'''simple docstring'''




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 				snake_case__							(			)    ->	Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  torch.nn.Linear(2      ,   4 )
		lowerCAmelCase   					=  torch.optim.AdamW(model.parameters()      ,   lr=1.0 )
		lowerCAmelCase   					=  torch.optim.lr_scheduler.OneCycleLR(_A      ,   max_lr=0.01      ,   steps_per_epoch=2      ,   epochs=1 )
		lowerCAmelCase   					=  DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
		lowerCAmelCase   					=  DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )

		return model, optimizer, scheduler, train_dl, valid_dl






def 				snake_case__							(			_A:					Any )    ->	Any:

		'''simple docstring'''


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






def 				snake_case__							(			_A:					str )    ->	Any:

		'''simple docstring'''


		lowerCAmelCase   					=  torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
		model.load_state_dict(_A )






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			@require_cuda
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Accelerator()
					assert PartialState._shared_state["_cpu"] is False
					assert PartialState._shared_state["device"].type == "cuda"
					with self.assertRaises(__lowerCAmelCase):
							lowerCAmelCase   					=  Accelerator(cpu=__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Accelerator()
					lowerCAmelCase   					=  GradientState()
					assert state.num_steps == 1
					lowerCAmelCase   					=  4
					assert state.num_steps == 4

					assert state.sync_gradients is True
					lowerCAmelCase   					=  False
					assert state.sync_gradients is False
					GradientState._reset_state()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Accelerator()
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  create_components()

					(
					    (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     
					)   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Accelerator()
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					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"""):
							lowerCAmelCase   					=  Accelerator()
							self.assertEqual(str(accelerator.state.device)       ,    """cuda:64""")



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Accelerator()
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  create_components()
					accelerator.prepare(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase)

					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Accelerator()
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  create_components()
					accelerator.prepare(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase)

					lowerCAmelCase   					=  get_signature(__lowerCAmelCase)

					# saving hook
					def save_config(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  {"""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:
									lowerCAmelCase   					=  json.load(__lowerCAmelCase)

							lowerCAmelCase   					=  config["""class_name"""]

					lowerCAmelCase   					=  accelerator.register_save_state_pre_hook(__lowerCAmelCase)
					lowerCAmelCase   					=  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
							lowerCAmelCase   					=  """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
							lowerCAmelCase   					=  """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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Accelerator()
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  create_components()
					lowerCAmelCase   					=  None

					# This should work
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  accelerator.prepare(
					    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase)
					self.assertTrue(dummy_obj is None)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Accelerator()
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  create_components()
					lowerCAmelCase   					=  [1, 2, 3]

					# This should work
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					from transformers import AutoModelForCausalLM

					lowerCAmelCase   					=  AutoModelForCausalLM.from_pretrained(
					    """EleutherAI/gpt-neo-125m"""       ,    load_in_abit=__lowerCAmelCase       ,    device_map={"""""": 0}       ,    )
					lowerCAmelCase   					=  Accelerator()

					# This should work
					lowerCAmelCase   					=  accelerator.prepare(__lowerCAmelCase)



			@slow
			@require_bnb
			def 							a_       (   self):




					"""simple docstring"""


					from transformers import AutoModelForCausalLM

					lowerCAmelCase   					=  Accelerator()

					with init_empty_weights():
							lowerCAmelCase   					=  AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""       ,    )
							model.tie_weights()
							lowerCAmelCase   					=  infer_auto_device_map(__lowerCAmelCase)
							lowerCAmelCase   					=  """cpu"""

					lowerCAmelCase   					=  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):
							lowerCAmelCase   					=  accelerator.prepare(__lowerCAmelCase)



			@slow
			@require_bnb
			@require_multi_gpu
			def 							a_       (   self):




					"""simple docstring"""


					from transformers import AutoModelForCausalLM

					lowerCAmelCase   					=  {"""distributed_type""": DistributedType.MULTI_GPU}

					with init_empty_weights():
							lowerCAmelCase   					=  AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""       ,    )
							model.tie_weights()
							lowerCAmelCase   					=  infer_auto_device_map(__lowerCAmelCase)
							lowerCAmelCase   					=  1

					lowerCAmelCase   					=  AutoModelForCausalLM.from_pretrained(
					    """EleutherAI/gpt-neo-125m"""       ,    load_in_abit=__lowerCAmelCase       ,    device_map=__lowerCAmelCase       ,    )
					lowerCAmelCase   					=  Accelerator()

					# This should not work and get value error
					with self.assertRaises(__lowerCAmelCase):
							lowerCAmelCase   					=  accelerator.prepare(__lowerCAmelCase)

					PartialState._reset_state()



			@slow
			@require_bnb
			@require_multi_gpu
			def 							a_       (   self):




					"""simple docstring"""


					from transformers import AutoModelForCausalLM

					with init_empty_weights():
							lowerCAmelCase   					=  AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""       ,    )
							lowerCAmelCase   					=  infer_auto_device_map(__lowerCAmelCase)
							lowerCAmelCase   					=  1

					lowerCAmelCase   					=  AutoModelForCausalLM.from_pretrained(
					    """EleutherAI/gpt-neo-125m"""       ,    load_in_abit=__lowerCAmelCase       ,    device_map=__lowerCAmelCase       ,    )
					lowerCAmelCase   					=  Accelerator()

					# This should work
					lowerCAmelCase   					=  accelerator.prepare(__lowerCAmelCase)







			@require_cuda
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  torch.nn.Linear(10       ,    10)
					lowerCAmelCase   					=  torch.optim.SGD(model.parameters()       ,    lr=0.01)
					lowerCAmelCase   					=  Accelerator(cpu=__lowerCAmelCase)
					lowerCAmelCase   					=  accelerator.prepare(__lowerCAmelCase)





'''simple docstring'''




import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=18       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  size if size is not None else {"""height""": 18, """width""": 18}
					lowerCAmelCase   					=  parent
					lowerCAmelCase   					=  batch_size
					lowerCAmelCase   					=  num_channels
					lowerCAmelCase   					=  image_size
					lowerCAmelCase   					=  min_resolution
					lowerCAmelCase   					=  max_resolution
					lowerCAmelCase   					=  do_resize
					lowerCAmelCase   					=  size
					lowerCAmelCase   					=  do_normalize
					lowerCAmelCase   					=  image_mean
					lowerCAmelCase   					=  image_std







			def 							a_       (   self):




					"""simple docstring"""


					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					}







@require_torch
@require_vision
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       DPTImageProcessor if is_vision_available() else None



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  DPTImageProcessingTester(self)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """image_mean"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """image_std"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """do_normalize"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """do_resize"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """size"""))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size       ,    {"""height""": 18, """width""": 18})

					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42)
					self.assertEqual(image_processor.size       ,    {"""height""": 42, """width""": 42})



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    Image.Image)

					# Test not batched input
					lowerCAmelCase   					=  image_processing(image_inputs[0]       ,    return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape       ,    (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape       ,    (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase       ,    numpify=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    np.ndarray)

					# Test not batched input
					lowerCAmelCase   					=  image_processing(image_inputs[0]       ,    return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape       ,    (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape       ,    (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase       ,    torchify=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    torch.Tensor)

					# Test not batched input
					lowerCAmelCase   					=  image_processing(image_inputs[0]       ,    return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape       ,    (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape       ,    (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )




