






'''simple docstring'''



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    __UpperCAmelCase	(				)  ->			List[Any]:

   """simple docstring"""
   __a				     =    torch.nn.Linear(2,					4    )
   __a				     =    torch.optim.AdamW(model.parameters(),					lr=1.0    )
   __a				     =    torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE__,					max_lr=0.0_1,					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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Any    )  ->			str:

   """simple docstring"""
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[str]    )  ->			str:

   """simple docstring"""
   __a				     =    torch.nn.Linear(*tuple(model.weight.T.shape    )    ).state_dict()
   model.load_state_dict(SCREAMING_SNAKE_CASE__    )



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      @require_cuda
      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    Accelerator()
         assert PartialState._shared_state["_cpu"] is False
         assert PartialState._shared_state["device"].type == "cuda"
         with self.assertRaises(lowerCamelCase      ):
            __a				     =    Accelerator(cpu=lowerCamelCase      )




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



         '''simple docstring'''
         __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  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    Accelerator()
         __a     ,			__a     ,			__a     ,			__a     ,			__a				     =    create_components()

         (
             (
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			
         )				     =    accelerator.prepare(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         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:



         '''simple docstring'''
         __a				     =    Accelerator()
         __a     ,			__a     ,			__a     ,			__a     ,			__a				     =    create_components()
         accelerator.prepare(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )
         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:



         '''simple docstring'''
         PartialState._reset_state()

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

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




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    Accelerator()
         __a     ,			__a     ,			__a     ,			__a     ,			__a				     =    create_components()
         accelerator.prepare(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         __a				     =    get_signature(lowerCamelCase      )

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

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

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




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



         '''simple docstring'''
         __a				     =    Accelerator()
         __a     ,			__a     ,			__a     ,			__a     ,			__a				     =    create_components()
         accelerator.prepare(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         __a				     =    get_signature(lowerCamelCase      )

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

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

        # loading hook
         def load_config(lowerCamelCase ,   lowerCamelCase      ):
            with open(os.path.join(lowerCamelCase ,   'data.json'      ) ,   'r'      ) as f:
               __a				     =    json.load(lowerCamelCase      )

            __a				     =    config['class_name']

         __a				     =    accelerator.register_save_state_pre_hook(lowerCamelCase      )
         __a				     =    accelerator.register_load_state_pre_hook(lowerCamelCase      )

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

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

            # random class name to verify correct one is loaded
            __a				     =    'random'

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

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

            # random class name to verify correct one is loaded
            __a				     =    'random'

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

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




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    Accelerator()
         __a     ,			__a     ,			__a     ,			__a     ,			__a				     =    create_components()
         __a				     =    None

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




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    Accelerator()
         __a     ,			__a     ,			__a     ,			__a     ,			__a				     =    create_components()
         __a				     =    [1, 2, 3]

         # This should work
         __a     ,			__a     ,			__a     ,			__a     ,			__a     ,			__a				     =    accelerator.prepare(
             lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )
         self.assertEqual(
             getattr(lowerCamelCase ,   '_is_accelerate_prepared' ,   lowerCamelCase      ) ,   lowerCamelCase ,   'Dummy object should have `_is_accelerate_prepared` set to `True`' ,   )
         self.assertEqual(
             getattr(lowerCamelCase ,   '_is_accelerate_prepared' ,   lowerCamelCase      ) ,   lowerCamelCase ,   'Model is missing `_is_accelerator_prepared` or is set to `False`' ,   )
         self.assertEqual(
             getattr(lowerCamelCase ,   '_is_accelerate_prepared' ,   lowerCamelCase      ) ,   lowerCamelCase ,   'Optimizer is missing `_is_accelerator_prepared` or is set to `False`' ,   )
         self.assertEqual(
             getattr(lowerCamelCase ,   '_is_accelerate_prepared' ,   lowerCamelCase      ) ,   lowerCamelCase ,   'Scheduler is missing `_is_accelerator_prepared` or is set to `False`' ,   )
         self.assertEqual(
             getattr(lowerCamelCase ,   '_is_accelerate_prepared' ,   lowerCamelCase      ) ,   lowerCamelCase ,   'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`' ,   )
         self.assertEqual(
             getattr(lowerCamelCase ,   '_is_accelerate_prepared' ,   lowerCamelCase      ) ,   lowerCamelCase ,   'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`' ,   )




      @slow
      @require_bnb
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         from transformers import AutoModelForCausalLM

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

         # This should work
         __a				     =    accelerator.prepare(lowerCamelCase      )




      @slow
      @require_bnb
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         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(lowerCamelCase      )
            __a				     =    'cpu'

         __a				     =    AutoModelForCausalLM.from_pretrained(
             'EleutherAI/gpt-neo-125m' ,   device_map=lowerCamelCase ,   load_in_abit=lowerCamelCase ,   llm_inta_enable_fpaa_cpu_offload=lowerCamelCase      )

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




      @slow
      @require_bnb
      @require_multi_gpu
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         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(lowerCamelCase      )
            __a				     =    1

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

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

         PartialState._reset_state()




      @slow
      @require_bnb
      @require_multi_gpu
      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         from transformers import AutoModelForCausalLM

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

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

         # This should work
         __a				     =    accelerator.prepare(lowerCamelCase      )






      @require_cuda
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    torch.nn.Linear(10 ,   10      )
         __a				     =    torch.optim.SGD(model.parameters() ,   lr=0.01      )
         __a				     =    Accelerator(cpu=lowerCamelCase      )
         __a				     =    accelerator.prepare(lowerCamelCase      )







'''simple docstring'''



import warnings

from .generation import TFGenerationMixin
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      # warning at import time
      warnings.warn(
          "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
          "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."      ,      _lowerCAmelCase      ,      )