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

				"""simple docstring"""







				A__  :	Optional[int]         =							torch.nn.Linear(2							,						4       )
				A__  :	int         =							torch.optim.AdamW(model.parameters()							,						lr=1.0       )
				A__  :	Any         =							torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase							,						max_lr=0.0_1							,						steps_per_epoch=2							,						epochs=1       )
				A__  :	Optional[Any]         =							DataLoader(TensorDataset(torch.tensor([1, 2, 3]       )       )       )
				A__  :	Union[str, Any]         =							DataLoader(TensorDataset(torch.tensor([4, 5, 6]       )       )       )

				return model, optimizer, scheduler, train_dl, valid_dl

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, Any]       ) ->       Dict:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       List[str]:

				"""simple docstring"""







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







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





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





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							Accelerator()
					A__  :	str         =							GradientState()
					assert state.num_steps == 1
					A__  :	Optional[Any]         =							4
					assert state.num_steps == 4

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





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							Accelerator()
					A__       ,							A__       ,							A__       ,							A__       ,							A__  :	Dict         =							create_components()

					(
					    (
					    A__
					)       ,							(
					    A__
					)       ,							(
					    A__
					)       ,							(
					    A__
					)       ,							(
					    A__
					)       ,							
					)  :	Any         =							accelerator.prepare(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

					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		):
					A__  :	Dict         =							Accelerator()
					A__       ,							A__       ,							A__       ,							A__       ,							A__  :	str         =							create_components()
					accelerator.prepare(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)
					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		):
					PartialState._reset_state()

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

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





	def   __snake_case				(			self		):
					A__  :	Tuple         =							Accelerator()
					A__       ,							A__       ,							A__       ,							A__       ,							A__  :	Any         =							create_components()
					accelerator.prepare(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

					A__  :	Optional[Any]         =							get_signature(UpperCamelCase__		)

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

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

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





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							Accelerator()
					A__       ,							A__       ,							A__       ,							A__       ,							A__  :	Optional[int]         =							create_components()
					accelerator.prepare(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

					A__  :	Union[str, Any]         =							get_signature(UpperCamelCase__		)

					# saving hook
					def save_config(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	Tuple         =							{'''class_name''': models[0].__class__.__name__}

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

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

									A__  :	List[Any]         =							config['''class_name''']

					A__  :	Dict         =							accelerator.register_save_state_pre_hook(UpperCamelCase__		)
					A__  :	Optional[int]         =							accelerator.register_load_state_pre_hook(UpperCamelCase__		)

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

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

									# random class name to verify correct one is loaded
									A__  :	Union[str, Any]         =							'''random'''

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

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

									# random class name to verify correct one is loaded
									A__  :	Union[str, Any]         =							'''random'''

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

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





	def   __snake_case				(			self		):
					A__  :	int         =							Accelerator()
					A__       ,							A__       ,							A__       ,							A__       ,							A__  :	Tuple         =							create_components()
					A__  :	List[Any]         =							None

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





	def   __snake_case				(			self		):
					A__  :	str         =							Accelerator()
					A__       ,							A__       ,							A__       ,							A__       ,							A__  :	int         =							create_components()
					A__  :	Any         =							[1, 2, 3]

					# This should work
					A__       ,							A__       ,							A__       ,							A__       ,							A__       ,							A__  :	List[Any]         =							accelerator.prepare(
					    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)
					self.assertEqual(
					    getattr(UpperCamelCase__				,       '''_is_accelerate_prepared'''				,       UpperCamelCase__		)				,       UpperCamelCase__				,       '''Dummy object should have `_is_accelerate_prepared` set to `True`'''				,       )
					self.assertEqual(
					    getattr(UpperCamelCase__				,       '''_is_accelerate_prepared'''				,       UpperCamelCase__		)				,       UpperCamelCase__				,       '''Model is missing `_is_accelerator_prepared` or is set to `False`'''				,       )
					self.assertEqual(
					    getattr(UpperCamelCase__				,       '''_is_accelerate_prepared'''				,       UpperCamelCase__		)				,       UpperCamelCase__				,       '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`'''				,       )
					self.assertEqual(
					    getattr(UpperCamelCase__				,       '''_is_accelerate_prepared'''				,       UpperCamelCase__		)				,       UpperCamelCase__				,       '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`'''				,       )
					self.assertEqual(
					    getattr(UpperCamelCase__				,       '''_is_accelerate_prepared'''				,       UpperCamelCase__		)				,       UpperCamelCase__				,       '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''				,       )
					self.assertEqual(
					    getattr(UpperCamelCase__				,       '''_is_accelerate_prepared'''				,       UpperCamelCase__		)				,       UpperCamelCase__				,       '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''				,       )





