
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 		UpperCamelCase__  (	unittest.TestCase   ):
   def 				lowerCAmelCase      (self			: str       ):
    __a			:  List[Any]		    =  torch.nn.Linear(1_0 ,    1_0       )
    __a			:  Optional[int]		    =  torch.optim.SGD(model.parameters() ,    0.1       )
    __a			:  Union[str, Any]		    =  Accelerator()
    __a			:  Dict		    =  accelerator.prepare(snake_case_       )
    try:
     pickle.loads(pickle.dumps(snake_case_       )       )
    except Exception as e:
     self.fail(f"Accelerated optimizer pickling failed with {e}"       )
    AcceleratorState._reset_state()


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

    __a			:  Optional[Any]		    =  model(input_ids.to(snake_case_       ) ,    labels=labels.to(snake_case_       )       ).loss
    __a			:  Tuple		    =  -(labels.shape[-1] * loss.item())

    __a			:  Dict		    =  -84.9127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )
