






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'''







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

					return model, optimizer, scheduler, train_dl, valid_dl




def      __A					(     _lowercase ):

					'''simple docstring'''







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




def      __A					(     _lowercase ):

					'''simple docstring'''







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





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@require_cuda
							def       __A (  self:   int   )    ->				List[str]:
												_A								= Accelerator()
												assert PartialState._shared_state["_cpu"] is False
												assert PartialState._shared_state["device"].type == "cuda"
												with self.assertRaises(__A   ):
																	_A								= Accelerator(cpu=__A   )

							def       __A (  self:   Tuple   )    ->				List[str]:
												_A								= Accelerator()
												_A								= GradientState()
												assert state.num_steps == 1
												_A								= 4
												assert state.num_steps == 4

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

							def       __A (  self:   int   )    ->				Union[str, Any]:
												_A								= Accelerator()
												_A							,_A							,_A							,_A							,_A								= create_components()

												(
												    (
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,
												)								= 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       __A (  self:   Optional[int]   )    ->				Any:
												_A								= Accelerator()
												_A							,_A							,_A							,_A							,_A								= 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       __A (  self:   List[str]   )    ->				List[Any]:
												PartialState._reset_state()

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

												with patch('''torch.cuda.set_device'''		,     __A   ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''   ):
																	_A								= Accelerator()
																	self.assertEqual(str(accelerator.state.device   )		,     '''cuda:64'''   )

							def       __A (  self:   Any   )    ->				Dict:
												_A								= Accelerator()
												_A							,_A							,_A							,_A							,_A								= create_components()
												accelerator.prepare(__A		,     __A		,     __A		,     __A		,     __A   )

												_A								= 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       __A (  self:   List[str]   )    ->				Dict:
												_A								= Accelerator()
												_A							,_A							,_A							,_A							,_A								= create_components()
												accelerator.prepare(__A		,     __A		,     __A		,     __A		,     __A   )

												_A								= get_signature(__A   )

												# saving hook
												def save_config(__A:   Optional[int]		,     __A:   List[Any]		,     __A:   Tuple   ):
																	_A								= {'''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:   List[Any]		,     __A:   List[Any]   ):
																	with open(os.path.join(__A		,     '''data.json'''   )		,     '''r'''   ) as f:
																						_A								= json.load(__A   )

																	_A								= config['''class_name''']

												_A								= accelerator.register_save_state_pre_hook(__A   )
												_A								= 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
																	_A								= '''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
																	_A								= '''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       __A (  self:   int   )    ->				Dict:
												_A								= Accelerator()
												_A							,_A							,_A							,_A							,_A								= create_components()
												_A								= None

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

							def       __A (  self:   Union[str, Any]   )    ->				Dict:
												_A								= Accelerator()
												_A							,_A							,_A							,_A							,_A								= create_components()
												_A								= [1, 2, 3]

												# This should work
												_A							,_A							,_A							,_A							,_A							,_A								= 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       __A (  self:   Union[str, Any]   )    ->				List[str]:
												from transformers import AutoModelForCausalLM

												_A								= AutoModelForCausalLM.from_pretrained(
												    '''EleutherAI/gpt-neo-125m'''		,     load_in_abit=__A		,     device_map={'''''': 0}		,     )
												_A								= Accelerator()

												# This should work
												_A								= accelerator.prepare(__A   )

							@slow
							@require_bnb
							def       __A (  self:   Optional[Any]   )    ->				Dict:
												from transformers import AutoModelForCausalLM

												_A								= Accelerator()

												with init_empty_weights():
																	_A								= AutoModelForCausalLM.from_pretrained(
																	    '''EleutherAI/gpt-neo-125m'''		,     )
																	model.tie_weights()
																	_A								= infer_auto_device_map(__A   )
																	_A								= '''cpu'''

												_A								= 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   ):
																	_A								= accelerator.prepare(__A   )

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

												_A								= {'''distributed_type''': DistributedType.MULTI_GPU}

												with init_empty_weights():
																	_A								= AutoModelForCausalLM.from_pretrained(
																	    '''EleutherAI/gpt-neo-125m'''		,     )
																	model.tie_weights()
																	_A								= infer_auto_device_map(__A   )
																	_A								= 1

												_A								= AutoModelForCausalLM.from_pretrained(
												    '''EleutherAI/gpt-neo-125m'''		,     load_in_abit=__A		,     device_map=__A		,     )
												_A								= Accelerator()

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

												PartialState._reset_state()

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

												with init_empty_weights():
																	_A								= AutoModelForCausalLM.from_pretrained(
																	    '''EleutherAI/gpt-neo-125m'''		,     )
																	_A								= infer_auto_device_map(__A   )
																	_A								= 1

												_A								= AutoModelForCausalLM.from_pretrained(
												    '''EleutherAI/gpt-neo-125m'''		,     load_in_abit=__A		,     device_map=__A		,     )
												_A								= Accelerator()

												# This should work
												_A								= accelerator.prepare(__A   )



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











from __future__ import annotations




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):  # noqa: E741

					'''simple docstring'''







					while r - l > 1:
										_A								= (l + r) // 2
										if v[m] >= key:
															_A								= m
										else:
															_A								= m  # noqa: E741
					return r




def      __A					(     _lowercase ):

					'''simple docstring'''







					if len(_lowercase ) == 0:
										return 0

					_A								= [0] * len(_lowercase )
					_A								= 1

					_A								= v[0]

					for i in range(1					,			len(_lowercase ) ):
										if v[i] < tail[0]:
															_A								= v[i]
										elif v[i] > tail[length - 1]:
															_A								= v[i]
															length += 1
										else:
															_A								= v[i]

					return length


if __name__ == "__main__":
				import doctest

				doctest.testmod()



