




"""simple docstring"""






import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
UpperCamelCase__						=			logging.get_logger('''transformers.models.encodec''')

UpperCamelCase__						=			{
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
UpperCamelCase__						=			{
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
UpperCamelCase__						=			{
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
UpperCamelCase__						=			{
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
UpperCamelCase__						=			{
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
UpperCamelCase__						=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
UpperCamelCase__						=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
UpperCamelCase__						=			[]
UpperCamelCase__						=			[]





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					Tuple      ,       snake_case			:					Optional[Any]      ,       snake_case			:					List[str]      ,       snake_case			:					int    ):
					for attribute in key.split('''.'''    ):
										_lowerCAmelCase:str									=  getattr(snake_case      ,       snake_case    )

					if weight_type is not None:
										_lowerCAmelCase:str									=  getattr(snake_case      ,       snake_case    ).shape
					else:
										_lowerCAmelCase:Any									=  hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
										    F' {value.shape} for {full_name}'    )

					if weight_type == "weight":
										_lowerCAmelCase:Tuple									=  value
					elif weight_type == "weight_g":
										_lowerCAmelCase:int									=  value
					elif weight_type == "weight_v":
										_lowerCAmelCase:Optional[int]									=  value
					elif weight_type == "bias":
										_lowerCAmelCase:Optional[Any]									=  value
					elif weight_type == "running_mean":
										_lowerCAmelCase:Optional[Any]									=  value
					elif weight_type == "running_var":
										_lowerCAmelCase:List[str]									=  value
					elif weight_type == "num_batches_tracked":
										_lowerCAmelCase:Optional[int]									=  value
					elif weight_type == "weight_ih_l0":
										_lowerCAmelCase:Optional[int]									=  value
					elif weight_type == "weight_hh_l0":
										_lowerCAmelCase:Optional[Any]									=  value
					elif weight_type == "bias_ih_l0":
										_lowerCAmelCase:str									=  value
					elif weight_type == "bias_hh_l0":
										_lowerCAmelCase:Optional[Any]									=  value
					elif weight_type == "weight_ih_l1":
										_lowerCAmelCase:List[Any]									=  value
					elif weight_type == "weight_hh_l1":
										_lowerCAmelCase:int									=  value
					elif weight_type == "bias_ih_l1":
										_lowerCAmelCase:List[str]									=  value
					elif weight_type == "bias_hh_l1":
										_lowerCAmelCase:Dict									=  value
					else:
										_lowerCAmelCase:Optional[Any]									=  value

					logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'    )





def 							UpperCAmelCase		(		snake_case			:					Optional[Any]      ,       snake_case			:					Optional[int]    ):
					for key in ignore_keys:
										if key.endswith('''.*'''    ):
															if name.startswith(key[:-1]    ):
																				return True
										elif ".*." in key:
															_lowerCAmelCase:Any									=  key.split('''.*.'''    )
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False





def 							UpperCAmelCase		(		snake_case			:					Optional[Any]      ,       snake_case			:					Any      ,       snake_case			:					Tuple    ):
					_lowerCAmelCase:str									=  []

					if model_name == "encodec_24khz" or "encodec_32khz":
										_lowerCAmelCase:Dict									=  MAPPING_24K
					elif model_name == "encodec_48khz":
										_lowerCAmelCase:Optional[Any]									=  MAPPING_48K
					else:
										raise ValueError(F'Unsupported model: {model_name}'    )

					for name, value in orig_dict.items():
										if should_ignore(snake_case      ,       snake_case    ):
															logger.info(F'{name} was ignored'    )
															continue

										_lowerCAmelCase:List[str]									=  False
										for key, mapped_key in MAPPING.items():
															if "*" in key:
																				_lowerCAmelCase:List[Any]									=  key.split('''.*.'''    )
																				if prefix in name and suffix in name:
																									_lowerCAmelCase:Dict									=  suffix

															if key in name:
																				# HACK otherwise .embed gets initialized with .embed_avg too
																				if key.endswith('''embed'''    ) and name.endswith('''embed_avg'''    ):
																									continue

