



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

   return model, optimizer, scheduler, train_dl, valid_dl
def 		UpperCamelCase	(   snake_case__       :    Tuple	)	->     Dict:
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def 		UpperCamelCase	(   snake_case__       :    List[Any]	)	->     List[str]:
   UpperCamelCase    :  Optional[Any]							=	torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
   model.load_state_dict(snake_case__	)



class 				lowerCAmelCase_		(  a__							):
       @require_cuda
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Dict							=	Accelerator()
          assert PartialState._shared_state["_cpu"] is False
          assert PartialState._shared_state["device"].type == "cuda"
          with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  List[str]							=	Accelerator(cpu=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Tuple							=	Accelerator()
          UpperCamelCase    :  Optional[int]							=	GradientState()
          assert state.num_steps == 1
          UpperCamelCase    :  Tuple							=	4
          assert state.num_steps == 4

          assert state.sync_gradients is True
          UpperCamelCase    :  Optional[int]							=	False
          assert state.sync_gradients is False
          GradientState._reset_state()
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Optional[int]							=	Accelerator()
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  List[Any]							=	create_components()

          (
              (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     
          )    :  Union[str, Any]							=	accelerator.prepare(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_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 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  str							=	Accelerator()
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  str							=	create_components()
          accelerator.prepare(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_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 					snake_case_       (			self       )     ->		List[str]:
          PartialState._reset_state()

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

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

          UpperCamelCase    :  int							=	get_signature(SCREAMING_SNAKE_CASE_       )

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

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

             # make sure loaded weights match
             accelerator.load_state(SCREAMING_SNAKE_CASE_       )
             self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE_       )       ) < 1e-3       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[int]							=	Accelerator()
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	create_components()
          accelerator.prepare(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[Any]							=	get_signature(SCREAMING_SNAKE_CASE_       )

          # saving hook
          def save_config(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  List[str]							=	{'class_name': models[0].__class__.__name__}

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

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

             UpperCamelCase    :  Optional[int]							=	config['class_name']

          UpperCamelCase    :  Optional[Any]							=	accelerator.register_save_state_pre_hook(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	accelerator.register_load_state_pre_hook(SCREAMING_SNAKE_CASE_       )

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

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

             # random class name to verify correct one is loaded
             UpperCamelCase    :  List[Any]							=	'random'

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

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

             # random class name to verify correct one is loaded
             UpperCamelCase    :  Optional[int]							=	'random'

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

             # mode.class_name is NOT loaded from config
             self.assertTrue(model.class_name != model.__class__.__name__       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	Accelerator()
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Tuple							=	create_components()
          UpperCamelCase    :  Any							=	None

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

          # This should work
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	accelerator.prepare(
              SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          self.assertEqual(
              getattr(SCREAMING_SNAKE_CASE_,			'_is_accelerate_prepared',			SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_,			'Dummy object should have `_is_accelerate_prepared` set to `True`',			)
          self.assertEqual(
              getattr(SCREAMING_SNAKE_CASE_,			'_is_accelerate_prepared',			SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_,			'Model is missing `_is_accelerator_prepared` or is set to `False`',			)
          self.assertEqual(
              getattr(SCREAMING_SNAKE_CASE_,			'_is_accelerate_prepared',			SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_,			'Optimizer is missing `_is_accelerator_prepared` or is set to `False`',			)
          self.assertEqual(
              getattr(SCREAMING_SNAKE_CASE_,			'_is_accelerate_prepared',			SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_,			'Scheduler is missing `_is_accelerator_prepared` or is set to `False`',			)
          self.assertEqual(
              getattr(SCREAMING_SNAKE_CASE_,			'_is_accelerate_prepared',			SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_,			'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`',			)
          self.assertEqual(
              getattr(SCREAMING_SNAKE_CASE_,			'_is_accelerate_prepared',			SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_,			'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`',			)
       @slow
       @require_bnb
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          from transformers import AutoModelForCausalLM

