




'''simple docstring'''







import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil


class     _a						(			__a				):
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	pa.array(TypedSequence([1, 2, 3]       )       )
           self.assertEqual(arr.type     ,       pa.intaa()       )
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           with self.assertRaises(lowercase       ):
               UpperCAmelCase     =	pa.array(TypedSequence([1, 2, 3]       )     ,       type=pa.intaa()       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           with self.assertRaises(lowercase       ):
               UpperCAmelCase     =	pa.array(TypedSequence([1, 2, 3]     ,       try_type=Value('''bool'''       )     ,       type=Value('''int64'''       )       )       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	pa.array(TypedSequence([1, 2, 3]     ,       type=Value('''int32'''       )       )       )
           self.assertEqual(arr.type     ,       pa.intaa()       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           with self.assertRaises((TypeError, pa.lib.ArrowInvalid)       ):
               UpperCAmelCase     =	pa.array(TypedSequence(['''foo''', '''bar''']     ,       type=Value('''int64'''       )       )       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	pa.array(TypedSequence([1, 2, 3]     ,       try_type=Value('''int32'''       )       )       )
           self.assertEqual(arr.type     ,       pa.intaa()       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	pa.array(TypedSequence(['''foo''', '''bar''']     ,       try_type=Value('''int64'''       )       )       )
           self.assertEqual(arr.type     ,       pa.string()       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	pa.array(TypedSequence([[[1, 2, 3]]]     ,       type=ArrayaD((1, 3)     ,       '''int64'''       )       )       )
           self.assertEqual(arr.type     ,       ArrayaDExtensionType((1, 3)     ,       '''int64'''       )       )
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           with self.assertRaises((TypeError, pa.lib.ArrowInvalid)       ):
               UpperCAmelCase     =	pa.array(TypedSequence(['''foo''', '''bar''']     ,       type=ArrayaD((1, 3)     ,       '''int64'''       )       )       )
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	pa.array(TypedSequence([[[1, 2, 3]]]     ,       try_type=ArrayaD((1, 3)     ,       '''int64'''       )       )       )
           self.assertEqual(arr.type     ,       ArrayaDExtensionType((1, 3)     ,       '''int64'''       )       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	pa.array(TypedSequence(['''foo''', '''bar''']     ,       try_type=ArrayaD((1, 3)     ,       '''int64'''       )       )       )
           self.assertEqual(arr.type     ,       pa.string()       )







       @require_pil
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           import PIL.Image

           UpperCAmelCase     =	PIL.Image.fromarray(np.arange(10     ,       dtype=np.uinta       ).reshape(2     ,       5       )       )
           with patch(
               '''datasets.arrow_writer.cast_to_python_objects'''     ,       side_effect=lowercase       ) as mock_cast_to_python_objects:
               UpperCAmelCase     =	pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image]     ,       type=Image()       )       )
               UpperCAmelCase ,  UpperCAmelCase     =	mock_cast_to_python_objects.call_args_list[-1]
               self.assertIn('''optimize_list_casting'''     ,       lowercase       )
               self.assertFalse(kwargs['''optimize_list_casting''']       )






def 		snake_case_							(_a			:							Optional[int]					,     _a			:							int  ):
    UpperCAmelCase     =	pa.BufferReader(_a  ) if isinstance(_a					,     pa.Buffer  ) else pa.memory_map(_a  )
    UpperCAmelCase     =	pa.ipc.open_stream(_a  )
    UpperCAmelCase     =	f.read_all()
    assert len(pa_table.to_batches()  ) == expected_num_chunks
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
    del pa_table





@pytest.mark.parametrize('''writer_batch_size'''					,     [None, 1, 1_0]  )
@pytest.mark.parametrize(
    '''fields'''					,     [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]  )
def 		snake_case_							(_a			:							Any					,     _a			:							Optional[int]  ):
    UpperCAmelCase     =	pa.BufferOutputStream()
    UpperCAmelCase     =	pa.schema(_a  ) if fields else None
    with ArrowWriter(stream=_a					,     schema=_a					,     writer_batch_size=_a  ) as writer:
        writer.write({'''col_1''': '''foo''', '''col_2''': 1}  )
        writer.write({'''col_1''': '''bar''', '''col_2''': 2}  )
        UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        UpperCAmelCase     =	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
    assert writer._schema == pa.schema(_a					,     metadata=writer._schema.metadata  )
    _check_output(output.getvalue()					,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





