


"""simple docstring"""




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,
)


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




class A  (       unittest.TestCase					):






      def 	lowerCamelCase	( self						:					Any			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =torch.randn(5      ,       2			)
             _lowerCamelCase	:		Optional[int]          =torch.device('cuda'			) if torch.cuda.is_available() else torch.device('cpu'			)

             _lowerCamelCase	:		Any          =send_to_device(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)
             self.assertTrue(torch.equal(resulta.cpu()      ,       __SCREAMING_SNAKE_CASE			)			)

             _lowerCamelCase	:		str          =send_to_device((tensor, [tensor, tensor], 1)      ,       __SCREAMING_SNAKE_CASE			)
             self.assertIsInstance(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)
             self.assertTrue(torch.equal(resulta[0].cpu()      ,       __SCREAMING_SNAKE_CASE			)			)
             self.assertIsInstance(resulta[1]      ,       __SCREAMING_SNAKE_CASE			)
             self.assertTrue(torch.equal(resulta[1][0].cpu()      ,       __SCREAMING_SNAKE_CASE			)			)
             self.assertTrue(torch.equal(resulta[1][1].cpu()      ,       __SCREAMING_SNAKE_CASE			)			)
             self.assertEqual(resulta[2]      ,       1			)

             _lowerCamelCase	:		Tuple          =send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}      ,       __SCREAMING_SNAKE_CASE			)
             self.assertIsInstance(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)
             self.assertTrue(torch.equal(resulta['a'].cpu()      ,       __SCREAMING_SNAKE_CASE			)			)
             self.assertIsInstance(resulta['b']      ,       __SCREAMING_SNAKE_CASE			)
             self.assertTrue(torch.equal(resulta['b'][0].cpu()      ,       __SCREAMING_SNAKE_CASE			)			)
             self.assertTrue(torch.equal(resulta['b'][1].cpu()      ,       __SCREAMING_SNAKE_CASE			)			)
             self.assertEqual(resulta['c']      ,       1			)

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

             _lowerCamelCase	:		Union[str, Any]          =send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}			)      ,       __SCREAMING_SNAKE_CASE			)
             self.assertIsInstance(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)
             self.assertTrue(torch.equal(resulta['a'].cpu()      ,       __SCREAMING_SNAKE_CASE			)			)
             self.assertIsInstance(resulta['b']      ,       __SCREAMING_SNAKE_CASE			)
             self.assertTrue(torch.equal(resulta['b'][0].cpu()      ,       __SCREAMING_SNAKE_CASE			)			)
             self.assertTrue(torch.equal(resulta['b'][1].cpu()      ,       __SCREAMING_SNAKE_CASE			)			)
             self.assertEqual(resulta['c']      ,       1			)






      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Optional[Any]:
             """simple docstring"""




             with self.assertRaises(__SCREAMING_SNAKE_CASE			) as cm:
                    _lowerCamelCase	:		Dict          =recursively_apply(torch.tensor      ,       (torch.tensor(1			), 1)      ,       error_on_other_type=__SCREAMING_SNAKE_CASE			)
             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 	lowerCamelCase	( self						:					Optional[Any]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		int          =torch.tensor([1, 2, 3, 4, 5]			)
             self.assertEqual(listify(__SCREAMING_SNAKE_CASE			)      ,       [1, 2, 3, 4, 5]			)

             _lowerCamelCase	:		Dict          =torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
             self.assertEqual(listify(__SCREAMING_SNAKE_CASE			)      ,       [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)

             _lowerCamelCase	:		Union[str, Any]          =torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)
             self.assertEqual(
                 listify(__SCREAMING_SNAKE_CASE			)      ,       [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)






      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						Optional[Any]:
             """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 	lowerCamelCase	( self						:					List[Any]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		str          =RegressionModel()
             _lowerCamelCase	:		Optional[int]          =model.forward
             _lowerCamelCase	:		int          =convert_outputs_to_fpaa(model.forward			)
             _lowerCamelCase	:		str          =extract_model_from_parallel(__SCREAMING_SNAKE_CASE      ,       keep_fpaa_wrapper=__SCREAMING_SNAKE_CASE			)
             _lowerCamelCase	:		List[Any]          =pickle.dumps(__SCREAMING_SNAKE_CASE			)