																				_lowerCAmelCase:Union[str, Any]									=  True
																				if "*" in mapped_key:
																									_lowerCAmelCase:str									=  name.split(snake_case    )[0].split('''.'''    )[-2]
																									_lowerCAmelCase:int									=  mapped_key.replace('''*'''      ,       snake_case    )
																				if "weight_g" in name:
																									_lowerCAmelCase:str									=  '''weight_g'''
																				elif "weight_v" in name:
																									_lowerCAmelCase:Any									=  '''weight_v'''
																				elif "weight_ih_l0" in name:
																									_lowerCAmelCase:Dict									=  '''weight_ih_l0'''
																				elif "weight_hh_l0" in name:
																									_lowerCAmelCase:Dict									=  '''weight_hh_l0'''
																				elif "bias_ih_l0" in name:
																									_lowerCAmelCase:Any									=  '''bias_ih_l0'''
																				elif "bias_hh_l0" in name:
																									_lowerCAmelCase:Union[str, Any]									=  '''bias_hh_l0'''
																				elif "weight_ih_l1" in name:
																									_lowerCAmelCase:Optional[Any]									=  '''weight_ih_l1'''
																				elif "weight_hh_l1" in name:
																									_lowerCAmelCase:Dict									=  '''weight_hh_l1'''
																				elif "bias_ih_l1" in name:
																									_lowerCAmelCase:List[str]									=  '''bias_ih_l1'''
																				elif "bias_hh_l1" in name:
																									_lowerCAmelCase:Any									=  '''bias_hh_l1'''
																				elif "bias" in name:
																									_lowerCAmelCase:Any									=  '''bias'''
																				elif "weight" in name:
																									_lowerCAmelCase:int									=  '''weight'''
																				elif "running_mean" in name:
																									_lowerCAmelCase:Any									=  '''running_mean'''
																				elif "running_var" in name:
																									_lowerCAmelCase:str									=  '''running_var'''
																				elif "num_batches_tracked" in name:
																									_lowerCAmelCase:Dict									=  '''num_batches_tracked'''
																				else:
																									_lowerCAmelCase:int									=  None
																				set_recursively(snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case    )
															continue
										if not is_used:
															unused_weights.append(snake_case    )

					logger.warning(F'Unused weights: {unused_weights}'    )





@torch.no_grad()
def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					List[Any]      ,       snake_case			:					Optional[Any]      ,       snake_case			:					Tuple=None      ,       snake_case			:					str=None      ,       ):
					if config_path is not None:
										_lowerCAmelCase:Union[str, Any]									=  EncodecConfig.from_pretrained(snake_case    )
					else:
										_lowerCAmelCase:str									=  EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										_lowerCAmelCase:Optional[Any]									=  [8, 5, 4, 4]
										_lowerCAmelCase:int									=  [2.2]
										_lowerCAmelCase:Any									=  64
										_lowerCAmelCase:Tuple									=  32000
										_lowerCAmelCase:Tuple									=  2048
										_lowerCAmelCase:int									=  False
										_lowerCAmelCase:Any									=  False
										_lowerCAmelCase:Tuple									=  False
					elif model_name == "encodec_48khz":
										_lowerCAmelCase:Union[str, Any]									=  [8, 5, 4, 2]
										_lowerCAmelCase:Optional[Any]									=  [3.0, 6.0, 12.0, 24.0]
										_lowerCAmelCase:Dict									=  48000
										_lowerCAmelCase:List[str]									=  2
										_lowerCAmelCase:Dict									=  False
										_lowerCAmelCase:Optional[Any]									=  '''time_group_norm'''
										_lowerCAmelCase:Dict									=  True
										_lowerCAmelCase:Tuple									=  1.0
										_lowerCAmelCase:Dict									=  0.01
					else:
										raise ValueError(F'Unknown model name: {model_name}'    )

