





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

       return model, optimizer, scheduler, train_dl, valid_dl




def    __UpperCAmelCase(      lowercase_      ):
       return (model.weight.abs().sum() + model.bias.abs().sum()).item()




def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     List[Any]	=		torch.nn.Linear(*tuple(model.weight.T.shape      )      ).state_dict()
       model.load_state_dict(_lowerCAmelCase      )







class 			__A       (      __snake_case  ):




      """simple docstring"""







      @require_cuda
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		Accelerator()
             assert PartialState._shared_state["_cpu"] is False
             assert PartialState._shared_state["device"].type == "cuda"
             with self.assertRaises(A_):
                    _lowerCamelCase							:     Union[str, Any]	=		Accelerator(cpu=A_)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		Accelerator()
             _lowerCamelCase							:     Dict	=		GradientState()
             assert state.num_steps == 1
             _lowerCamelCase							:     Tuple	=		4
             assert state.num_steps == 4

             assert state.sync_gradients is True
             _lowerCamelCase							:     Dict	=		False
             assert state.sync_gradients is False
             GradientState._reset_state()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		Accelerator()
             _lowerCamelCase							:     Tuple	=		create_components()

             (
                 _lowerCamelCase					
             )							:     Dict	=		accelerator.prepare(A_      ,							A_      ,							A_      ,							A_      ,							A_)

             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):



             """simple docstring"""





             _lowerCamelCase							:     str	=		Accelerator()
             _lowerCamelCase							:     Optional[Any]	=		create_components()
             accelerator.prepare(A_      ,							A_      ,							A_      ,							A_      ,							A_)
             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):



             """simple docstring"""





             PartialState._reset_state()

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		Accelerator()
             _lowerCamelCase							:     int	=		create_components()
             accelerator.prepare(A_      ,							A_      ,							A_      ,							A_      ,							A_)

             _lowerCamelCase							:     Any	=		get_signature(A_)

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

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		Accelerator()
             _lowerCamelCase							:     Optional[int]	=		create_components()
             accelerator.prepare(A_      ,							A_      ,							A_      ,							A_      ,							A_)

             _lowerCamelCase							:     Any	=		get_signature(A_)

             # saving hook
             def save_config(a__      ,							a__      ,							a__):
                    _lowerCamelCase							:     Dict	=		{"class_name": models[0].__class__.__name__}

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

                    # loading hook
             def load_config(a__      ,							a__):
                    with open(os.path.join(A_      ,							'''data.json''')      ,							'''r''') as f:
                           _lowerCamelCase							:     int	=		json.load(A_)

                    _lowerCamelCase							:     List[str]	=		config["class_name"]

             _lowerCamelCase							:     List[Any]	=		accelerator.register_save_state_pre_hook(A_)
             _lowerCamelCase							:     List[Any]	=		accelerator.register_load_state_pre_hook(A_)

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

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

                    # random class name to verify correct one is loaded
                    _lowerCamelCase							:     Optional[Any]	=		"random"

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

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

                    # random class name to verify correct one is loaded
                    _lowerCamelCase							:     int	=		"random"

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		Accelerator()
             _lowerCamelCase							:     Optional[Any]	=		create_components()
             _lowerCamelCase							:     Dict	=		None

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		Accelerator()
             _lowerCamelCase							:     Any	=		create_components()
             _lowerCamelCase							:     int	=		[1, 2, 3]

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


      @slow
      @require_bnb
      def 				__snake_case			(		self):



             """simple docstring"""





             from transformers import AutoModelForCausalLM

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

             # This should work
             _lowerCamelCase							:     str	=		accelerator.prepare(A_)


      @slow
      @require_bnb
      def 				__snake_case			(		self):



             """simple docstring"""





             from transformers import AutoModelForCausalLM

             _lowerCamelCase							:     Optional[Any]	=		Accelerator()

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

             _lowerCamelCase							:     Tuple	=		AutoModelForCausalLM.from_pretrained(
                 '''EleutherAI/gpt-neo-125m'''      ,							device_map=A_      ,							load_in_abit=A_      ,							llm_inta_enable_fpaa_cpu_offload=A_)

             # This should not work and get value error
             with self.assertRaises(A_):
                    _lowerCamelCase							:     Any	=		accelerator.prepare(A_)


      @slow
      @require_bnb
      @require_multi_gpu
      def 				__snake_case			(		self):



             """simple docstring"""





             from transformers import AutoModelForCausalLM

             _lowerCamelCase							:     str	=		{"distributed_type": DistributedType.MULTI_GPU}

             with init_empty_weights():
                    _lowerCamelCase							:     Union[str, Any]	=		AutoModelForCausalLM.from_pretrained(
                        '''EleutherAI/gpt-neo-125m'''      ,							)
                    model.tie_weights()
                    _lowerCamelCase							:     Union[str, Any]	=		infer_auto_device_map(A_)
                    _lowerCamelCase							:     int	=		1

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

             # This should not work and get value error
             with self.assertRaises(A_):
                    _lowerCamelCase							:     int	=		accelerator.prepare(A_)

             PartialState._reset_state()


      @slow
      @require_bnb
      @require_multi_gpu
      def 				__snake_case			(		self):



             """simple docstring"""





             from transformers import AutoModelForCausalLM

             with init_empty_weights():
                    _lowerCamelCase							:     Optional[int]	=		AutoModelForCausalLM.from_pretrained(
                        '''EleutherAI/gpt-neo-125m'''      ,							)
                    _lowerCamelCase							:     Optional[int]	=		infer_auto_device_map(A_)
                    _lowerCamelCase							:     Any	=		1

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

             # This should work
             _lowerCamelCase							:     Optional[Any]	=		accelerator.prepare(A_)



      @require_cuda
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		torch.nn.Linear(10      ,							10)
             _lowerCamelCase							:     Tuple	=		torch.optim.SGD(model.parameters()      ,							lr=0.01)
             _lowerCamelCase							:     Tuple	=		Accelerator(cpu=A_)
             _lowerCamelCase							:     str	=		accelerator.prepare(A_)








import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCamelCase			       =      logging.get_logger(__name__)







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DonutImageProcessor instead.'''      ,							a__      ,							)
             super().__init__(*a__      ,							**a__)

