





import json
import os
import tempfile
from unittest.mock import patch

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

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	Dict						    =							torch.nn.Linear(2,       4	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.optim.AdamW(model.parameters(),       lr=1.0	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.optim.lr_scheduler.OneCycleLR(__lowerCamelCase,       max_lr=0.01,       steps_per_epoch=2,       epochs=1	)
     _SCREAMING_SNAKE_CASE       :	str						    =							DataLoader(TensorDataset(torch.tensor([1, 2, 3]	)	)	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							DataLoader(TensorDataset(torch.tensor([4, 5, 6]	)	)	)

     return model, optimizer, scheduler, train_dl, valid_dl






def     lowerCamelCase__			(__lowerCamelCase	):
     return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
     model.load_state_dict(__lowerCamelCase	)





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       @require_cuda
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							Accelerator()
            assert PartialState._shared_state["_cpu"] is False
            assert PartialState._shared_state["device"].type == "cuda"
            with self.assertRaises(__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							Accelerator(cpu=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							Accelerator()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							GradientState()
            assert state.num_steps == 1
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							4
            assert state.num_steps == 4

            assert state.sync_gradients is True
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							False
            assert state.sync_gradients is False
            GradientState._reset_state()




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							Accelerator()
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Tuple						    =							create_components()

            (
                (
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			
            )       :	int						    =							accelerator.prepare(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)

            self.assertTrue(prepared_model in accelerator._models							)
            self.assertTrue(prepared_optimizer in accelerator._optimizers							)
            self.assertTrue(prepared_scheduler in accelerator._schedulers							)
            self.assertTrue(prepared_train_dl in accelerator._dataloaders							)
            self.assertTrue(prepared_valid_dl in accelerator._dataloaders							)




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							Accelerator()
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Dict						    =							create_components()
            accelerator.prepare(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)
            accelerator.free_memory()

            self.assertTrue(len(accelerator._models							) == 0							)
            self.assertTrue(len(accelerator._optimizers							) == 0							)
            self.assertTrue(len(accelerator._schedulers							) == 0							)
            self.assertTrue(len(accelerator._dataloaders							) == 0							)




       def     UpperCamelCase_			(      self							)			->     Tuple:
            PartialState._reset_state()

            # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
            def noop(*__lowerCamelCase    ,						**__lowerCamelCase							):
                 pass

            with patch("torch.cuda.set_device"    ,						__lowerCamelCase							), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"							):
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							Accelerator()
                 self.assertEqual(str(accelerator.state.device							)    ,						"cuda:64"							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Accelerator()
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							create_components()
            accelerator.prepare(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							get_signature(__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmpdirname:
                 accelerator.save_state(__lowerCamelCase							)

                 # make sure random weights don't match
                 load_random_weights(__lowerCamelCase							)
                 self.assertTrue(abs(model_signature - get_signature(__lowerCamelCase							)							) > 1E-3							)

                 # make sure loaded weights match
                 accelerator.load_state(__lowerCamelCase							)
                 self.assertTrue(abs(model_signature - get_signature(__lowerCamelCase							)							) < 1E-3							)




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Accelerator()
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Any						    =							create_components()
            accelerator.prepare(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							get_signature(__lowerCamelCase							)

            # saving hook
            def save_config(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{"class_name": models[0].__class__.__name__}

                 with open(os.path.join(__lowerCamelCase    ,						"data.json"							)    ,						"w"							) as f:
                      json.dump(__lowerCamelCase    ,						__lowerCamelCase							)

        # loading hook
            def load_config(__lowerCamelCase    ,						__lowerCamelCase							):
                 with open(os.path.join(__lowerCamelCase    ,						"data.json"							)    ,						"r"							) as f:
                      _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							json.load(__lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE       :	str						    =							config["class_name"]

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							accelerator.register_save_state_pre_hook(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							accelerator.register_load_state_pre_hook(__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmpdirname:
                 accelerator.save_state(__lowerCamelCase							)

