


import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




class A  (       UpperCamelCase_					):






      def 	lowerCamelCase	( self						:					Dict      ,       lowercase_						:					List[str]      ,       lowercase_						:					Optional[int]=False			) ->						int:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase	:		Union[str, Any]          =(
                 filename_skip_take['filename'],
                 filename_skip_take['skip'] if 'skip' in filename_skip_take else None,
                 filename_skip_take['take'] if 'take' in filename_skip_take else None,
             )
             open(os.path.join(lowercase_			)      ,       'wb'			).close()
             _lowerCamelCase	:		Optional[Any]          =pa.Table.from_pydict({'filename': [Path(lowercase_			).name] * 100}			)
             if take == -1:
                    _lowerCamelCase	:		List[str]          =len(lowercase_			) - skip
             if skip is not None and take is not None:
                    _lowerCamelCase	:		Union[str, Any]          =pa_table.slice(lowercase_      ,       lowercase_			)
             return pa_table




class A  (       UpperCamelCase_					):






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




             _lowerCamelCase	:		int          ='my_name'
             _lowerCamelCase	:		Tuple          =SplitInfo(name='train'      ,       num_examples=100			)
             _lowerCamelCase	:		Optional[Any]          =SplitInfo(name='test'      ,       num_examples=100			)
             _lowerCamelCase	:		str          =[train_info, test_info]
             _lowerCamelCase	:		Tuple          =SplitDict()
             split_dict.add(lowercase_			)
             split_dict.add(lowercase_			)
             _lowerCamelCase	:		int          =DatasetInfo(splits=lowercase_			)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    _lowerCamelCase	:		Optional[Any]          =ReaderTest(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Optional[Any]          ='test[:33%]'
                    _lowerCamelCase	:		int          =Dataset(**reader.read(lowercase_      ,       lowercase_      ,       lowercase_			)			)
                    self.assertEqual(dset['filename'][0]      ,       F'''{name}-test'''			)
                    self.assertEqual(dset.num_rows      ,       33			)
                    self.assertEqual(dset.num_columns      ,       1			)

                    _lowerCamelCase	:		int          =['train', 'test[:33%]']
                    _lowerCamelCase	:		Any          =[Split.TRAIN, ReadInstruction.from_spec('test[:33%]'			)]
                    for instructions in [instructionsa, instructionsa]:
                           _lowerCamelCase	:		List[Any]          =[reader.read(lowercase_      ,       lowercase_      ,       lowercase_			) for instr in instructions]
                           _lowerCamelCase     ,					_lowerCamelCase	:		Union[str, Any]          =(Dataset(**lowercase_			) for dataset_kwargs in datasets_kwargs)
                           self.assertEqual(train_dset['filename'][0]      ,       F'''{name}-train'''			)
                           self.assertEqual(train_dset.num_rows      ,       100			)
                           self.assertEqual(train_dset.num_columns      ,       1			)
                           self.assertIsInstance(train_dset.split      ,       lowercase_			)
                           self.assertEqual(str(train_dset.split			)      ,       'train'			)
                           self.assertEqual(test_dset['filename'][0]      ,       F'''{name}-test'''			)
                           self.assertEqual(test_dset.num_rows      ,       33			)
                           self.assertEqual(test_dset.num_columns      ,       1			)
                           self.assertIsInstance(test_dset.split      ,       lowercase_			)
                           self.assertEqual(str(test_dset.split			)      ,       'test[:33%]'			)
                           del train_dset, test_dset






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




             _lowerCamelCase	:		Tuple          ='my_name'
             _lowerCamelCase	:		Optional[int]          =SplitInfo(name='train'      ,       num_examples=1000      ,       shard_lengths=[100] * 10			)
             _lowerCamelCase	:		Any          =[train_info]
             _lowerCamelCase	:		Dict          =SplitDict()
             split_dict.add(lowercase_			)
             _lowerCamelCase	:		List[str]          =DatasetInfo(splits=lowercase_			)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    _lowerCamelCase	:		str          =ReaderTest(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Dict          ='train[:33%]'
                    _lowerCamelCase	:		Union[str, Any]          =Dataset(**reader.read(lowercase_      ,       lowercase_      ,       lowercase_			)			)
                    self.assertEqual(dset['filename'][0]      ,       F'''{name}-train-00000-of-00010'''			)
                    self.assertEqual(dset['filename'][-1]      ,       F'''{name}-train-00003-of-00010'''			)
                    self.assertEqual(dset.num_rows      ,       330			)
                    self.assertEqual(dset.num_columns      ,       1			)






      def 	lowerCamelCase	( self						:					Dict			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =SplitInfo(name='train'      ,       num_examples=100			)
             _lowerCamelCase	:		Dict          =SplitInfo(name='test'      ,       num_examples=100			)
             _lowerCamelCase	:		str          =SplitDict()
             split_dict.add(lowercase_			)
             split_dict.add(lowercase_			)
             _lowerCamelCase	:		Optional[int]          =DatasetInfo(splits=lowercase_			)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    _lowerCamelCase	:		Tuple          =ReaderTest(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Union[str, Any]          =[
                        {'filename': os.path.join(lowercase_      ,       'train'			)},
                        {'filename': os.path.join(lowercase_      ,       'test'			), 'skip': 10, 'take': 10},
                    ]
                    _lowerCamelCase	:		int          =Dataset(**reader.read_files(lowercase_      ,       original_instructions='train+test[10:20]'			)			)
                    self.assertEqual(dset.num_rows      ,       110			)
                    self.assertEqual(dset.num_columns      ,       1			)
                    del dset




