






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()
__UpperCAmelCase								=						logging.get_logger(__name__)


__UpperCAmelCase								=						'https://openaipublic.azureedge.net/jukebox/models/'
__UpperCAmelCase								=						{
    '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      lowercase__      (     __snake_case    :							List[Any]  ):


					'''simple docstring'''


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

					if "conditioner_blocks.0." in key:
										UpperCAmelCase_    :      List[str]            =						key.replace('conditioner_blocks.0'     ,       'conditioner_blocks'  )

					if "prime_prior" in key:
										UpperCAmelCase_    :      List[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:
										UpperCAmelCase_    :      Optional[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:
										UpperCAmelCase_    :      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      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							Any     ,       __snake_case    :							Dict     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						{}
					import re

					UpperCAmelCase_    :      List[str]            =						re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'  )
					UpperCAmelCase_    :      List[str]            =						re.compile(
					    R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
					UpperCAmelCase_    :      int            =						re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

					UpperCAmelCase_    :      Optional[Any]            =						re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'  )
					UpperCAmelCase_    :      Optional[Any]            =						re.compile(
					    R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
					UpperCAmelCase_    :      Dict            =						re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

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

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

										elif re_encoder_block_proj_out.fullmatch(__snake_case  ):
															UpperCAmelCase_    :      str            =						re_encoder_block_proj_out.match(__snake_case  )
															UpperCAmelCase_    :      str            =						regex_match.groups()
															UpperCAmelCase_    :      int            =						F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
															UpperCAmelCase_    :      List[str]            =						re_encoder_block_proj_out.sub(__snake_case     ,       __snake_case  )

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

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

										elif re_decoder_block_proj_in.fullmatch(__snake_case  ):
															UpperCAmelCase_    :      Dict            =						re_decoder_block_proj_in.match(__snake_case  )
															UpperCAmelCase_    :      Tuple            =						regex_match.groups()
															UpperCAmelCase_    :      Optional[int]            =						F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
															UpperCAmelCase_    :      Any            =						re_decoder_block_proj_in.sub(__snake_case     ,       __snake_case  )

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

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

										elif re_prior_cond_proj_in.fullmatch(__snake_case  ):
															UpperCAmelCase_    :      Optional[int]            =						re_prior_cond_proj_in.match(__snake_case  )
															UpperCAmelCase_    :      Tuple            =						regex_match.groups()
															UpperCAmelCase_    :      List[str]            =						F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
															UpperCAmelCase_    :      Union[str, Any]            =						re_prior_cond_proj_in.sub(__snake_case     ,       __snake_case  )

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

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

										UpperCAmelCase_    :      str            =						original_key
										UpperCAmelCase_    :      Tuple            =						value

					return new_dict




@torch.no_grad()
def      lowercase__      (     __snake_case    :							str=None     ,       __snake_case    :							Optional[int]=None  ):


					'''simple docstring'''


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

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

					UpperCAmelCase_    :      List[str]            =						JukeboxConfig.from_pretrained(__snake_case  )
					UpperCAmelCase_    :      Optional[int]            =						JukeboxModel(__snake_case  )

					UpperCAmelCase_    :      Any            =						[]
					UpperCAmelCase_    :      Any            =						{}
					for i, dict_name in enumerate(__snake_case  ):
										UpperCAmelCase_    :      Union[str, Any]            =						torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'  )[-1]}"  )['model']

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

										UpperCAmelCase_    :      List[Any]            =						'vqvae' if i == 0 else F"priors.{3 - i}"
										UpperCAmelCase_    :      str            =						fix_jukebox_keys(__snake_case     ,       model.state_dict()     ,       __snake_case     ,       __snake_case  )
										weight_dict.append(__snake_case  )

					UpperCAmelCase_    :      Optional[Any]            =						weight_dict.pop(0  )
					model.vqvae.load_state_dict(__snake_case  )
					for i in range(len(__snake_case  )  ):
										model.priors[i].load_state_dict(weight_dict[2 - i]  )

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

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

					return weight_dict


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














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


__UpperCAmelCase								=						logging.getLogger(__name__)




def      lowercase__      (     __snake_case    :							List[Any]=2     ,       __snake_case    :							Union[str, Any]=3     ,       __snake_case    :							Any=16     ,       __snake_case    :							int = 10     ,       __snake_case    :							int = 2  ):


					'''simple docstring'''


					def get_dataset(__snake_case    :							Optional[Any]  ):
										UpperCAmelCase_    :      Optional[Any]            =						torch.randn(batch_size * n_batches     ,       1  )
										return TensorDataset(__snake_case     ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches     ,       1  )  )

					UpperCAmelCase_    :      Any            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      str            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					return (train_dataloader, valid_dataloader)