                 # make sure random weights don't match with hooks
                 load_random_weights(__lowerCamelCase							)
                 self.assertTrue(abs(model_signature - get_signature(__lowerCamelCase							)							) > 1E-3							)

                 # random class name to verify correct one is loaded
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"random"

                 # make sure loaded weights match with hooks
                 accelerator.load_state(__lowerCamelCase							)
                 self.assertTrue(abs(model_signature - get_signature(__lowerCamelCase							)							) < 1E-3							)

                 # mode.class_name is loaded from config
                 self.assertTrue(model.class_name == model.__class__.__name__							)

            # remove hooks
            save_hook.remove()
            load_hook.remove()

            with tempfile.TemporaryDirectory() as tmpdirname:
                 accelerator.save_state(__lowerCamelCase							)

                 # make sure random weights don't match with hooks removed
                 load_random_weights(__lowerCamelCase							)
                 self.assertTrue(abs(model_signature - get_signature(__lowerCamelCase							)							) > 1E-3							)

                 # random class name to verify correct one is loaded
                 _SCREAMING_SNAKE_CASE       :	str						    =							"random"

                 # make sure loaded weights match with hooks removed
                 accelerator.load_state(__lowerCamelCase							)
                 self.assertTrue(abs(model_signature - get_signature(__lowerCamelCase							)							) < 1E-3							)

                 # mode.class_name is NOT loaded from config
                 self.assertTrue(model.class_name != model.__class__.__name__							)




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							Accelerator()
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Dict						    =							create_components()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							None

            # This should work
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							accelerator.prepare(
                __lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)
            self.assertTrue(dummy_obj is None							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	int						    =							Accelerator()
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	str						    =							create_components()
            _SCREAMING_SNAKE_CASE       :	int						    =							[1, 2, 3]

            # This should work
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							accelerator.prepare(
                __lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)
            self.assertEqual(
                getattr(__lowerCamelCase    ,						"_is_accelerate_prepared"    ,						__lowerCamelCase							)    ,						__lowerCamelCase    ,						"Dummy object should have `_is_accelerate_prepared` set to `True`"    ,						)
            self.assertEqual(
                getattr(__lowerCamelCase    ,						"_is_accelerate_prepared"    ,						__lowerCamelCase							)    ,						__lowerCamelCase    ,						"Model is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
            self.assertEqual(
                getattr(__lowerCamelCase    ,						"_is_accelerate_prepared"    ,						__lowerCamelCase							)    ,						__lowerCamelCase    ,						"Optimizer is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
            self.assertEqual(
                getattr(__lowerCamelCase    ,						"_is_accelerate_prepared"    ,						__lowerCamelCase							)    ,						__lowerCamelCase    ,						"Scheduler is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
            self.assertEqual(
                getattr(__lowerCamelCase    ,						"_is_accelerate_prepared"    ,						__lowerCamelCase							)    ,						__lowerCamelCase    ,						"Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
            self.assertEqual(
                getattr(__lowerCamelCase    ,						"_is_accelerate_prepared"    ,						__lowerCamelCase							)    ,						__lowerCamelCase    ,						"Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,						)




       @slow
       @require_bnb
       def     UpperCamelCase_			(      self							)			->     Any:
            from transformers import AutoModelForCausalLM

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						load_in_abit=__lowerCamelCase    ,						device_map={"": 0}    ,						)
            _SCREAMING_SNAKE_CASE       :	str						    =							Accelerator()

            # This should work
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							accelerator.prepare(__lowerCamelCase							)




       @slow
       @require_bnb
       def     UpperCamelCase_			(      self							)			->     Tuple:
            from transformers import AutoModelForCausalLM

