



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 				A				(					):





							'''simple docstring'''




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

							return model, optimizer, scheduler, train_dl, valid_dl






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    torch.nn.Linear(*tuple(model.weight.T.shape  )  ).state_dict()
							model.load_state_dict(_lowerCamelCase  )



class   UpperCAmelCase_    (       a):







							@require_cuda
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    Accelerator()
														assert PartialState._shared_state["_cpu"] is False
														assert PartialState._shared_state["device"].type == "cuda"
														with self.assertRaises(__a):
																					_lowerCAmelCase     :							List[str]    =    Accelerator(cpu=__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    Accelerator()
														_lowerCAmelCase     :							Any    =    GradientState()
														assert state.num_steps == 1
														_lowerCAmelCase     :							Any    =    4
														assert state.num_steps == 4

														assert state.sync_gradients is True
														_lowerCAmelCase     :							Any    =    False
														assert state.sync_gradients is False
														GradientState._reset_state()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    Accelerator()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    create_components()

														(
														    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    
														)     :							Union[str, Any]    =    accelerator.prepare(__a,			__a,			__a,			__a,			__a)

														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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    Accelerator()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    create_components()
														accelerator.prepare(__a,			__a,			__a,			__a,			__a)
														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    snake_case__      (   self):

														'''simple docstring'''

														PartialState._reset_state()

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

														with patch("torch.cuda.set_device",			__a), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"):
																					_lowerCAmelCase     :							int    =    Accelerator()
																					self.assertEqual(str(accelerator.state.device),			"cuda:64")







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    Accelerator()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[Any]    =    create_components()
														accelerator.prepare(__a,			__a,			__a,			__a,			__a)

														_lowerCAmelCase     :							Optional[Any]    =    get_signature(__a)

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

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

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    Accelerator()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[Any]    =    create_components()
														accelerator.prepare(__a,			__a,			__a,			__a,			__a)

														_lowerCAmelCase     :							Optional[Any]    =    get_signature(__a)

														# saving hook
														def save_config(__a,			__a,			__a):
																					_lowerCAmelCase     :							Optional[int]    =    {"class_name": models[0].__class__.__name__}

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

        # loading hook
														def load_config(__a,			__a):
																					with open(os.path.join(__a,			"data.json"),			"r") as f:
																												_lowerCAmelCase     :							Optional[int]    =    json.load(__a)

																					_lowerCAmelCase     :							List[Any]    =    config["class_name"]

														_lowerCAmelCase     :							Optional[int]    =    accelerator.register_save_state_pre_hook(__a)
														_lowerCAmelCase     :							List[str]    =    accelerator.register_load_state_pre_hook(__a)

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

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

																					# random class name to verify correct one is loaded
																					_lowerCAmelCase     :							Any    =    "random"

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

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

																					# random class name to verify correct one is loaded
																					_lowerCAmelCase     :							int    =    "random"

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

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    Accelerator()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    create_components()
														_lowerCAmelCase     :							Any    =    None

														# This should work
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    accelerator.prepare(
														    __a,			__a,			__a,			__a,			__a,			__a)
														self.assertTrue(dummy_obj is None)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    Accelerator()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    create_components()
														_lowerCAmelCase     :							Any    =    [1, 2, 3]

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







							@slow
							@require_bnb
							def    snake_case__      (   self):

														'''simple docstring'''

														from transformers import AutoModelForCausalLM

														_lowerCAmelCase     :							List[str]    =    AutoModelForCausalLM.from_pretrained(
														    "EleutherAI/gpt-neo-125m",			load_in_abit=__a,			device_map={"": 0},			)
														_lowerCAmelCase     :							Dict    =    Accelerator()

														# This should work
														_lowerCAmelCase     :							List[str]    =    accelerator.prepare(__a)







							@slow
							@require_bnb
							def    snake_case__      (   self):

														'''simple docstring'''

														from transformers import AutoModelForCausalLM

														_lowerCAmelCase     :							List[Any]    =    Accelerator()

														with init_empty_weights():
																					_lowerCAmelCase     :							Tuple    =    AutoModelForCausalLM.from_pretrained(
																					    "EleutherAI/gpt-neo-125m",			)
																					model.tie_weights()
																					_lowerCAmelCase     :							Optional[int]    =    infer_auto_device_map(__a)
																					_lowerCAmelCase     :							Dict    =    "cpu"

														_lowerCAmelCase     :							Tuple    =    AutoModelForCausalLM.from_pretrained(
														    "EleutherAI/gpt-neo-125m",			device_map=__a,			load_in_abit=__a,			llm_inta_enable_fpaa_cpu_offload=__a)

														# This should not work and get value error
														with self.assertRaises(__a):
																					_lowerCAmelCase     :							Optional[Any]    =    accelerator.prepare(__a)







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

														'''simple docstring'''

														from transformers import AutoModelForCausalLM

														_lowerCAmelCase     :							Union[str, Any]    =    {"distributed_type": DistributedType.MULTI_GPU}

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

														_lowerCAmelCase     :							int    =    AutoModelForCausalLM.from_pretrained(
														    "EleutherAI/gpt-neo-125m",			load_in_abit=__a,			device_map=__a,			)
														_lowerCAmelCase     :							str    =    Accelerator()

														# This should not work and get value error
														with self.assertRaises(__a):
																					_lowerCAmelCase     :							List[Any]    =    accelerator.prepare(__a)

														PartialState._reset_state()







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

														'''simple docstring'''

														from transformers import AutoModelForCausalLM

														with init_empty_weights():
																					_lowerCAmelCase     :							List[str]    =    AutoModelForCausalLM.from_pretrained(
																					    "EleutherAI/gpt-neo-125m",			)
																					_lowerCAmelCase     :							List[Any]    =    infer_auto_device_map(__a)
																					_lowerCAmelCase     :							List[str]    =    1

														_lowerCAmelCase     :							Tuple    =    AutoModelForCausalLM.from_pretrained(
														    "EleutherAI/gpt-neo-125m",			load_in_abit=__a,			device_map=__a,			)
														_lowerCAmelCase     :							Dict    =    Accelerator()

														# This should work
														_lowerCAmelCase     :							Union[str, Any]    =    accelerator.prepare(__a)







							@require_cuda
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    torch.nn.Linear(10,			10)
														_lowerCAmelCase     :							Union[str, Any]    =    torch.optim.SGD(model.parameters(),			lr=0.01)
														_lowerCAmelCase     :							Dict    =    Accelerator(cpu=__a)
														_lowerCAmelCase     :							Optional[int]    =    accelerator.prepare(__a)





from PIL import Image






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    image.size
							_lowerCAmelCase     :							Any    =    0
							_lowerCAmelCase     :							Tuple    =    image.load()
							for i in range(_lowerCamelCase  ):
														for j in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Union[str, Any]    =    pixels[j, i]
																					mean += pixel
							mean //= width * height

							for j in range(_lowerCamelCase  ):
														for i in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Optional[Any]    =    255 if pixels[i, j] > mean else 0
							return image


if __name__ == "__main__":
						_snake_case		=  mean_threshold(Image.open("path_to_image").convert("L"))
						image.save("output_image_path")
