import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Optional[Any]      =				(DDIMParallelScheduler,)
    a__     :						List[Any]      =				(("""eta""", 0.0), ("""num_inference_steps""", 5_0))







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Dict:
        __UpperCamelCase					:List[Any]        =		{
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.00_01,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowercase)
        return config







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     int:
        __UpperCamelCase					:Tuple        =		self.scheduler_classes[0]
        __UpperCamelCase					:Optional[int]        =		self.get_scheduler_config(**__lowercase)
        __UpperCamelCase					:int        =		scheduler_class(**__lowercase)

        __UpperCamelCase					:List[Any]        =		10, 0.0

        __UpperCamelCase					:List[str]        =		self.dummy_model()
        __UpperCamelCase					:Union[str, Any]        =		self.dummy_sample_deter

        scheduler.set_timesteps(__lowercase)

        for t in scheduler.timesteps:
            __UpperCamelCase					:Optional[int]        =		model(__lowercase			,			__lowercase)
            __UpperCamelCase					:Any        =		scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase).prev_sample

        return sample







    def       UpperCamelCase__       (     self)	->     int:
        for timesteps in [100, 500, 1_000]:
            self.check_over_configs(num_train_timesteps=__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=__lowercase)

        __UpperCamelCase					:Optional[int]        =		self.scheduler_classes[0]
        __UpperCamelCase					:Any        =		self.get_scheduler_config(steps_offset=1)
        __UpperCamelCase					:Union[str, Any]        =		scheduler_class(**__lowercase)
        scheduler.set_timesteps(5)
        assert torch.equal(scheduler.timesteps			,			torch.LongTensor([801, 601, 401, 201, 1]))







    def       UpperCamelCase__       (     self)	->     List[Any]:
        for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,			[0.0_02, 0.02, 0.2, 2]):
            self.check_over_configs(beta_start=__lowercase			,			beta_end=__lowercase)







    def       UpperCamelCase__       (     self)	->     List[Any]:
        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=__lowercase)







    def       UpperCamelCase__       (     self)	->     Dict:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__lowercase)







    def       UpperCamelCase__       (     self)	->     int:
        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=__lowercase)







    def       UpperCamelCase__       (     self)	->     Any:
        for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=__lowercase)







    def       UpperCamelCase__       (     self)	->     Any:
        for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        self.check_over_configs(thresholding=__lowercase)
        for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=__lowercase			,			prediction_type=__lowercase			,			sample_max_value=__lowercase			,			)







    def       UpperCamelCase__       (     self)	->     Tuple:
        for t in [1, 10, 49]:
            self.check_over_forward(time_step=__lowercase)







    def       UpperCamelCase__       (     self)	->     str:
        for t, num_inference_steps in zip([1, 10, 50]			,			[10, 50, 500]):
            self.check_over_forward(time_step=__lowercase			,			num_inference_steps=__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        for t, eta in zip([1, 10, 49]			,			[0.0, 0.5, 1.0]):
            self.check_over_forward(time_step=__lowercase			,			eta=__lowercase)







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:List[str]        =		self.scheduler_classes[0]
        __UpperCamelCase					:Tuple        =		self.get_scheduler_config()
        __UpperCamelCase					:str        =		scheduler_class(**__lowercase)

        assert torch.sum(torch.abs(scheduler._get_variance(0			,			0) - 0.0)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420			,			400) - 0.1_47_71)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980			,			960) - 0.3_24_60)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0			,			0) - 0.0)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487			,			486) - 0.0_09_79)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999			,			998) - 0.02)) < 1E-5







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:List[Any]        =		self.scheduler_classes[0]
        __UpperCamelCase					:Tuple        =		self.get_scheduler_config()
        __UpperCamelCase					:List[Any]        =		scheduler_class(**__lowercase)

        __UpperCamelCase					:Union[str, Any]        =		10, 0.0
        scheduler.set_timesteps(__lowercase)

        __UpperCamelCase					:List[str]        =		self.dummy_model()
        __UpperCamelCase					:List[str]        =		self.dummy_sample_deter
        __UpperCamelCase					:int        =		self.dummy_sample_deter + 0.1
        __UpperCamelCase					:Any        =		self.dummy_sample_deter - 0.1

