

'''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 	_lowerCAmelCase (						)				->      Dict:
					"""simple docstring"""




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

					return model, optimizer, scheduler, train_dl, valid_dl


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[int]    )				->      str:
					"""simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict    )				->      Optional[Any]:
					"""simple docstring"""




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







class 							A__   (	lowerCamelCase_  ):





		@require_cuda
		def 						A  (		self				:     Any			)     ->   str:





							'''simple docstring'''






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





		def 						A  (		self				:     Union[str, Any]			)     ->   Optional[int]:





							'''simple docstring'''






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

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





		def 						A  (		self				:     Dict			)     ->   Union[str, Any]:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =Accelerator()
							_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =create_components()

							(
							    (
							    _SCREAMING_SNAKE_CASE 
							)   ,						(
							    _SCREAMING_SNAKE_CASE 
							)   ,						(
							    _SCREAMING_SNAKE_CASE 
							)   ,						(
							    _SCREAMING_SNAKE_CASE 
							)   ,						(
							    _SCREAMING_SNAKE_CASE 
							)   ,						 
							)							   =accelerator.prepare(__snake_case							, __snake_case							, __snake_case							, __snake_case							, __snake_case			)

							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				:     Tuple			)     ->   List[str]:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =Accelerator()
							_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =create_components()
							accelerator.prepare(__snake_case							, __snake_case							, __snake_case							, __snake_case							, __snake_case			)
							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				:     Optional[Any]			)     ->   Optional[Any]:





							'''simple docstring'''






							PartialState._reset_state()

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

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





		def 						A  (		self				:     Dict			)     ->   Dict:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =Accelerator()
							_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =create_components()
							accelerator.prepare(__snake_case							, __snake_case							, __snake_case							, __snake_case							, __snake_case			)

							_SCREAMING_SNAKE_CASE							   =get_signature(__snake_case			)

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

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

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





		def 						A  (		self				:     Dict			)     ->   Union[str, Any]:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =Accelerator()
							_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =create_components()
							accelerator.prepare(__snake_case							, __snake_case							, __snake_case							, __snake_case							, __snake_case			)

							_SCREAMING_SNAKE_CASE							   =get_signature(__snake_case			)

							# saving hook
							def save_config(_a				:     Optional[int]							, _a				:     Any							, _a				:     List[Any]			):
												_SCREAMING_SNAKE_CASE							   ={'class_name': models[0].__class__.__name__}

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

        # loading hook
							def load_config(_a				:     Any							, _a				:     List[str]			):
												with open(os.path.join(__snake_case							, 'data.json'			)							, 'r'			) as f:
																	_SCREAMING_SNAKE_CASE							   =json.load(__snake_case			)

												_SCREAMING_SNAKE_CASE							   =config['class_name']

							_SCREAMING_SNAKE_CASE							   =accelerator.register_save_state_pre_hook(__snake_case			)
							_SCREAMING_SNAKE_CASE							   =accelerator.register_load_state_pre_hook(__snake_case			)

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

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

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

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

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

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

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

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





		def 						A  (		self				:     int			)     ->   Dict:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =Accelerator()
							_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =create_components()
							_SCREAMING_SNAKE_CASE							   =None

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





		def 						A  (		self				:     List[str]			)     ->   Dict:





							'''simple docstring'''






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

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





		@slow
		@require_bnb
		def 						A  (		self				:     int			)     ->   Tuple:





							'''simple docstring'''






							from transformers import AutoModelForCausalLM

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

							# This should work
							_SCREAMING_SNAKE_CASE							   =accelerator.prepare(__snake_case			)





		@slow
		@require_bnb
		def 						A  (		self				:     Tuple			)     ->   Any:





							'''simple docstring'''






							from transformers import AutoModelForCausalLM

							_SCREAMING_SNAKE_CASE							   =Accelerator()

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

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

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





		@slow
		@require_bnb
		@require_multi_gpu
		def 						A  (		self				:     List[str]			)     ->   Union[str, Any]:





							'''simple docstring'''






							from transformers import AutoModelForCausalLM

							_SCREAMING_SNAKE_CASE							   ={'distributed_type': DistributedType.MULTI_GPU}

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

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

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

							PartialState._reset_state()





		@slow
		@require_bnb
		@require_multi_gpu
		def 						A  (		self				:     Optional[Any]			)     ->   Dict:





							'''simple docstring'''






							from transformers import AutoModelForCausalLM

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

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

							# This should work
							_SCREAMING_SNAKE_CASE							   =accelerator.prepare(__snake_case			)





		@require_cuda
		def 						A  (		self				:     str			)     ->   int:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =torch.nn.Linear(10							, 10			)
							_SCREAMING_SNAKE_CASE							   =torch.optim.SGD(model.parameters()							, lr=0.01			)
							_SCREAMING_SNAKE_CASE							   =Accelerator(cpu=__snake_case			)
							_SCREAMING_SNAKE_CASE							   =accelerator.prepare(__snake_case			)



'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowerCamelCase			:				List[Any] 			=					"\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

lowerCamelCase			:				List[str] 			=					"\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

lowerCamelCase			:				Dict 			=					"\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Dict    )				->      List[str]:
     """simple docstring"""




     return float((preds == labels).mean()    )


def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Any    )				->      Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =simple_accuracy(_UpperCamelCase							,			_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =float(fa_score(y_true=_UpperCamelCase							,			y_pred=_UpperCamelCase    )    )
     return {
         "accuracy": acc,
         "f1": fa,
     }


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					Optional[int]    )				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =float(pearsonr(_UpperCamelCase							,			_UpperCamelCase    )[0]    )
     _SCREAMING_SNAKE_CASE							   =float(spearmanr(_UpperCamelCase							,			_UpperCamelCase    )[0]    )
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
     }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,       _KWARGS_DESCRIPTION  )
class 							A__   (	datasets.Metric  ):





  def 						A  (		self				:     Optional[int]			)     ->   Any:





       '''simple docstring'''






       if self.config_name not in [
           "sst2",
           "mnli",
           "mnli_mismatched",
           "mnli_matched",
           "cola",
           "stsb",
           "mrpc",
           "qqp",
           "qnli",
           "rte",
           "wnli",
           "hans",
       ]:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'			)
       return datasets.MetricInfo(
           description=_DESCRIPTION							, citation=_CITATION							, inputs_description=_KWARGS_DESCRIPTION							, features=datasets.Features(
               {
                   'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'			),
                   'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'			),
               }			)							, codebase_urls=[]							, reference_urls=[]							, format='numpy'							, )





  def 						A  (		self				:     str							, _a				:     str							, _a				:     List[Any]			)     ->   str:





       '''simple docstring'''






       if self.config_name == "cola":
            return {"matthews_correlation": matthews_corrcoef(_a							, _a			)}
       elif self.config_name == "stsb":
            return pearson_and_spearman(_a							, _a			)
       elif self.config_name in ["mrpc", "qqp"]:
            return acc_and_fa(_a							, _a			)
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
            return {"accuracy": simple_accuracy(_a							, _a			)}
       else:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'			)
