




"""simple docstring"""





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

import datasets


_lowercase						   =     """\\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"""

_lowercase						   =     """\\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"""


_lowercase						   =     """\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    __lowercase							(      datasets.Metric						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										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[Any]   ,SCREAMING_SNAKE_CASE_ :  str   ):
										if self.config_name == "default":
																snake_case__							:       Union[str, Any]				      =       '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
										else:
																snake_case__							:       Optional[Any]				      =       self.config_name
										snake_case__							:       int				      =       AutoModelForSequenceClassification.from_pretrained(_A   )
										snake_case__							:       str				      =       AutoTokenizer.from_pretrained(_A   )





				def    _lowerCamelCase       ( self :  Union[str, Any]   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  Dict   ,SCREAMING_SNAKE_CASE_ :  str=3_2   ,SCREAMING_SNAKE_CASE_ :  Tuple=1_2_8   ,SCREAMING_SNAKE_CASE_ :  List[str]=None   ,):
										assert len(_A   ) == len(
										    _A   ), "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:
																snake_case__							:       Any				      =       '''gpu''' if torch.cuda.is_available() else '''cpu'''
										snake_case__							:       List[str]				      =       TrainingArguments(
										    '''trainer'''   ,fpaa=(device == '''gpu''')   ,per_device_eval_batch_size=_A   ,report_to='''all'''   ,no_cuda=(device == '''cpu''')   ,log_level='''warning'''   ,)
										snake_case__							:       int				      =       {'''sentence1''': predictions, '''sentence2''': references}
										snake_case__							:       List[str]				      =       datasets.Dataset.from_dict(_A   )

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

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




import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types







class 	lowerCamelCase						(    unittest.TestCase				,					A_    ):





     def 						UpperCAmelCase(self :       Any  )		->		int:
         snake_case						=   load_tool("translation"  )
         self.tool.setup()
         snake_case						=   load_tool("translation"							,       remote=_A  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Optional[int]:
         snake_case						=   self.tool("Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       str  )		->		int:
         snake_case						=   self.remote_tool("Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   self.tool(text="Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		str:
         snake_case						=   self.remote_tool(text="Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       Any  )		->		Tuple:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]
         snake_case						=   self.tool(*_A  )

         # There is a single output
         if len(self.tool.outputs  ) == 1:
             snake_case						=   [outputs]

         self.assertListEqual(output_types(_A  )							,       self.tool.outputs  )





     def 						UpperCAmelCase(self :       List[str]  )		->		List[Any]:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]
         snake_case						=   self.tool(*_A  )

         if not isinstance(_A							,       _A  ):
             snake_case						=   [outputs]

         self.assertEqual(len(_A  )							,       len(self.tool.outputs  )  )

         for output, output_type in zip(_A							,       self.tool.outputs  ):
             snake_case						=   AGENT_TYPE_MAPPING[output_type]
             self.assertTrue(isinstance(_A							,       _A  )  )





     def 						UpperCAmelCase(self :       int  )		->		Any:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]

         snake_case						=   []

         for _input, input_type in zip(_A							,       self.tool.inputs  ):
             if isinstance(_A							,       _A  ):
                 _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input  ) for _input_type in input_type]  )
             else:
                 _inputs.append(AGENT_TYPE_MAPPING[input_type](_input  )  )

        # Should not raise an error
         snake_case						=   self.tool(*_A  )

         if not isinstance(_A							,       _A  ):
             snake_case						=   [outputs]

         self.assertEqual(len(_A  )							,       len(self.tool.outputs  )  )