        __UpperCamelCase					:Tuple        =		samplea.shape[0]
        __UpperCamelCase					:List[str]        =		torch.stack([samplea, samplea, samplea]			,			dim=0)
        __UpperCamelCase					:Dict        =		torch.arange(__lowercase)[0:3, None].repeat(1			,			__lowercase)

        __UpperCamelCase					:Optional[Any]        =		model(samples.flatten(0			,			1)			,			timesteps.flatten(0			,			1))
        __UpperCamelCase					:Union[str, Any]        =		scheduler.batch_step_no_noise(__lowercase			,			timesteps.flatten(0			,			1)			,			samples.flatten(0			,			1)			,			__lowercase)

        __UpperCamelCase					:Optional[Any]        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:Optional[Any]        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 1_147.7_904) < 1E-2
        assert abs(result_mean.item() - 0.49_82) < 1E-3







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		self.full_loop()

        __UpperCamelCase					:Union[str, Any]        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:int        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 172.0_067) < 1E-2
        assert abs(result_mean.item() - 0.22_39_67) < 1E-3







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:str        =		self.full_loop(prediction_type='''v_prediction''')

        __UpperCamelCase					:Dict        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:str        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 52.53_02) < 1E-2
        assert abs(result_mean.item() - 0.06_84) < 1E-3







    def       UpperCamelCase__       (     self)	->     Tuple:
        # We specify different beta, so that the first alpha is 0.99
        __UpperCamelCase					:List[Any]        =		self.full_loop(set_alpha_to_one=__lowercase			,			beta_start=0.01)
        __UpperCamelCase					:Any        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:List[str]        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 149.8_295) < 1E-2
        assert abs(result_mean.item() - 0.19_51) < 1E-3







    def       UpperCamelCase__       (     self)	->     Dict:
        # We specify different beta, so that the first alpha is 0.99
        __UpperCamelCase					:Optional[int]        =		self.full_loop(set_alpha_to_one=__lowercase			,			beta_start=0.01)
        __UpperCamelCase					:Union[str, Any]        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:Any        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 149.0_784) < 1E-2
        assert abs(result_mean.item() - 0.19_41) < 1E-3




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  (       ):
    '''simple docstring'''







    __UpperCamelCase					:List[str]        =		torch.nn.Linear(2  ,      4     )
    __UpperCamelCase					:Any        =		torch.optim.AdamW(model.parameters()  ,      lr=1.0     )
    __UpperCamelCase					:List[Any]        =		torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE  ,      max_lr=0.01  ,      steps_per_epoch=2  ,      epochs=1     )
    __UpperCamelCase					:List[Any]        =		DataLoader(TensorDataset(torch.tensor([1, 2, 3]     )     )     )
    __UpperCamelCase					:Dict        =		DataLoader(TensorDataset(torch.tensor([4, 5, 6]     )     )     )

    return model, optimizer, scheduler, train_dl, valid_dl



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Union[str, Any]        =		torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
    model.load_state_dict(SCREAMING_SNAKE_CASE     )


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @require_cuda
    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Dict        =		Accelerator()
        assert PartialState._shared_state["_cpu"] is False
        assert PartialState._shared_state["device"].type == "cuda"
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:Any        =		Accelerator(cpu=__lowercase)







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:List[Any]        =		Accelerator()
        __UpperCamelCase					:List[Any]        =		GradientState()
        assert state.num_steps == 1
        __UpperCamelCase					:Any        =		4
        assert state.num_steps == 4

        assert state.sync_gradients is True
        __UpperCamelCase					:int        =		False
        assert state.sync_gradients is False
        GradientState._reset_state()







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Tuple        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		create_components()

        (
            (
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	
        )					:int        =		accelerator.prepare(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)

        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)	->     Optional[Any]:
        __UpperCamelCase					:str        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		create_components()
        accelerator.prepare(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)
        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)	->     Union[str, Any]:
        PartialState._reset_state()

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

        with patch('''torch.cuda.set_device'''			,			__lowercase), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''):
            __UpperCamelCase					:Optional[Any]        =		Accelerator()
            self.assertEqual(str(accelerator.state.device)			,			'''cuda:64''')







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		create_components()
        accelerator.prepare(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)

        __UpperCamelCase					:Tuple        =		get_signature(__lowercase)

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

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

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







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:List[Any]        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Any        =		create_components()
        accelerator.prepare(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)

        __UpperCamelCase					:Any        =		get_signature(__lowercase)

        # saving hook
        def save_config(__lowercase			,			__lowercase			,			__lowercase):
            __UpperCamelCase					:Union[str, Any]        =		{'''class_name''': models[0].__class__.__name__}

