adam(params=model.parameters()  ,				lr=1E-3							)
    __a							=	torch.optim.lr_scheduler.StepLR(__SCREAMING_SNAKE_CASE  ,				step_size=1  ,				gamma=0.99							)
    __a							,  __a							=	dummy_dataloaders()
    __a							=	ProjectConfiguration(automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE							)
    # Train baseline
    __a							=	Accelerator(project_dir=__SCREAMING_SNAKE_CASE  ,				project_config=__SCREAMING_SNAKE_CASE							)
    __a							,  __a							,  __a							,  __a							,  __a							=	accelerator.prepare(
        __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    # Save initial
    accelerator.save_state()
    __a							=	scheduler.state_dict()
    train(3  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    self.assertNotEqual(__SCREAMING_SNAKE_CASE  ,				scheduler.state_dict()							)

    # Load everything back in and make sure all states work
    accelerator.load_state(os.path.join(__SCREAMING_SNAKE_CASE  ,				"checkpoints"  ,				"checkpoint_0"							)							)
    self.assertEqual(__SCREAMING_SNAKE_CASE  ,				scheduler.state_dict()							)

  def 		_UpperCAmelCase (		self			:   Dict							):
   with tempfile.TemporaryDirectory() as tmpdir:
    set_seed(42							)
    __a							=	DummyModel()
    __a							=	ProjectConfiguration(automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE  ,				total_limit=2							)
    # Train baseline
    __a							=	Accelerator(project_dir=__SCREAMING_SNAKE_CASE  ,				project_config=__SCREAMING_SNAKE_CASE							)
    __a							=	accelerator.prepare(__SCREAMING_SNAKE_CASE							)
    # Save 3 states:
    for _ in range(11							):
     accelerator.save_state()
    self.assertTrue(not os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE  ,				"checkpoints"  ,				"checkpoint_0"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE  ,				"checkpoints"  ,				"checkpoint_9"							)							)							)
    self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE  ,				"checkpoints"  ,				"checkpoint_10"							)							)							)

  @require_cuda
  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__							)]
   execute_subprocess_async(__SCREAMING_SNAKE_CASE  ,				env=os.environ.copy()							)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						List[Any]				  =		"""/tmp/accelerate/state_checkpointing"""
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		DummyModel()
  SCREAMING_SNAKE_CASE			:						Dict				  =		torch.optim.Adam(params=model.parameters(), lr=1e-3)
  SCREAMING_SNAKE_CASE			:						List[str]				  =		torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
  SCREAMING_SNAKE_CASE  ,    SCREAMING_SNAKE_CASE			:						List[Any]				  =		dummy_dataloaders()
  SCREAMING_SNAKE_CASE			:						Dict				  =		ProjectConfiguration(automatic_checkpoint_naming=True)
  # Train baseline
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
  if accelerator.process_index == 0:
    if os.path.exists(savedir):
      shutil.rmtree(savedir)
    os.makedirs(savedir)
  SCREAMING_SNAKE_CASE  ,    SCREAMING_SNAKE_CASE  ,    SCREAMING_SNAKE_CASE  ,    SCREAMING_SNAKE_CASE  ,    SCREAMING_SNAKE_CASE			:						List[str]				  =		accelerator.prepare(
      model, optimizer, train_dataloader, valid_dataloader, scheduler
  )
  SCREAMING_SNAKE_CASE  ,    SCREAMING_SNAKE_CASE			:						str				  =		accelerator.prepare(model, optimizer)
  train(3, model, train_dataloader, optimizer, accelerator, scheduler)
  # Check that the intial optimizer is loaded on the GPU
  for group in optimizer.param_groups:
    SCREAMING_SNAKE_CASE			:						Tuple				  =		group["""params"""][0].device
    break
  assert param_device.type == accelerator.device.type
  SCREAMING_SNAKE_CASE			:						Any				  =		model.cpu()
  accelerator.wait_for_everyone()
  accelerator.save_state()
  accelerator.wait_for_everyone()

  # Check CPU state
  accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
  for group in optimizer.param_groups:
    SCREAMING_SNAKE_CASE			:						List[Any]				  =		group["""params"""][0].device
    break
  assert (
      param_device.type == torch.device("""cpu""").type
  ), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

  # Check device state
  model.to(accelerator.device)
  accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
  for group in optimizer.param_groups:
    SCREAMING_SNAKE_CASE			:						List[str]				  =		group["""params"""][0].device
    break
  assert (
      param_device.type == accelerator.device.type
  ), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

  # Check error
  with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
    accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
  accelerator.wait_for_everyone()
  if accelerator.process_index == 0:
    shutil.rmtree(savedir)
  accelerator.wait_for_everyone()



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()
SCREAMING_SNAKE_CASE			:						Optional[int]				  =		logging.get_logger(__name__)


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		"""https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE			:						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 	__A		(      _A       ):



 """simple docstring"""


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

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

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

 if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
  __a							=	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:
  __a							=	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"""


 __a							=	{}
 import re

 __a							=	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"       )
 __a							=	re.compile(
     r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
 __a							=	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"       )

 __a							=	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"       )
 __a							=	re.compile(
     r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
 __a							=	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"       )

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

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

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

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

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

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

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

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

  # keep original key
  else:
   __a							=	original_key

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

  __a							=	original_key
  __a							=	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]}"""       ):
   __a							=	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       )

 __a							=	MODEL_MAPPING[model_name.split("/"       )[-1]]

 __a							=	JukeboxConfig.from_pretrained(_A       )
 __a							=	JukeboxModel(_A       )

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

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

  __a							=	"vqvae" if i == 0 else f"""priors.{3 - i}"""
  __a							=	fix_jukebox_keys(_A				,					model.state_dict()				,					_A				,					_A       )
  weight_dict.append(_A       )

 __a							=	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__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		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.""",
  )
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