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							str     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							List[str]     ,       __snake_case    :							Any     ,       __snake_case    :							Tuple=None  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						[]
					for epoch in range(__snake_case  ):
										# Train quickly
										model.train()
										for batch in dataloader:
															UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						batch
															UpperCAmelCase_    :      List[Any]            =						model(__snake_case  )
															UpperCAmelCase_    :      int            =						torch.nn.functional.mse_loss(__snake_case     ,       __snake_case  )
															accelerator.backward(__snake_case  )
															optimizer.step()
															optimizer.zero_grad()
										rands.append(random.random()  )  # Introduce some randomness
										if scheduler is not None:
															scheduler.step()
					return rands



class   lowerCamelCase      (nn.Module			):







		'''simple docstring'''



		def __init__(							self   )	->							Optional[Any]:
							super().__init__()
							UpperCAmelCase_    :      List[Any]            =						nn.Parameter(torch.randn(1   )   )
							UpperCAmelCase_    :      Optional[int]            =						nn.Parameter(torch.randn(1   )   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[Any]:
							return x * self.a + self.b





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[int]            =						ProjectConfiguration(total_limit=1				,		project_dir=_UpperCamelCase				,		automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Dict            =						Accelerator(project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# 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(4_2   )
												UpperCAmelCase_    :      Optional[Any]            =						DummyModel()
												UpperCAmelCase_    :      str            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												UpperCAmelCase_    :      Any            =						os.path.join(_UpperCamelCase				,		'initial'   )
												accelerator.save_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[int]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Dict            =						optimizer.state_dict()
												UpperCAmelCase_    :      Union[str, Any]            =						train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Union[str, Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Any            =						optimizer.state_dict()

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      int            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[Any]            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      List[str]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Dict            =						train(2				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save everything
												UpperCAmelCase_    :      Union[str, Any]            =						os.path.join(_UpperCamelCase				,		'checkpoint'   )
												accelerator.save_state(_UpperCamelCase   )

												# Load everything back in and make sure all states work
												accelerator.load_state(_UpperCamelCase   )
												test_rands += train(1				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Union[str, Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      Optional[int]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Any            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )

												# Train baseline
												UpperCAmelCase_    :      str            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[int]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[int]            =						optimizer.state_dict()
												UpperCAmelCase_    :      Optional[Any]            =						train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Tuple            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[int]            =						optimizer.state_dict()

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      Any            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(iteration=1				,		automatic_checkpoint_naming=_UpperCamelCase   )
												UpperCAmelCase_    :      List[Any]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      str            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      List[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Union[str, Any]            =						train(2				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save everything
												accelerator.save_state()

												# Load everything back in and make sure all states work
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_1'   )   )
												test_rands += train(1				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      List[Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Dict            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      Optional[Any]            =						torch.tensor([1, 2, 3]   )
							UpperCAmelCase_    :      Any            =						torch.tensor([2, 3, 4]   )
							UpperCAmelCase_    :      Union[str, Any]            =						DummyModel()
							UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(net.parameters()   )
							UpperCAmelCase_    :      Any            =						Accelerator()
							with self.assertRaises(_UpperCamelCase   ) as ve:
												accelerator.register_for_checkpointing(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						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   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_    :      Dict            =						torch.optim.lr_scheduler.StepLR(_UpperCamelCase				,		step_size=1				,		gamma=0.99   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()
												UpperCAmelCase_    :      Dict            =						scheduler.state_dict()
												train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												self.assertNotEqual(_UpperCamelCase				,		scheduler.state_dict()   )

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



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Optional[int]            =						DummyModel()
												UpperCAmelCase_    :      Dict            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase				,		total_limit=2   )
												# Train baseline
												UpperCAmelCase_    :      Optional[int]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_    :      str            =						accelerator.prepare(_UpperCamelCase   )
												# Save 3 states:
												for _ in range(1_1   ):
																	accelerator.save_state()
												self.assertTrue(not os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )   )
												self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_9'   )   )   )
												self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_10'   )   )   )



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


if __name__ == "__main__":
		__UpperCAmelCase								=						'/tmp/accelerate/state_checkpointing'
		__UpperCAmelCase								=						DummyModel()
		__UpperCAmelCase								=						torch.optim.Adam(params=model.parameters(), lr=1E-3)
		__UpperCAmelCase								=						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
		__UpperCAmelCase							,   __UpperCAmelCase								=						dummy_dataloaders()
		__UpperCAmelCase								=						ProjectConfiguration(automatic_checkpoint_naming=True)
		# Train baseline
		__UpperCAmelCase								=						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)
		__UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase								=						accelerator.prepare(
		    model, optimizer, train_dataloader, valid_dataloader, scheduler
		)
		__UpperCAmelCase							,   __UpperCAmelCase								=						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:
				__UpperCAmelCase								=						group['params'][0].device
				break
		assert param_device.type == accelerator.device.type
		__UpperCAmelCase								=						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:
				__UpperCAmelCase								=						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:
				__UpperCAmelCase								=						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()