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

        # loading hook
        def load_config(__lowercase			,			__lowercase):
            with open(os.path.join(__lowercase			,			'''data.json''')			,			'''r''') as f:
                __UpperCamelCase					:Dict        =		json.load(__lowercase)

            __UpperCamelCase					:Dict        =		config['''class_name''']

        __UpperCamelCase					:Union[str, Any]        =		accelerator.register_save_state_pre_hook(__lowercase)
        __UpperCamelCase					:Any        =		accelerator.register_load_state_pre_hook(__lowercase)

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

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

            # random class name to verify correct one is loaded
            __UpperCamelCase					:int        =		'''random'''

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

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

            # random class name to verify correct one is loaded
            __UpperCamelCase					:Dict        =		'''random'''

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

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







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Optional[Any]        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		create_components()
        __UpperCamelCase					:Optional[Any]        =		None

        # This should work
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		accelerator.prepare(
            __lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)
        self.assertTrue(dummy_obj is None)







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:List[str]        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Any        =		create_components()
        __UpperCamelCase					:Dict        =		[1, 2, 3]

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







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

        __UpperCamelCase					:Optional[int]        =		AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''			,			load_in_abit=__lowercase			,			device_map={'''''': 0}			,			)
        __UpperCamelCase					:Optional[Any]        =		Accelerator()

        # This should work
        __UpperCamelCase					:int        =		accelerator.prepare(__lowercase)







    @slow
    @require_bnb
    def       UpperCamelCase__       (     self)	->     List[str]:
        from transformers import AutoModelForCausalLM

        __UpperCamelCase					:str        =		Accelerator()

        with init_empty_weights():
            __UpperCamelCase					:List[Any]        =		AutoModelForCausalLM.from_pretrained(
                '''EleutherAI/gpt-neo-125m'''			,			)
            model.tie_weights()
            __UpperCamelCase					:List[str]        =		infer_auto_device_map(__lowercase)
            __UpperCamelCase					:str        =		'''cpu'''

        __UpperCamelCase					:List[Any]        =		AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''			,			device_map=__lowercase			,			load_in_abit=__lowercase			,			llm_inta_enable_fpaa_cpu_offload=__lowercase)

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







    @slow
    @require_bnb
    @require_multi_gpu
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        from transformers import AutoModelForCausalLM

        __UpperCamelCase					:int        =		{'''distributed_type''': DistributedType.MULTI_GPU}

        with init_empty_weights():
            __UpperCamelCase					:Tuple        =		AutoModelForCausalLM.from_pretrained(
                '''EleutherAI/gpt-neo-125m'''			,			)
            model.tie_weights()
            __UpperCamelCase					:int        =		infer_auto_device_map(__lowercase)
            __UpperCamelCase					:List[Any]        =		1

        __UpperCamelCase					:int        =		AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''			,			load_in_abit=__lowercase			,			device_map=__lowercase			,			)
        __UpperCamelCase					:Dict        =		Accelerator()

        # This should not work and get value error
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:Any        =		accelerator.prepare(__lowercase)

        PartialState._reset_state()







    @slow
    @require_bnb
    @require_multi_gpu
    def       UpperCamelCase__       (     self)	->     Dict:
        from transformers import AutoModelForCausalLM

        with init_empty_weights():
            __UpperCamelCase					:Optional[int]        =		AutoModelForCausalLM.from_pretrained(
                '''EleutherAI/gpt-neo-125m'''			,			)
            __UpperCamelCase					:List[str]        =		infer_auto_device_map(__lowercase)
            __UpperCamelCase					:Optional[int]        =		1

        __UpperCamelCase					:Optional[int]        =		AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''			,			load_in_abit=__lowercase			,			device_map=__lowercase			,			)
        __UpperCamelCase					:int        =		Accelerator()

        # This should work
        __UpperCamelCase					:int        =		accelerator.prepare(__lowercase)







    @require_cuda
    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:Tuple        =		torch.nn.Linear(10			,			10)
        __UpperCamelCase					:Optional[Any]        =		torch.optim.SGD(model.parameters()			,			lr=0.01)
        __UpperCamelCase					:Any        =		Accelerator(cpu=__lowercase)
        __UpperCamelCase					:Tuple        =		accelerator.prepare(__lowercase)





