
import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


UpperCAmelCase__      =   namedtuple('ExampleNamedTuple', 'a b c')






class UpperCAmelCase     (		unittest.TestCase     ):





   '''simple docstring'''


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						str	    = torch.randn(5    ,    2  )
      _A:						str	    = torch.device('''cuda'''  ) if torch.cuda.is_available() else torch.device('''cpu'''  )

      _A:						List[Any]	    = send_to_device(lowerCAmelCase_    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta.cpu()    ,    lowerCAmelCase_  )  )

      _A:						Dict	    = send_to_device((tensor, [tensor, tensor], 1)    ,    lowerCAmelCase_  )
      self.assertIsInstance(lowerCAmelCase_    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta[0].cpu()    ,    lowerCAmelCase_  )  )
      self.assertIsInstance(resulta[1]    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta[1][0].cpu()    ,    lowerCAmelCase_  )  )
      self.assertTrue(torch.equal(resulta[1][1].cpu()    ,    lowerCAmelCase_  )  )
      self.assertEqual(resulta[2]    ,    1  )

      _A:						Optional[int]	    = send_to_device({'''a''': tensor, '''b''': [tensor, tensor], '''c''': 1}    ,    lowerCAmelCase_  )
      self.assertIsInstance(lowerCAmelCase_    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta['''a'''].cpu()    ,    lowerCAmelCase_  )  )
      self.assertIsInstance(resulta['''b''']    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta['''b'''][0].cpu()    ,    lowerCAmelCase_  )  )
      self.assertTrue(torch.equal(resulta['''b'''][1].cpu()    ,    lowerCAmelCase_  )  )
      self.assertEqual(resulta['''c''']    ,    1  )

      _A:						List[Any]	    = send_to_device(ExampleNamedTuple(a=lowerCAmelCase_    ,    b=[tensor, tensor]    ,    c=1  )    ,    lowerCAmelCase_  )
      self.assertIsInstance(lowerCAmelCase_    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta.a.cpu()    ,    lowerCAmelCase_  )  )
      self.assertIsInstance(resulta.b    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta.b[0].cpu()    ,    lowerCAmelCase_  )  )
      self.assertTrue(torch.equal(resulta.b[1].cpu()    ,    lowerCAmelCase_  )  )
      self.assertEqual(resulta.c    ,    1  )

      _A:						Optional[int]	    = send_to_device(UserDict({'''a''': tensor, '''b''': [tensor, tensor], '''c''': 1}  )    ,    lowerCAmelCase_  )
      self.assertIsInstance(lowerCAmelCase_    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta['''a'''].cpu()    ,    lowerCAmelCase_  )  )
      self.assertIsInstance(resulta['''b''']    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta['''b'''][0].cpu()    ,    lowerCAmelCase_  )  )
      self.assertTrue(torch.equal(resulta['''b'''][1].cpu()    ,    lowerCAmelCase_  )  )
      self.assertEqual(resulta['''c''']    ,    1  )


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      with self.assertRaises(lowerCAmelCase_  ) as cm:
         _A:						Tuple	    = recursively_apply(torch.tensor    ,    (torch.tensor(1  ), 1)    ,    error_on_other_type=lowerCAmelCase_  )
      self.assertEqual(
          str(cm.exception  )    ,    '''Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'''    ,    )


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A:						Any	    = torch.tensor([1, 2, 3, 4, 5]  )
      self.assertEqual(listify(lowerCAmelCase_  )    ,    [1, 2, 3, 4, 5]  )

      _A:						Optional[int]	    = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]  )
      self.assertEqual(listify(lowerCAmelCase_  )    ,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]  )

      _A:						Tuple	    = torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]  )
      self.assertEqual(
          listify(lowerCAmelCase_  )    ,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]  )


   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      with patch_environment(aa=1    ,    BB=2  ):
         self.assertEqual(os.environ.get('''AA'''  )    ,    '''1'''  )
         self.assertEqual(os.environ.get('''BB'''  )    ,    '''2'''  )

      self.assertNotIn('''AA'''    ,    os.environ  )
      self.assertNotIn('''BB'''    ,    os.environ  )


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A:						Dict	    = RegressionModel()
      _A:						Any	    = model.forward
      _A:						Optional[Any]	    = convert_outputs_to_fpaa(model.forward  )
      _A:						int	    = extract_model_from_parallel(lowerCAmelCase_    ,    keep_fpaa_wrapper=lowerCAmelCase_  )
      _A:						Any	    = pickle.dumps(lowerCAmelCase_  )


   @require_cuda
   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Dict	    = RegressionModel()
      _A:						Tuple	    = model.forward
      _A:						int	    = torch.cuda.amp.autocast(dtype=torch.floataa  )(model.forward  )
      _A:						Dict	    = convert_outputs_to_fpaa(model.forward  )
      _A:						str	    = extract_model_from_parallel(lowerCAmelCase_    ,    keep_fpaa_wrapper=lowerCAmelCase_  )
      _A:						List[str]	    = pickle.dumps(lowerCAmelCase_  )