					_lowerCAmelCase:Optional[Any]									=  EncodecModel(snake_case    )

					_lowerCAmelCase:List[str]									=  EncodecFeatureExtractor(
					    feature_size=config.audio_channels      ,       sampling_rate=config.sampling_rate      ,       chunk_length_s=config.chunk_length_s      ,       overlap=config.overlap      ,       )
					feature_extractor.save_pretrained(snake_case    )

					_lowerCAmelCase:int									=  torch.load(snake_case    )
					if "best_state" in original_checkpoint:
										# we might have a training state saved, in which case discard the yaml results and just retain the weights
										_lowerCAmelCase:List[str]									=  original_checkpoint['''best_state''']
					recursively_load_weights(snake_case      ,       snake_case      ,       snake_case    )
					model.save_pretrained(snake_case    )

					if repo_id:
										print('''Pushing to the hub...'''    )
										feature_extractor.push_to_hub(snake_case    )
										model.push_to_hub(snake_case    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				parser.add_argument(
				    '''--model''',
				    default='''encodec_24khz''',
				    type=str,
				    help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
				)
				parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
				)

				UpperCamelCase__						=			parser.parse_args()
				convert_checkpoint(
				    args.model,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)












"""simple docstring"""






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 							UpperCAmelCase		(		snake_case			:					List[Any]=2      ,       snake_case			:					int=3      ,       snake_case			:					Optional[int]=16      ,       snake_case			:					int = 10      ,       snake_case			:					int = 2    ):

					def get_dataset(snake_case			:					Any    ):
										_lowerCAmelCase:Union[str, Any]									=  torch.randn(batch_size * n_batches      ,       1    )
										return TensorDataset(snake_case      ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches      ,       1    )    )

					_lowerCAmelCase:Tuple									=  get_dataset(snake_case    )
					_lowerCAmelCase:List[Any]									=  get_dataset(snake_case    )
					_lowerCAmelCase:Dict									=  DataLoader(snake_case      ,       shuffle=snake_case      ,       batch_size=snake_case      ,       num_workers=4    )
					_lowerCAmelCase:Any									=  DataLoader(snake_case      ,       shuffle=snake_case      ,       batch_size=snake_case      ,       num_workers=4    )
					return (train_dataloader, valid_dataloader)





def 							UpperCAmelCase		(		snake_case			:					List[Any]      ,       snake_case			:					int      ,       snake_case			:					Dict      ,       snake_case			:					Dict      ,       snake_case			:					int      ,       snake_case			:					Tuple=None    ):
					_lowerCAmelCase:Optional[Any]									=  []
					for epoch in range(snake_case    ):
										# Train quickly
										model.train()
										for batch in dataloader:
															_lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  batch
															_lowerCAmelCase:int									=  model(snake_case    )
															_lowerCAmelCase:Optional[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   a__			(       nn.Module  ):





						def __init__(							self      :	List[Any])						->       Any:




											"""simple docstring"""







											super().__init__()
											_lowerCAmelCase:str									=  nn.Parameter(torch.randn(1))
											_lowerCAmelCase:Dict									=  nn.Parameter(torch.randn(1))







						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	List[Any])						->       Tuple:




											"""simple docstring"""







											return x * self.a + self.b




class   a__			(       unittest.TestCase  ):





						def       __UpperCamelCase     (							self      :	Any)						->       Optional[Any]:




											"""simple docstring"""







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

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





						def       __UpperCamelCase     (							self      :	Any)						->       Union[str, Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:int									=  DummyModel()
																_lowerCAmelCase:Dict									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  dummy_dataloaders()
																# Train baseline
																_lowerCAmelCase:List[str]									=  Accelerator()
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																# Save initial
																_lowerCAmelCase:Union[str, Any]									=  os.path.join(a__     ,'''initial''')
																accelerator.save_state(a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Any									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Optional[int]									=  optimizer.state_dict()
																_lowerCAmelCase:Tuple									=  train(3     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Optional[int]									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Any									=  optimizer.state_dict()

