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



						'''simple docstring'''

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

						return model, optimizer, scheduler, train_dl, valid_dl




def 	A_ (							snake_case							:   Union[str, Any] )      ->    Dict:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   List[str] )      ->    List[str]:



						'''simple docstring'''

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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							@require_cuda
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






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



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






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

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



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			Accelerator()
													__UpperCamelCase       =			create_components()

													(
													    __UpperCamelCase							
													)       =			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     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			Accelerator()
													__UpperCamelCase       =			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     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													PartialState._reset_state()

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

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



							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			Accelerator()
													__UpperCamelCase       =			create_components()
													accelerator.prepare(UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__			)

													__UpperCamelCase       =			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     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			Accelerator()
													__UpperCamelCase       =			create_components()
													accelerator.prepare(UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__			)

													__UpperCamelCase       =			get_signature(UpperCamelCase__			)

													# saving hook
													def save_config(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			{'''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(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																			with open(os.path.join(UpperCamelCase__     ,	'''data.json'''			)     ,	'''r'''			) as f:
																									__UpperCamelCase       =			json.load(UpperCamelCase__			)

																			__UpperCamelCase       =			config['''class_name''']

													__UpperCamelCase       =			accelerator.register_save_state_pre_hook(UpperCamelCase__			)
													__UpperCamelCase       =			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
																			__UpperCamelCase       =			'''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
																			__UpperCamelCase       =			'''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     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			Accelerator()
													__UpperCamelCase       =			create_components()
													__UpperCamelCase       =			None

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



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






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

													# This should work
													__UpperCamelCase       =			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     A__		(  self			)->   List[str]:





													'''simple docstring'''






													from transformers import AutoModelForCausalLM

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

													# This should work
													__UpperCamelCase       =			accelerator.prepare(UpperCamelCase__			)



							@slow
							@require_bnb
							def     A__		(  self			)->   str:





													'''simple docstring'''






													from transformers import AutoModelForCausalLM

													__UpperCamelCase       =			Accelerator()

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

													__UpperCamelCase       =			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__			):
																			__UpperCamelCase       =			accelerator.prepare(UpperCamelCase__			)



							@slow
							@require_bnb
							@require_multi_gpu
							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													from transformers import AutoModelForCausalLM

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

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

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

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

													PartialState._reset_state()



							@slow
							@require_bnb
							@require_multi_gpu
							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													from transformers import AutoModelForCausalLM

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

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

													# This should work
													__UpperCamelCase       =			accelerator.prepare(UpperCamelCase__			)



							@require_cuda
							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			torch.nn.Linear(10     ,	10			)
													__UpperCamelCase       =			torch.optim.SGD(model.parameters()     ,	lr=0.0_1			)
													__UpperCamelCase       =			Accelerator(cpu=UpperCamelCase__			)
													__UpperCamelCase       =			accelerator.prepare(UpperCamelCase__			)






from collections import Counter
from timeit import timeit




def 	A_ (							snake_case							:   str = "" ,      )      ->    bool:



						'''simple docstring'''

						return sum(c % 2 for c in Counter(input_str.replace(''' ''' ,      '''''' ).lower() ).values() ) < 2




def 	A_ (							snake_case							:   str = "" )      ->    bool:



						'''simple docstring'''

						if len(snake_case ) == 0:
												return True
						__UpperCamelCase       =			input_str.replace(''' ''' ,      '''''' ).lower()
						# character_freq_dict: Stores the frequency of every character in the input string
						__UpperCamelCase       =			{}

						for character in lower_case_input_str:
												__UpperCamelCase       =			character_freq_dict.get(snake_case ,      0 ) + 1
						__UpperCamelCase       =			0

						for character_count in character_freq_dict.values():
												if character_count % 2:
																		odd_char += 1
						if odd_char > 1:
												return False
						return True




def 	A_ (							snake_case							:   str = "" )      ->    None:



						'''simple docstring'''

						print('''\nFor string = ''' ,      snake_case ,      ''':''' )
						print(
						    '''> can_string_be_rearranged_as_palindrome_counter()''' ,      '''\tans =''' ,      can_string_be_rearranged_as_palindrome_counter(snake_case ) ,      '''\ttime =''' ,      timeit(
						        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)''' ,      setup='''import __main__ as z''' ,      ) ,      '''seconds''' ,      )
						print(
						    '''> can_string_be_rearranged_as_palindrome()''' ,      '''\tans =''' ,      can_string_be_rearranged_as_palindrome(snake_case ) ,      '''\ttime =''' ,      timeit(
						        '''z.can_string_be_rearranged_as_palindrome(z.check_str)''' ,      setup='''import __main__ as z''' ,      ) ,      '''seconds''' ,      )


if __name__ == "__main__":
				lowercase__							:							Tuple									=			input(
				    "Enter string to determine if it can be rearranged as a palindrome or not: "
				).strip()
				benchmark(check_str)
				lowercase__							:							Dict									=			can_string_be_rearranged_as_palindrome_counter(check_str)
				print(F"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")