def 		snake_case_							():
    UpperCAmelCase     =	pa.BufferOutputStream()
    UpperCAmelCase     =	Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']  )}  )
    with ArrowWriter(stream=_a					,     features=_a  ) as writer:
        writer.write({'''labels''': 0}  )
        writer.write({'''labels''': 1}  )
        UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == features.arrow_schema
    assert writer._schema.metadata == features.arrow_schema.metadata
    UpperCAmelCase     =	pa.BufferReader(output.getvalue()  )
    UpperCAmelCase     =	pa.ipc.open_stream(_a  )
    UpperCAmelCase     =	f.read_all()
    UpperCAmelCase     =	pa_table.schema
    assert pa_table.num_rows == 2
    assert schema == features.arrow_schema
    assert schema.metadata == features.arrow_schema.metadata
    assert features == Features.from_arrow_schema(_a  )





@pytest.mark.parametrize('''writer_batch_size'''					,     [None, 1, 1_0]  )
def 		snake_case_							(_a			:							Dict  ):
    UpperCAmelCase     =	pa.BufferOutputStream()
    with ArrowWriter(
        stream=_a					,     writer_batch_size=_a					,     hash_salt='''split_name'''					,     check_duplicates=_a					,     ) as writer:
        with pytest.raises(_a  ):
            writer.write({'''col_1''': '''foo''', '''col_2''': 1}					,     key=[1, 2]  )
            UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()





@pytest.mark.parametrize('''writer_batch_size'''					,     [None, 2, 1_0]  )
def 		snake_case_							(_a			:							Tuple  ):
    UpperCAmelCase     =	pa.BufferOutputStream()
    with ArrowWriter(
        stream=_a					,     writer_batch_size=_a					,     hash_salt='''split_name'''					,     check_duplicates=_a					,     ) as writer:
        with pytest.raises(_a  ):
            writer.write({'''col_1''': '''foo''', '''col_2''': 1}					,     key=1_0  )
            writer.write({'''col_1''': '''bar''', '''col_2''': 2}					,     key=1_0  )
            UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()





@pytest.mark.parametrize('''writer_batch_size'''					,     [None, 2, 1_0]  )
def 		snake_case_							(_a			:							Optional[int]  ):
    UpperCAmelCase     =	pa.BufferOutputStream()
    with ArrowWriter(
        stream=_a					,     writer_batch_size=_a					,     hash_salt='''split_name'''					,     check_duplicates=_a					,     ) as writer:
        writer.write({'''col_1''': '''foo''', '''col_2''': 1}					,     key=1  )
        writer.write({'''col_1''': '''bar''', '''col_2''': 2}					,     key=2  )
        UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    _check_output(output.getvalue()					,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





@pytest.mark.parametrize('''writer_batch_size'''					,     [None, 1, 1_0]  )
@pytest.mark.parametrize(
    '''fields'''					,     [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]  )
def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Dict  ):
    UpperCAmelCase     =	pa.BufferOutputStream()
    UpperCAmelCase     =	pa.schema(_a  ) if fields else None
    with ArrowWriter(stream=_a					,     schema=_a					,     writer_batch_size=_a  ) as writer:
        writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}  )
        writer.write_batch({'''col_1''': [], '''col_2''': []}  )
        UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        UpperCAmelCase     =	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
    assert writer._schema == pa.schema(_a					,     metadata=writer._schema.metadata  )
    _check_output(output.getvalue()					,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





@pytest.mark.parametrize('''writer_batch_size'''					,     [None, 1, 1_0]  )
@pytest.mark.parametrize(
    '''fields'''					,     [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]  )
def 		snake_case_							(_a			:							Optional[int]					,     _a			:							Union[str, Any]  ):
    UpperCAmelCase     =	pa.BufferOutputStream()
    UpperCAmelCase     =	pa.schema(_a  ) if fields else None
    with ArrowWriter(stream=_a					,     schema=_a					,     writer_batch_size=_a  ) as writer:
        writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}  )  )
        UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        UpperCAmelCase     =	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
    assert writer._schema == pa.schema(_a					,     metadata=writer._schema.metadata  )
    _check_output(output.getvalue()					,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





@pytest.mark.parametrize('''writer_batch_size'''					,     [None, 1, 1_0]  )
@pytest.mark.parametrize(
    '''fields'''					,     [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]  )
def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							int  ):
    UpperCAmelCase     =	pa.BufferOutputStream()
    UpperCAmelCase     =	pa.schema(_a  ) if fields else None
    with ArrowWriter(stream=_a					,     schema=_a					,     writer_batch_size=_a  ) as writer:
        writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]}  )  )
        writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]}  )  )
        UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        UpperCAmelCase     =	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
    assert writer._schema == pa.schema(_a					,     metadata=writer._schema.metadata  )
    _check_output(output.getvalue()					,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





def 		snake_case_							():
    with tempfile.TemporaryDirectory() as tmp_dir:
        UpperCAmelCase     =	{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
        UpperCAmelCase     =	os.path.join(_a					,     '''test.arrow'''  )
        with ArrowWriter(path=_a					,     schema=pa.schema(_a  )  ) as writer:
            writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}  )
            UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()
        assert num_examples == 2
        assert num_bytes > 0
        assert writer._schema == pa.schema(_a					,     metadata=writer._schema.metadata  )
        _check_output(_a					,     1  )





def 		snake_case_							(_a			:							str  ):
    if pa.types.is_list(_a  ):
        return get_base_dtype(arr_type.value_type  )
    else:
        return arr_type





def 		snake_case_							(_a			:							int					,     _a			:							Optional[int]  ):
    if isinstance(lst[0]					,     _a  ):
        change_first_primitive_element_in_list(lst[0]					,     _a  )
    else:
        UpperCAmelCase     =	value





@pytest.mark.parametrize('''optimized_int_type, expected_dtype'''					,     [(None, pa.intaa()), (Value('''int32'''  ), pa.intaa())]  )
@pytest.mark.parametrize('''sequence'''					,     [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]  )
def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							str					,     _a			:							Dict  ):
    UpperCAmelCase     =	pa.array(TypedSequence(_a					,     optimized_int_type=_a  )  )
    assert get_base_dtype(arr.type  ) == expected_dtype





@pytest.mark.parametrize(
    '''col, expected_dtype'''					,     [
        ('''attention_mask''', pa.inta()),
        ('''special_tokens_mask''', pa.inta()),
        ('''token_type_ids''', pa.inta()),
        ('''input_ids''', pa.intaa()),
        ('''other''', pa.intaa()),
    ]					,     )
@pytest.mark.parametrize('''sequence'''					,     [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]  )
def 		snake_case_							(_a			:							Optional[int]					,     _a			:							Optional[Any]					,     _a			:							Tuple  ):
    # in range
    UpperCAmelCase     =	pa.array(OptimizedTypedSequence(_a					,     col=_a  )  )
    assert get_base_dtype(arr.type  ) == expected_dtype

    # not in range
    if col != "other":
        # avoids errors due to in-place modifications
        UpperCAmelCase     =	copy.deepcopy(_a  )
        UpperCAmelCase     =	np.iinfo(expected_dtype.to_pandas_dtype()  ).max + 1
        change_first_primitive_element_in_list(_a					,     _a  )
        UpperCAmelCase     =	pa.array(OptimizedTypedSequence(_a					,     col=_a  )  )
        assert get_base_dtype(arr.type  ) == pa.intaa()





@pytest.mark.parametrize('''raise_exception'''					,     [False, True]  )
def 		snake_case_							(_a			:							List[Any]					,     _a			:							List[Any]  ):
    UpperCAmelCase     =	str(tmp_path / '''dataset-train.arrow'''  )
    try:
        with ArrowWriter(path=_a  ) as writer:
            if raise_exception:
                raise pa.lib.ArrowInvalid()
            else:
                writer.stream.close()
    except pa.lib.ArrowInvalid:
        pass
    finally:
        assert writer.stream.closed