																# Train partially
																set_seed(42)
																_lowerCAmelCase:str									=  DummyModel()
																_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Any									=  dummy_dataloaders()
																_lowerCAmelCase:Union[str, Any]									=  Accelerator()
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Tuple									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																accelerator.load_state(a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):str									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Any									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)

																_lowerCAmelCase:str									=  train(2     ,a__     ,a__     ,a__     ,a__)
																# Save everything
																_lowerCAmelCase:Optional[Any]									=  os.path.join(a__     ,'''checkpoint''')
																accelerator.save_state(a__)

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





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Optional[Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:List[str]									=  DummyModel()
																_lowerCAmelCase:int									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Tuple									=  dummy_dataloaders()
																_lowerCAmelCase:Any									=  ProjectConfiguration(automatic_checkpoint_naming=a__)

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

																# Train partially
																set_seed(42)
																_lowerCAmelCase:Optional[int]									=  DummyModel()
																_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  dummy_dataloaders()
																_lowerCAmelCase:Union[str, Any]									=  ProjectConfiguration(iteration=1     ,automatic_checkpoint_naming=a__)
																_lowerCAmelCase:Dict									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Optional[int]									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__)
																accelerator.load_state(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_0'''))
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Dict									=  model.a.item(), model.b.item()
																_lowerCAmelCase:int									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)

																_lowerCAmelCase:Dict									=  train(2     ,a__     ,a__     ,a__     ,a__)
																# Save everything
																accelerator.save_state()

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





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.tensor([1, 2, 3])
											_lowerCAmelCase:Tuple									=  torch.tensor([2, 3, 4])
											_lowerCAmelCase:Tuple									=  DummyModel()
											_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(net.parameters())
											_lowerCAmelCase:Union[str, Any]									=  Accelerator()
											with self.assertRaises(a__) as ve:
																accelerator.register_for_checkpointing(a__     ,a__     ,a__     ,a__)
											_lowerCAmelCase:List[Any]									=  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      :	str)						->       Optional[Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:int									=  DummyModel()
																_lowerCAmelCase:str									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase:Union[str, Any]									=  torch.optim.lr_scheduler.StepLR(a__     ,step_size=1     ,gamma=0.99)
																_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  dummy_dataloaders()
																_lowerCAmelCase:Any									=  ProjectConfiguration(automatic_checkpoint_naming=a__)
																# Train baseline
																_lowerCAmelCase:Optional[int]									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:int									=  accelerator.prepare(
																    a__     ,a__     ,a__     ,a__     ,a__)
																# Save initial
																accelerator.save_state()
																_lowerCAmelCase:Dict									=  scheduler.state_dict()
																train(3     ,a__     ,a__     ,a__     ,a__     ,a__)
																self.assertNotEqual(a__     ,scheduler.state_dict())

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





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:str									=  DummyModel()
																_lowerCAmelCase:Optional[Any]									=  ProjectConfiguration(automatic_checkpoint_naming=a__     ,total_limit=2)
																# Train baseline
																_lowerCAmelCase:List[str]									=  Accelerator(project_dir=a__     ,project_config=a__)
																_lowerCAmelCase:List[str]									=  accelerator.prepare(a__)
																# Save 3 states:
																for _ in range(11):
																					accelerator.save_state()
																self.assertTrue(not os.path.exists(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_0''')))
																self.assertTrue(os.path.exists(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_9''')))
																self.assertTrue(os.path.exists(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_10''')))







						@require_cuda
						def       __UpperCamelCase     (							self      :	int)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
											execute_subprocess_async(a__     ,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=0.99)
				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()






