





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

    return model, optimizer, scheduler, train_dl, valid_dl


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Tuple:
    return (model.weight.abs().sum() + model.bias.abs().sum()).item()


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Union[str, Any]:
    lowerCamelCase__					:   Optional[Any]      				=	torch.nn.Linear(*tuple(model.weight.T.shape				)				).state_dict()
    model.load_state_dict(_UpperCAmelCase				)


class      lowerCAmelCase							(      __UpperCamelCase    ):
 @require_cuda
 def   A_			(   self						:   str			)    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	Accelerator()
     assert PartialState._shared_state["_cpu"] is False
     assert PartialState._shared_state["device"].type == "cuda"
     with self.assertRaises(UpperCAmelCase			):
         lowerCamelCase__					:   str      				=	Accelerator(cpu=UpperCAmelCase			)
 def   A_			(   self						:   Dict			)    ->			Any:
     lowerCamelCase__					:   Union[str, Any]      				=	Accelerator()
     lowerCamelCase__					:   Dict      				=	GradientState()
     assert state.num_steps == 1
     lowerCamelCase__					:   Any      				=	4
     assert state.num_steps == 4

     assert state.sync_gradients is True
     lowerCamelCase__					:   Optional[Any]      				=	False
     assert state.sync_gradients is False
     GradientState._reset_state()
 def   A_			(   self						:   Optional[Any]			)    ->			Dict:
     lowerCamelCase__					:   Optional[Any]      				=	Accelerator()
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	create_components()

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

     # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
     def noop(*UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			):
         pass

     with patch('torch.cuda.set_device'    ,  UpperCAmelCase			), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'			):
         lowerCamelCase__					:   Tuple      				=	Accelerator()
         self.assertEqual(str(accelerator.state.device			)    ,  'cuda:64'			)
 def   A_			(   self						:   Optional[int]			)    ->			Dict:
     lowerCamelCase__					:   Dict      				=	Accelerator()
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	create_components()
     accelerator.prepare(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

     lowerCamelCase__					:   str      				=	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						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[int]      				=	Accelerator()
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	create_components()
     accelerator.prepare(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

     lowerCamelCase__					:   int      				=	get_signature(UpperCAmelCase			)

     # saving hook
     def save_config(UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   List[str]			):
         lowerCamelCase__					:   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						:   List[Any]    ,  UpperCAmelCase						:   Dict			):
         with open(os.path.join(UpperCAmelCase    ,  'data.json'			)    ,  'r'			) as f:
             lowerCamelCase__					:   List[str]      				=	json.load(UpperCAmelCase			)

         lowerCamelCase__					:   Union[str, Any]      				=	config['class_name']

     lowerCamelCase__					:   Union[str, Any]      				=	accelerator.register_save_state_pre_hook(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[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
         lowerCamelCase__					:   Tuple      				=	'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
         lowerCamelCase__					:   str      				=	'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						:   Optional[Any]			)    ->			List[str]:
     lowerCamelCase__					:   Optional[Any]      				=	Accelerator()
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	create_components()
     lowerCamelCase__					:   List[str]      				=	None

     # This should work
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	accelerator.prepare(
         UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
     self.assertTrue(dummy_obj is None			)
 def   A_			(   self						:   List[str]			)    ->			Optional[int]:
     lowerCamelCase__					:   Union[str, Any]      				=	Accelerator()
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	create_components()
     lowerCamelCase__					:   List[Any]      				=	[1, 2, 3]

     # This should work
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Optional[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   A_			(   self						:   Optional[int]			)    ->			Optional[Any]:
     from transformers import AutoModelForCausalLM

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

     # This should work
     lowerCamelCase__					:   Tuple      				=	accelerator.prepare(UpperCAmelCase			)
 @slow
 @require_bnb
 def   A_			(   self						:   List[Any]			)    ->			str:
     from transformers import AutoModelForCausalLM

     lowerCamelCase__					:   str      				=	Accelerator()

     with init_empty_weights():
         lowerCamelCase__					:   int      				=	AutoModelForCausalLM.from_pretrained(
             'EleutherAI/gpt-neo-125m'    ,  )
         model.tie_weights()
         lowerCamelCase__					:   int      				=	infer_auto_device_map(UpperCAmelCase			)
         lowerCamelCase__					:   Dict      				=	'cpu'

     lowerCamelCase__					:   int      				=	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			):
         lowerCamelCase__					:   Union[str, Any]      				=	accelerator.prepare(UpperCAmelCase			)
 @slow
 @require_bnb
 @require_multi_gpu
 def   A_			(   self						:   List[Any]			)    ->			Tuple:
     from transformers import AutoModelForCausalLM

     lowerCamelCase__					:   Union[str, Any]      				=	{'distributed_type': DistributedType.MULTI_GPU}

     with init_empty_weights():
         lowerCamelCase__					:   Optional[Any]      				=	AutoModelForCausalLM.from_pretrained(
             'EleutherAI/gpt-neo-125m'    ,  )
         model.tie_weights()
         lowerCamelCase__					:   Tuple      				=	infer_auto_device_map(UpperCAmelCase			)
         lowerCamelCase__					:   int      				=	1

     lowerCamelCase__					:   Any      				=	AutoModelForCausalLM.from_pretrained(
         'EleutherAI/gpt-neo-125m'    ,  load_in_abit=UpperCAmelCase    ,  device_map=UpperCAmelCase    ,  )
     lowerCamelCase__					:   int      				=	Accelerator()

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

     PartialState._reset_state()
 @slow
 @require_bnb
 @require_multi_gpu
 def   A_			(   self						:   Union[str, Any]			)    ->			Optional[Any]:
     from transformers import AutoModelForCausalLM

     with init_empty_weights():
         lowerCamelCase__					:   str      				=	AutoModelForCausalLM.from_pretrained(
             'EleutherAI/gpt-neo-125m'    ,  )
         lowerCamelCase__					:   Tuple      				=	infer_auto_device_map(UpperCAmelCase			)
         lowerCamelCase__					:   Union[str, Any]      				=	1

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

     # This should work
     lowerCamelCase__					:   Any      				=	accelerator.prepare(UpperCAmelCase			)







 @require_cuda
 def   A_			(   self						:   str			)    ->			Tuple:
     lowerCamelCase__					:   List[Any]      				=	torch.nn.Linear(10    ,  10			)
     lowerCamelCase__					:   Optional[Any]      				=	torch.optim.SGD(model.parameters()    ,  lr=0.0_1			)
     lowerCamelCase__					:   str      				=	Accelerator(cpu=UpperCAmelCase			)
     lowerCamelCase__					:   Dict      				=	accelerator.prepare(UpperCAmelCase			)












_UpperCAmelCase       : str			     =       """Tobias Carryer"""

from time import time

class      lowerCAmelCase							:
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   str=int(time()			)			)    ->			Optional[int]:  # noqa: B008
     lowerCamelCase__					:   List[Any]      				=	multiplier
     lowerCamelCase__					:   Tuple      				=	increment
     lowerCamelCase__					:   int      				=	modulo
     lowerCamelCase__					:   Any      				=	seed







 def   A_			(   self						:   Optional[int]			)    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	(self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
    # Show the LCG in action.
    _UpperCAmelCase       : List[Any]			     =       LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
    while True:
        print(lcg.next_number())