def 		snake_case_							(_a			:							List[str]  ):
    UpperCAmelCase     =	'''mock://dataset-train.arrow'''
    with ArrowWriter(path=_a					,     storage_options=mockfs.storage_options  ) as writer:
        assert isinstance(writer._fs					,     type(_a  )  )
        assert writer._fs.storage_options == mockfs.storage_options
        writer.write({'''col_1''': '''foo''', '''col_2''': 1}  )
        writer.write({'''col_1''': '''bar''', '''col_2''': 2}  )
        UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert mockfs.exists(_a  )





def 		snake_case_							():
    UpperCAmelCase     =	pa.BufferOutputStream()
    with ParquetWriter(stream=_a  ) as writer:
        writer.write({'''col_1''': '''foo''', '''col_2''': 1}  )
        writer.write({'''col_1''': '''bar''', '''col_2''': 2}  )
        UpperCAmelCase ,  UpperCAmelCase     =	writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    UpperCAmelCase     =	pa.BufferReader(output.getvalue()  )
    UpperCAmelCase     =	pq.read_table(_a  )
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}





@require_pil
@pytest.mark.parametrize('''embed_local_files'''					,     [False, True]  )
def 		snake_case_							(_a			:							Any					,     _a			:							Any  ):
    import PIL.Image

    UpperCAmelCase     =	str(tmp_path / '''test_image_rgb.jpg'''  )
    PIL.Image.fromarray(np.zeros((5, 5)					,     dtype=np.uinta  )  ).save(_a					,     format='''png'''  )
    UpperCAmelCase     =	pa.BufferOutputStream()
    with ParquetWriter(
        stream=_a					,     features=Features({'''image''': Image()}  )					,     embed_local_files=_a  ) as writer:
        writer.write({'''image''': image_path}  )
        writer.finalize()
    UpperCAmelCase     =	pa.BufferReader(output.getvalue()  )
    UpperCAmelCase     =	pq.read_table(_a  )
    UpperCAmelCase     =	pa_table.to_pydict()
    if embed_local_files:
        assert isinstance(out['''image'''][0]['''path''']					,     _a  )
        with open(_a					,     '''rb'''  ) as f:
            assert out["image"][0]["bytes"] == f.read()
    else:
        assert out["image"][0]["path"] == image_path
        assert out["image"][0]["bytes"] is None







def 		snake_case_							():
    UpperCAmelCase     =	pa.schema([pa.field('''col_1'''					,     pa.string()					,     nullable=_a  )]  )
    UpperCAmelCase     =	pa.BufferOutputStream()
    with ArrowWriter(stream=_a  ) as writer:
        writer._build_writer(inferred_schema=_a  )
        assert writer._schema == pa.schema([pa.field('''col_1'''					,     pa.string()  )]  )










'''simple docstring'''







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


A									=datasets.logging.get_logger(__name__)


A									='\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

A									='\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

A									='\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

A									={
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION				)
class     _a						(			datasets.Metric				):
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           return datasets.MetricInfo(
               description=_DESCRIPTION     ,       citation=_CITATION     ,       homepage='''https://github.com/google-research/bleurt'''     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
                   {
                       '''predictions''': datasets.Value('''string'''     ,       id='''sequence'''       ),
                       '''references''': datasets.Value('''string'''     ,       id='''sequence'''       ),
                   }       )     ,       codebase_urls=['''https://github.com/google-research/bleurt''']     ,       reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']     ,       )
       def 					A   (			self		:			List[Any]     ,       lowercase		:			Union[str, Any]       ):



           '''simple docstring'''

           if self.config_name == "default":
               logger.warning(
                   '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
                   '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''       )
               UpperCAmelCase     =	'''bleurt-base-128'''

           if self.config_name.lower() in CHECKPOINT_URLS:
               UpperCAmelCase     =	self.config_name.lower()

           elif self.config_name.upper() in CHECKPOINT_URLS:
               UpperCAmelCase     =	self.config_name.upper()

           else:
               raise KeyError(
                   f"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"       )

           # download the model checkpoint specified by self.config_name and set up the scorer
           UpperCAmelCase     =	dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
           UpperCAmelCase     =	score.BleurtScorer(os.path.join(lowercase     ,       lowercase       )       )







       def 					A   (			self		:			str     ,       lowercase		:			str     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.scorer.score(references=lowercase     ,       candidates=lowercase       )
           return {"scores": scores}