            _SCREAMING_SNAKE_CASE       :	Dict						    =							Accelerator()

            with init_empty_weights():
                 _SCREAMING_SNAKE_CASE       :	Any						    =							AutoModelForCausalLM.from_pretrained(
                     "EleutherAI/gpt-neo-125m"    ,						)
                 model.tie_weights()
                 _SCREAMING_SNAKE_CASE       :	int						    =							infer_auto_device_map(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"cpu"

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						device_map=__lowerCamelCase    ,						load_in_abit=__lowerCamelCase    ,						llm_inta_enable_fpaa_cpu_offload=__lowerCamelCase							)

            # This should not work and get value error
            with self.assertRaises(__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							accelerator.prepare(__lowerCamelCase							)




       @slow
       @require_bnb
       @require_multi_gpu
       def     UpperCamelCase_			(      self							)			->     int:
            from transformers import AutoModelForCausalLM

            _SCREAMING_SNAKE_CASE       :	Any						    =							{"distributed_type": DistributedType.MULTI_GPU}

            with init_empty_weights():
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							AutoModelForCausalLM.from_pretrained(
                     "EleutherAI/gpt-neo-125m"    ,						)
                 model.tie_weights()
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							infer_auto_device_map(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							1

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						load_in_abit=__lowerCamelCase    ,						device_map=__lowerCamelCase    ,						)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							Accelerator()

            # This should not work and get value error
            with self.assertRaises(__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							accelerator.prepare(__lowerCamelCase							)

            PartialState._reset_state()




       @slow
       @require_bnb
       @require_multi_gpu
       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            from transformers import AutoModelForCausalLM

            with init_empty_weights():
                 _SCREAMING_SNAKE_CASE       :	Any						    =							AutoModelForCausalLM.from_pretrained(
                     "EleutherAI/gpt-neo-125m"    ,						)
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							infer_auto_device_map(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							1

            _SCREAMING_SNAKE_CASE       :	Any						    =							AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						load_in_abit=__lowerCamelCase    ,						device_map=__lowerCamelCase    ,						)
            _SCREAMING_SNAKE_CASE       :	int						    =							Accelerator()

            # This should work
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							accelerator.prepare(__lowerCamelCase							)







       @require_cuda
       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.nn.Linear(1_0    ,						1_0							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							torch.optim.SGD(model.parameters()    ,						lr=0.01							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							Accelerator(cpu=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							accelerator.prepare(__lowerCamelCase							)






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    42





class 			lowerCAmelCase__( __lowercase  ,     __lowercase    ):






       '''simple docstring'''


       @register_to_config
       def __init__(      self    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = ("DownEncoderBlock2D",)    ,						__lowerCamelCase = ("UpDecoderBlock2D",)    ,						__lowerCamelCase = (6_4,)    ,						__lowerCamelCase = 1    ,						__lowerCamelCase = "silu"    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = 2_5_6    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0.1_8215    ,						__lowerCamelCase = "group"    ,						)			->     List[str]:
            super().__init__()

            # pass init params to Encoder
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Encoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						down_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						double_z=__lowerCamelCase    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							vq_embed_dim if vq_embed_dim is not None else latent_channels

            _SCREAMING_SNAKE_CASE       :	str						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							VectorQuantizer(__lowerCamelCase    ,						__lowerCamelCase    ,						beta=0.25    ,						remap=__lowerCamelCase    ,						sane_index_shape=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)

            # pass init params to Decoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							Decoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						up_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						norm_type=__lowerCamelCase    ,						)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     VQEncoderOutput:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.encoder(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.quant_conv(__lowerCamelCase							)

            if not return_dict:
                 return (h,)

            return VQEncoderOutput(latents=__lowerCamelCase							)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = False    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.quantize(__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							h
            _SCREAMING_SNAKE_CASE       :	str						    =							self.post_quant_conv(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.decoder(__lowerCamelCase    ,						quant if self.config.norm_type == "spatial" else None							)

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							sample
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.encode(__lowerCamelCase							).latents
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.decode(__lowerCamelCase							).sample

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)