          UpperCamelCase    :  Tuple							=	AutoModelForCausalLM.from_pretrained(
              'EleutherAI/gpt-neo-125m',			load_in_abit=SCREAMING_SNAKE_CASE_,			device_map={'': 0},			)
          UpperCamelCase    :  Dict							=	Accelerator()

          # This should work
          UpperCamelCase    :  List[Any]							=	accelerator.prepare(SCREAMING_SNAKE_CASE_       )
       @slow
       @require_bnb
       def 					snake_case_       (			self       )     ->		List[Any]:
          from transformers import AutoModelForCausalLM

          UpperCamelCase    :  Dict							=	Accelerator()

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

          UpperCamelCase    :  Union[str, Any]							=	AutoModelForCausalLM.from_pretrained(
              'EleutherAI/gpt-neo-125m',			device_map=SCREAMING_SNAKE_CASE_,			load_in_abit=SCREAMING_SNAKE_CASE_,			llm_inta_enable_fpaa_cpu_offload=SCREAMING_SNAKE_CASE_       )

          # This should not work and get value error
          with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Union[str, Any]							=	accelerator.prepare(SCREAMING_SNAKE_CASE_       )
       @slow
       @require_bnb
       @require_multi_gpu
       def 					snake_case_       (			self       )     ->		List[str]:
          from transformers import AutoModelForCausalLM

          UpperCamelCase    :  Tuple							=	{'distributed_type': DistributedType.MULTI_GPU}

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

          UpperCamelCase    :  Optional[int]							=	AutoModelForCausalLM.from_pretrained(
              'EleutherAI/gpt-neo-125m',			load_in_abit=SCREAMING_SNAKE_CASE_,			device_map=SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  Tuple							=	Accelerator()

          # This should not work and get value error
          with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Optional[int]							=	accelerator.prepare(SCREAMING_SNAKE_CASE_       )

          PartialState._reset_state()
       @slow
       @require_bnb
       @require_multi_gpu
       def 					snake_case_       (			self       )     ->		List[str]:
          from transformers import AutoModelForCausalLM

          with init_empty_weights():
             UpperCamelCase    :  Optional[int]							=	AutoModelForCausalLM.from_pretrained(
                 'EleutherAI/gpt-neo-125m',			)
             UpperCamelCase    :  Dict							=	infer_auto_device_map(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Union[str, Any]							=	1

          UpperCamelCase    :  int							=	AutoModelForCausalLM.from_pretrained(
              'EleutherAI/gpt-neo-125m',			load_in_abit=SCREAMING_SNAKE_CASE_,			device_map=SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  Optional[Any]							=	Accelerator()

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






       @require_cuda
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  str							=	torch.nn.Linear(10,			10       )
          UpperCamelCase    :  Optional[int]							=	torch.optim.SGD(model.parameters(),			lr=0.01       )
          UpperCamelCase    :  str							=	Accelerator(cpu=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	accelerator.prepare(SCREAMING_SNAKE_CASE_       )








import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__UpperCAmelCase          =       get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
    __UpperCAmelCase          =       json.load(f)



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          if torch_device == "cuda":
             model.half()
          return model






       @parameterized.expand(
           [
               ['en-ru', 26.0],
               ['ru-en', 22.0],
               ['en-de', 22.0],
               ['de-en', 29.0],
           ]       )
       @slow
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCamelCase    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

          UpperCamelCase    :  List[Any]							=	tokenizer(SCREAMING_SNAKE_CASE_,			return_tensors='pt',			truncation=SCREAMING_SNAKE_CASE_,			padding='longest'       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	model.generate(
              input_ids=batch.input_ids,			num_beams=8,			)
          UpperCamelCase    :  Tuple							=	tokenizer.batch_decode(
              SCREAMING_SNAKE_CASE_,			skip_special_tokens=SCREAMING_SNAKE_CASE_,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	calculate_bleu(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          print(SCREAMING_SNAKE_CASE_       )
          self.assertGreaterEqual(scores['bleu'],			SCREAMING_SNAKE_CASE_       )