@pytest.mark.parametrize('in_memory'		,      [False, True]					)
def      a_				(   SCREAMING_SNAKE_CASE__ :				List[Any]		,      SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Any					):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[int]          =arrow_file
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              _lowerCamelCase	:		Optional[int]          =ArrowReader.read_table(SCREAMING_SNAKE_CASE__		,      in_memory=SCREAMING_SNAKE_CASE__					)
       assert table.shape == dataset.data.shape
       assert set(table.column_names					) == set(dataset.data.column_names					)
       assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict




@pytest.mark.parametrize('in_memory'		,      [False, True]					)
def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Union[str, Any]		,      SCREAMING_SNAKE_CASE__ :				Tuple					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =arrow_file
       _lowerCamelCase	:		List[str]          =ArrowReader(''		,      SCREAMING_SNAKE_CASE__					)
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              _lowerCamelCase	:		str          =reader.read_files([{'filename': filename}]		,      in_memory=SCREAMING_SNAKE_CASE__					)
       assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
       _lowerCamelCase	:		List[str]          =dataset_kwargs['arrow_table']
       assert table.shape == dataset.data.shape
       assert set(table.column_names					) == set(dataset.data.column_names					)
       assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict




def      a_				(   ):
       '''simple docstring'''





       assert ReadInstruction('train'		,      to=10		,      unit='abs'					).to_spec() == "train[:10]"
       assert ReadInstruction('train'		,      from_=-80		,      to=10		,      unit='%'					).to_spec() == "train[-80%:10%]"

       _lowerCamelCase	:		Any          ='train+test'
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE__					).to_spec() == spec_train_test

       _lowerCamelCase	:		Optional[Any]          ='train[2:10]'
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE__					).to_spec() == spec_train_abs

       _lowerCamelCase	:		Dict          ='train[15%:-20%]'
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE__					).to_spec() == spec_train_pct

       _lowerCamelCase	:		Tuple          ='train[:10%](closest)'
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE__					).to_spec() == "train[:10%]"

       _lowerCamelCase	:		str          ='train[:10%](pct1_dropremainder)'
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE__					).to_spec() == spec_train_pct_rounding

       _lowerCamelCase	:		Union[str, Any]          ='train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE__					).to_spec() == spec_train_test_pct_rounding




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Any          ='dummy'
       _lowerCamelCase	:		Dict          =[SplitInfo(name='train'		,      num_examples=100					)]
       _lowerCamelCase	:		Any          ='train[:33%]'
       _lowerCamelCase	:		List[Any]          ='arrow'
       _lowerCamelCase	:		Dict          ='prefix'

       _lowerCamelCase	:		Any          =make_file_instructions(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       assert isinstance(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       assert file_instructions.num_examples == 33
       assert file_instructions.file_instructions == [
           {"filename": os.path.join(SCREAMING_SNAKE_CASE__		,      F'''{name}-train.arrow'''					), "skip": 0, "take": 33}
       ]

       _lowerCamelCase	:		int          =[SplitInfo(name='train'		,      num_examples=100		,      shard_lengths=[10] * 10					)]
       _lowerCamelCase	:		Any          =make_file_instructions(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       assert isinstance(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       assert file_instructions.num_examples == 33
       assert file_instructions.file_instructions == [
           {"filename": os.path.join(SCREAMING_SNAKE_CASE__		,      F'''{name}-train-00000-of-00010.arrow'''					), "skip": 0, "take": -1},
           {"filename": os.path.join(SCREAMING_SNAKE_CASE__		,      F'''{name}-train-00001-of-00010.arrow'''					), "skip": 0, "take": -1},
           {"filename": os.path.join(SCREAMING_SNAKE_CASE__		,      F'''{name}-train-00002-of-00010.arrow'''					), "skip": 0, "take": -1},
           {"filename": os.path.join(SCREAMING_SNAKE_CASE__		,      F'''{name}-train-00003-of-00010.arrow'''					), "skip": 0, "take": 3},
       ]




@pytest.mark.parametrize('name, expected_exception'		,      [(None, TypeError), ('', ValueError)]					)
def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[int]		,      SCREAMING_SNAKE_CASE__ :				int					):
       '''simple docstring'''





       _lowerCamelCase	:		str          =[SplitInfo(name='train'		,      num_examples=100					)]
       _lowerCamelCase	:		str          ='train'
       _lowerCamelCase	:		Tuple          ='arrow'
       _lowerCamelCase	:		List[str]          ='prefix_path'
       with pytest.raises(SCREAMING_SNAKE_CASE__					):
              _lowerCamelCase	:		Dict          =make_file_instructions(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)






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			)			)}