      @require_cuda
      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =RegressionModel()
             _lowerCamelCase	:		List[Any]          =model.forward
             _lowerCamelCase	:		Tuple          =torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
             _lowerCamelCase	:		str          =convert_outputs_to_fpaa(model.forward			)
             _lowerCamelCase	:		Union[str, Any]          =extract_model_from_parallel(__SCREAMING_SNAKE_CASE      ,       keep_fpaa_wrapper=__SCREAMING_SNAKE_CASE			)
             _lowerCamelCase	:		str          =pickle.dumps(__SCREAMING_SNAKE_CASE			)






      @require_cuda
      @require_torch_min_version(version='2.0'			)
      def 	lowerCamelCase	( self						:					Dict			) ->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =RegressionModel()
             _lowerCamelCase	:		Dict          =model.forward
             _lowerCamelCase	:		List[str]          =torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
             _lowerCamelCase	:		Optional[Any]          =convert_outputs_to_fpaa(model.forward			)
             _lowerCamelCase	:		int          =torch.compile(model.forward      ,       backend='inductor'			)
             _lowerCamelCase	:		List[str]          =torch.randn(4      ,       10			).cuda()
             _lowerCamelCase	:		Optional[Any]          =model(__SCREAMING_SNAKE_CASE			)






      def 	lowerCamelCase	( self						:					Any			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =RegressionModel()
             # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
             _lowerCamelCase	:		Dict          =torch.nn.parallel.DataParallel(__SCREAMING_SNAKE_CASE			)
             _lowerCamelCase	:		str          =extract_model_from_parallel(__SCREAMING_SNAKE_CASE			)

             self.assertEqual(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)






      @require_torch_min_version(version='2.0'			)
      def 	lowerCamelCase	( self						:					List[str]			) ->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =RegressionModel()
             _lowerCamelCase	:		Union[str, Any]          =torch.compile(__SCREAMING_SNAKE_CASE			)

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

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






      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						Optional[Any]:
             """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


lowerCamelCase						 =	'\\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'

lowerCamelCase						 =	'\\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'


lowerCamelCase						 =	'\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 A  (       datasets.Metric					):






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """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 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




             if self.config_name == "default":
                    _lowerCamelCase	:		Optional[int]          ='moussaKam/frugalscore_tiny_bert-base_bert-score'
             else:
                    _lowerCamelCase	:		List[Any]          =self.config_name
             _lowerCamelCase	:		str          =AutoModelForSequenceClassification.from_pretrained(lowercase_			)
             _lowerCamelCase	:		str          =AutoTokenizer.from_pretrained(lowercase_			)






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]=32      ,       lowercase_						:					int=128      ,       lowercase_						:					Tuple=None      ,       ) ->						str:
             """simple docstring"""




             assert len(lowercase_			) == len(
                 lowercase_			), "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:
                    _lowerCamelCase	:		List[str]          ='gpu' if torch.cuda.is_available() else 'cpu'
             _lowerCamelCase	:		List[str]          =TrainingArguments(
                 'trainer'      ,       fpaa=(device == 'gpu')      ,       per_device_eval_batch_size=lowercase_      ,       report_to='all'      ,       no_cuda=(device == 'cpu')      ,       log_level='warning'      ,       )
             _lowerCamelCase	:		str          ={'sentence1': predictions, 'sentence2': references}
             _lowerCamelCase	:		Tuple          =datasets.Dataset.from_dict(lowercase_			)

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

             _lowerCamelCase	:		Dict          =raw_datasets.map(lowercase_      ,       batched=lowercase_			)
             tokenized_datasets.remove_columns(['sentence1', 'sentence2']			)
             _lowerCamelCase	:		Union[str, Any]          =Trainer(self.model      ,       lowercase_      ,       tokenizer=self.tokenizer			)
             _lowerCamelCase	:		List[str]          =trainer.predict(lowercase_			)
             return {"scores": list(predictions.predictions.squeeze(-1			)			)}


