






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      lowercase__      (     ):


					'''simple docstring'''


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

					return model, optimizer, scheduler, train_dl, valid_dl




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							Optional[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						torch.nn.Linear(*tuple(model.weight.T.shape  )  ).state_dict()
					model.load_state_dict(__snake_case  )






class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		@require_cuda
		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      Union[str, Any]            =						Accelerator()
							assert PartialState._shared_state["_cpu"] is False
							assert PartialState._shared_state["device"].type == "cuda"
							with self.assertRaises(_UpperCamelCase   ):
												UpperCAmelCase_    :      List[str]            =						Accelerator(cpu=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      Optional[int]            =						Accelerator()
							UpperCAmelCase_    :      Dict            =						GradientState()
							assert state.num_steps == 1
							UpperCAmelCase_    :      Any            =						4
							assert state.num_steps == 4

							assert state.sync_gradients is True
							UpperCAmelCase_    :      Tuple            =						False
							assert state.sync_gradients is False
							GradientState._reset_state()



		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      Union[str, Any]            =						Accelerator()
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						create_components()

							(
							    (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  
							)    :      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 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      List[str]            =						Accelerator()
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						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 					__UpperCAmelCase			(							self   )	->							Tuple:
							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'   ):
												UpperCAmelCase_    :      Any            =						Accelerator()
												self.assertEqual(str(accelerator.state.device   )				,		'cuda:64'   )



		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      Tuple            =						Accelerator()
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						create_components()
							accelerator.prepare(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Dict            =						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 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      List[Any]            =						Accelerator()
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						create_components()
							accelerator.prepare(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						get_signature(_UpperCamelCase   )

							# saving hook
							def save_config(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						{'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:
																	UpperCAmelCase_    :      Optional[Any]            =						json.load(_UpperCamelCase   )

												UpperCAmelCase_    :      str            =						config['class_name']

							UpperCAmelCase_    :      Union[str, Any]            =						accelerator.register_save_state_pre_hook(_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						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_    :      List[str]            =						'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_    :      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 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Optional[int]            =						Accelerator()
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						create_components()
							UpperCAmelCase_    :      Tuple            =						None

							# This should work
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						accelerator.prepare(
							    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							self.assertTrue(dummy_obj is None   )



		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      List[Any]            =						Accelerator()
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						create_components()
							UpperCAmelCase_    :      List[Any]            =						[1, 2, 3]

							# This should work
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      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 					__UpperCAmelCase			(							self   )	->							Dict:
							from transformers import AutoModelForCausalLM

							UpperCAmelCase_    :      Optional[Any]            =						AutoModelForCausalLM.from_pretrained(
							    'EleutherAI/gpt-neo-125m'				,		load_in_abit=_UpperCamelCase				,		device_map={'': 0}				,		)
							UpperCAmelCase_    :      List[str]            =						Accelerator()

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



		@slow
		@require_bnb
		def 					__UpperCAmelCase			(							self   )	->							Any:
							from transformers import AutoModelForCausalLM

							UpperCAmelCase_    :      Optional[int]            =						Accelerator()

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

							UpperCAmelCase_    :      Optional[Any]            =						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_    :      Optional[Any]            =						accelerator.prepare(_UpperCamelCase   )



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

							UpperCAmelCase_    :      List[Any]            =						{'distributed_type': DistributedType.MULTI_GPU}

							with init_empty_weights():
												UpperCAmelCase_    :      List[str]            =						AutoModelForCausalLM.from_pretrained(
												    'EleutherAI/gpt-neo-125m'				,		)
												model.tie_weights()
												UpperCAmelCase_    :      Optional[Any]            =						infer_auto_device_map(_UpperCamelCase   )
												UpperCAmelCase_    :      Any            =						1

							UpperCAmelCase_    :      Tuple            =						AutoModelForCausalLM.from_pretrained(
							    'EleutherAI/gpt-neo-125m'				,		load_in_abit=_UpperCamelCase				,		device_map=_UpperCamelCase				,		)
							UpperCAmelCase_    :      str            =						Accelerator()

							# This should not work and get value error
							with self.assertRaises(_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						accelerator.prepare(_UpperCamelCase   )

							PartialState._reset_state()



		@slow
		@require_bnb
		@require_multi_gpu
		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							from transformers import AutoModelForCausalLM

							with init_empty_weights():
												UpperCAmelCase_    :      List[Any]            =						AutoModelForCausalLM.from_pretrained(
												    'EleutherAI/gpt-neo-125m'				,		)
												UpperCAmelCase_    :      Tuple            =						infer_auto_device_map(_UpperCamelCase   )
												UpperCAmelCase_    :      int            =						1

							UpperCAmelCase_    :      Union[str, Any]            =						AutoModelForCausalLM.from_pretrained(
							    'EleutherAI/gpt-neo-125m'				,		load_in_abit=_UpperCamelCase				,		device_map=_UpperCamelCase				,		)
							UpperCAmelCase_    :      int            =						Accelerator()

							# This should work
							UpperCAmelCase_    :      Any            =						accelerator.prepare(_UpperCamelCase   )



		@require_cuda
		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      str            =						torch.nn.Linear(1_0				,		1_0   )
							UpperCAmelCase_    :      Dict            =						torch.optim.SGD(model.parameters()				,		lr=0.01   )
							UpperCAmelCase_    :      Any            =						Accelerator(cpu=_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						accelerator.prepare(_UpperCamelCase   )








"""simple docstring"""





from math import isqrt, loga



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = [True] * max_number
		for i in range(2  ,				isqrt(max_number - 1				) + 1				):
				if is_prime[i]:
						for j in range(i**2  ,				UpperCamelCase_  ,				UpperCamelCase_				):
								__SCREAMING_SNAKE_CASE            = False

		return [i for i in range(2  ,				UpperCamelCase_				) if is_prime[i]]



def 						_lowerCAmelCase				(				UpperCamelCase_ = 80_0800  ,				UpperCamelCase_ = 80_0800				):
		__SCREAMING_SNAKE_CASE            = degree * loga(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = int(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = calculate_prime_numbers(UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				) - 1
		while left < right:
				while (
				    prime_numbers[right] * loga(prime_numbers[left]				)
				    + prime_numbers[left] * loga(prime_numbers[right]				)
				    > upper_bound
				):
						right -= 1
				hybrid_integers_count += right - left
				left += 1

		return hybrid_integers_count


if __name__ == "__main__":
							print(F"""{solution() = }""")