   @require_cuda
   @require_torch_min_version(version='''2.0'''  )
   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      _A:						List[Any]	    = RegressionModel()
      _A:						Dict	    = model.forward
      _A:						Optional[int]	    = torch.cuda.amp.autocast(dtype=torch.floataa  )(model.forward  )
      _A:						Optional[int]	    = convert_outputs_to_fpaa(model.forward  )
      _A:						List[Any]	    = torch.compile(model.forward    ,    backend='''inductor'''  )
      _A:						List[str]	    = torch.randn(4    ,    1_0  ).cuda()
      _A:						List[Any]	    = model(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      _A:						int	    = RegressionModel()
      # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
      _A:						Optional[Any]	    = torch.nn.parallel.DataParallel(lowerCAmelCase_  )
      _A:						List[str]	    = extract_model_from_parallel(lowerCAmelCase_  )

      self.assertEqual(lowerCAmelCase_    ,    lowerCAmelCase_  )


   @require_torch_min_version(version='''2.0'''  )
   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = RegressionModel()
      _A:						str	    = torch.compile(lowerCAmelCase_  )

      # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
      _A:						Any	    = torch.nn.parallel.DataParallel(lowerCAmelCase_  )
      _A:						Optional[Any]	    = torch.compile(lowerCAmelCase_  )
      _A:						Union[str, Any]	    = extract_model_from_parallel(lowerCAmelCase_  )

      self.assertEqual(compiled_model._orig_mod    ,    compiled_model_unwrapped._orig_mod  )


   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      self.assertEqual(find_device([1, '''a''', torch.tensor([1, 2, 3]  )]  )    ,    torch.device('''cpu'''  )  )
      self.assertEqual(find_device({'''a''': 1, '''b''': torch.tensor([1, 2, 3]  )}  )    ,    torch.device('''cpu'''  )  )
      self.assertIsNone(find_device([1, '''a''']  )  )





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__	:  Optional[Any]      =   '\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

UpperCAmelCase__	:  List[Any]      =   '\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


UpperCAmelCase__	:  List[str]      =   '\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'






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





   '''simple docstring'''


   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,    citation=_CITATION    ,    inputs_description=_KWARGS_DESCRIPTION    ,    features=datasets.Features(
              {
                  '''predictions''': datasets.Value('''string'''  ),
                  '''references''': datasets.Value('''string'''  ),
              }  )    ,    homepage='''https://github.com/moussaKam/FrugalScore'''    ,    )


   def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							int  ):

      """simple docstring"""
      if self.config_name == "default":
         _A:						Dict	    = '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
      else:
         _A:						str	    = self.config_name
      _A:						Any	    = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_  )
      _A:						Tuple	    = AutoTokenizer.from_pretrained(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Dict    ,    lowerCAmelCase_  :							Dict    ,    lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							Any=3_2    ,    lowerCAmelCase_  :							str=1_2_8    ,    lowerCAmelCase_  :							str=None    ,    ):

      """simple docstring"""
      assert len(lowerCAmelCase_  ) == len(
          lowerCAmelCase_  ), "predictions and references should have the same number of sentences."
      if device is not None:
         assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
      else:
         _A:						Union[str, Any]	    = '''gpu''' if torch.cuda.is_available() else '''cpu'''
      _A:						Optional[int]	    = TrainingArguments(
          '''trainer'''    ,    fpaa=(device == '''gpu''')    ,    per_device_eval_batch_size=lowerCAmelCase_    ,    report_to='''all'''    ,    no_cuda=(device == '''cpu''')    ,    log_level='''warning'''    ,    )
      _A:						Optional[int]	    = {'''sentence1''': predictions, '''sentence2''': references}
      _A:						Dict	    = datasets.Dataset.from_dict(lowerCAmelCase_  )

      def tokenize_function(lowerCAmelCase_  :							Any  ):
         return self.tokenizer(
             data['''sentence1''']    ,    data['''sentence2''']    ,    max_length=lowerCAmelCase_    ,    truncation=lowerCAmelCase_    ,    padding=lowerCAmelCase_  )

      _A:						Dict	    = raw_datasets.map(lowerCAmelCase_    ,    batched=lowerCAmelCase_  )
      tokenized_datasets.remove_columns(['''sentence1''', '''sentence2''']  )
      _A:						Optional[int]	    = Trainer(self.model    ,    lowerCAmelCase_    ,    tokenizer=self.tokenizer  )
      _A:						int	    = trainer.predict(lowerCAmelCase_  )
      return {"scores": list(predictions.predictions.squeeze(-1  )  )}