	@slow
	@require_bnb
	def   __snake_case				(			self		):
					from transformers import AutoModelForCausalLM

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

					# This should work
					A__  :	Optional[Any]         =							accelerator.prepare(UpperCamelCase__		)





	@slow
	@require_bnb
	def   __snake_case				(			self		):
					from transformers import AutoModelForCausalLM

					A__  :	List[str]         =							Accelerator()

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

					A__  :	Dict         =							AutoModelForCausalLM.from_pretrained(
					    '''EleutherAI/gpt-neo-125m'''				,       device_map=UpperCamelCase__				,       load_in_abit=UpperCamelCase__				,       llm_inta_enable_fpaa_cpu_offload=UpperCamelCase__		)

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





	@slow
	@require_bnb
	@require_multi_gpu
	def   __snake_case				(			self		):
					from transformers import AutoModelForCausalLM

					A__  :	Any         =							{'''distributed_type''': DistributedType.MULTI_GPU}

					with init_empty_weights():
									A__  :	Tuple         =							AutoModelForCausalLM.from_pretrained(
									    '''EleutherAI/gpt-neo-125m'''				,       )
									model.tie_weights()
									A__  :	Dict         =							infer_auto_device_map(UpperCamelCase__		)
									A__  :	Dict         =							1

					A__  :	Dict         =							AutoModelForCausalLM.from_pretrained(
					    '''EleutherAI/gpt-neo-125m'''				,       load_in_abit=UpperCamelCase__				,       device_map=UpperCamelCase__				,       )
					A__  :	Any         =							Accelerator()

					# This should not work and get value error
					with self.assertRaises(UpperCamelCase__		):
									A__  :	Dict         =							accelerator.prepare(UpperCamelCase__		)

					PartialState._reset_state()





	@slow
	@require_bnb
	@require_multi_gpu
	def   __snake_case				(			self		):
					from transformers import AutoModelForCausalLM

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

					A__  :	str         =							AutoModelForCausalLM.from_pretrained(
					    '''EleutherAI/gpt-neo-125m'''				,       load_in_abit=UpperCamelCase__				,       device_map=UpperCamelCase__				,       )
					A__  :	List[Any]         =							Accelerator()

					# This should work
					A__  :	List[Any]         =							accelerator.prepare(UpperCamelCase__		)





	@require_cuda
	def   __snake_case				(			self		):
					A__  :	int         =							torch.nn.Linear(10				,       10		)
					A__  :	int         =							torch.optim.SGD(model.parameters()				,       lr=0.0_1		)
					A__  :	str         =							Accelerator(cpu=UpperCamelCase__		)
					A__  :	Optional[Any]         =							accelerator.prepare(UpperCamelCase__		)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							0
				A__  :	Optional[Any]         =							len(__UpperCamelCase       )

				for i in range(n - 1       ):
								for j in range(i + 1							,						__UpperCamelCase       ):
												if arr[i] > arr[j]:
																num_inversions += 1

				return num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Tuple:

				"""simple docstring"""







				if len(__UpperCamelCase       ) <= 1:
								return arr, 0
				A__  :	Optional[int]         =							len(__UpperCamelCase       ) // 2
				A__  :	List[str]         =							arr[0:mid]
				A__  :	Union[str, Any]         =							arr[mid:]

				A__       ,							A__  :	List[Any]         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							_count_cross_inversions(__UpperCamelCase							,						__UpperCamelCase       )

				A__  :	Any         =							inversion_p + inversions_q + cross_inversions
				return c, num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						List[Any]       ) ->       Dict:

				"""simple docstring"""







				A__  :	str         =							[]
				A__  :	Tuple         =							0
				while i < len(__UpperCamelCase       ) and j < len(__UpperCamelCase       ):
								if p[i] > q[j]:
												# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
												# These are all inversions. The claim emerges from the
												# property that P is sorted.
												num_inversion += len(__UpperCamelCase       ) - i
												r.append(q[j]       )
												j += 1
								else:
												r.append(p[i]       )
												i += 1

				if i < len(__UpperCamelCase       ):
								r.extend(p[i:]       )
				else:
								r.extend(q[j:]       )

				return r, num_inversion

def 				SCREAMING_SNAKE_CASE     (      ) ->       Tuple:

				"""simple docstring"""







				A__  :	List[str]         =							[10, 2, 1, 5, 5, 2, 11]

				# this arr has 8 inversions:
				# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

				A__  :	int         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 8

				print('''number of inversions = '''							,						__UpperCamelCase       )

				# testing an array with zero inversion (a sorted arr_1)

				arr_a.sort()
				A__  :	Optional[Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )

				# an empty list should also have zero inversions
				A__  :	Union[str, Any]         =							[]
				A__  :	Union[str, Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Any         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()