




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__   ( __lowercase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =       pa.array(TypedSequence([1, 2, 3]      )      )
											self.assertEqual(arr.type		,      pa.intaa()      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaises(_A      ):
																__lowerCAmelCase           =       pa.array(TypedSequence([1, 2, 3]      )		,      type=pa.intaa()      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaises(_A      ):
																__lowerCAmelCase           =       pa.array(TypedSequence([1, 2, 3]		,      try_type=Value("bool"      )		,      type=Value("int64"      )      )      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =       pa.array(TypedSequence([1, 2, 3]		,      type=Value("int32"      )      )      )
											self.assertEqual(arr.type		,      pa.intaa()      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaises((TypeError, pa.lib.ArrowInvalid)      ):
																__lowerCAmelCase           =       pa.array(TypedSequence(["foo", "bar"]		,      type=Value("int64"      )      )      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =       pa.array(TypedSequence([1, 2, 3]		,      try_type=Value("int32"      )      )      )
											self.assertEqual(arr.type		,      pa.intaa()      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =       pa.array(TypedSequence(["foo", "bar"]		,      try_type=Value("int64"      )      )      )
											self.assertEqual(arr.type		,      pa.string()      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =       pa.array(TypedSequence([[[1, 2, 3]]]		,      type=ArrayaD((1, 3)		,      "int64"      )      )      )
											self.assertEqual(arr.type		,      ArrayaDExtensionType((1, 3)		,      "int64"      )      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaises((TypeError, pa.lib.ArrowInvalid)      ):
																__lowerCAmelCase           =       pa.array(TypedSequence(["foo", "bar"]		,      type=ArrayaD((1, 3)		,      "int64"      )      )      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =       pa.array(TypedSequence([[[1, 2, 3]]]		,      try_type=ArrayaD((1, 3)		,      "int64"      )      )      )
											self.assertEqual(arr.type		,      ArrayaDExtensionType((1, 3)		,      "int64"      )      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						@require_pil
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											import PIL.Image

											__lowerCAmelCase           =       PIL.Image.fromarray(np.arange(1_0		,      dtype=np.uinta      ).reshape(2		,      5      )      )
											with patch(
											    "datasets.arrow_writer.cast_to_python_objects"		,      side_effect=_A      ) as mock_cast_to_python_objects:
																__lowerCAmelCase           =       pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]		,      type=Image()      )      )
																__lowerCAmelCase           =       mock_cast_to_python_objects.call_args_list[-1]
																self.assertIn("optimize_list_casting"		,      _A      )
																self.assertFalse(kwargs["optimize_list_casting"]      )







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Optional[int]					):
					__lowerCAmelCase           =       pa.BufferReader(_lowerCamelCase					) if isinstance(_lowerCamelCase				,				pa.Buffer					) else pa.memory_map(_lowerCamelCase					)
					__lowerCAmelCase           =       pa.ipc.open_stream(_lowerCamelCase					)
					__lowerCAmelCase           =       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, 10]					)
@pytest.mark.parametrize(
    "fields"				,				[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]					)
def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[Any]				,				SCREAMING_SNAKE_CASE_							:    List[str]					):
					__lowerCAmelCase           =       pa.BufferOutputStream()
					__lowerCAmelCase           =       pa.schema(_lowerCamelCase					) if fields else None
					with ArrowWriter(stream=_lowerCamelCase				,				schema=_lowerCamelCase				,				writer_batch_size=_lowerCamelCase					) as writer:
										writer.write({"col_1": "foo", "col_2": 1}					)
										writer.write({"col_1": "bar", "col_2": 2}					)
										__lowerCAmelCase           =       writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										__lowerCAmelCase           =       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
					assert writer._schema == pa.schema(_lowerCamelCase				,				metadata=writer._schema.metadata					)
					_check_output(output.getvalue()				,				expected_num_chunks=num_examples if writer_batch_size == 1 else 1					)







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







@pytest.mark.parametrize("writer_batch_size"				,				[None, 1, 10]					)
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase           =       pa.BufferOutputStream()
					with ArrowWriter(
					    stream=_lowerCamelCase				,				writer_batch_size=_lowerCamelCase				,				hash_salt="split_name"				,				check_duplicates=_lowerCamelCase				,				) as writer:
										with pytest.raises(_lowerCamelCase					):
															writer.write({"col_1": "foo", "col_2": 1}				,				key=[1, 2]					)
															__lowerCAmelCase           =       writer.finalize()







@pytest.mark.parametrize("writer_batch_size"				,				[None, 2, 10]					)
def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =       pa.BufferOutputStream()
					with ArrowWriter(
					    stream=_lowerCamelCase				,				writer_batch_size=_lowerCamelCase				,				hash_salt="split_name"				,				check_duplicates=_lowerCamelCase				,				) as writer:
										with pytest.raises(_lowerCamelCase					):
															writer.write({"col_1": "foo", "col_2": 1}				,				key=10					)
															writer.write({"col_1": "bar", "col_2": 2}				,				key=10					)
															__lowerCAmelCase           =       writer.finalize()







@pytest.mark.parametrize("writer_batch_size"				,				[None, 2, 10]					)
def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]					):
					__lowerCAmelCase           =       pa.BufferOutputStream()
					with ArrowWriter(
					    stream=_lowerCamelCase				,				writer_batch_size=_lowerCamelCase				,				hash_salt="split_name"				,				check_duplicates=_lowerCamelCase				,				) as writer:
										writer.write({"col_1": "foo", "col_2": 1}				,				key=1					)
										writer.write({"col_1": "bar", "col_2": 2}				,				key=2					)
										__lowerCAmelCase           =       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, 10]					)
@pytest.mark.parametrize(
    "fields"				,				[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]					)
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =       pa.BufferOutputStream()
					__lowerCAmelCase           =       pa.schema(_lowerCamelCase					) if fields else None
					with ArrowWriter(stream=_lowerCamelCase				,				schema=_lowerCamelCase				,				writer_batch_size=_lowerCamelCase					) as writer:
										writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}					)
										writer.write_batch({"col_1": [], "col_2": []}					)
										__lowerCAmelCase           =       writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										__lowerCAmelCase           =       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
					assert writer._schema == pa.schema(_lowerCamelCase				,				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, 10]					)
@pytest.mark.parametrize(
    "fields"				,				[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]					)
def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    List[str]					):
					__lowerCAmelCase           =       pa.BufferOutputStream()
					__lowerCAmelCase           =       pa.schema(_lowerCamelCase					) if fields else None
					with ArrowWriter(stream=_lowerCamelCase				,				schema=_lowerCamelCase				,				writer_batch_size=_lowerCamelCase					) as writer:
										writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}					)					)
										__lowerCAmelCase           =       writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										__lowerCAmelCase           =       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
					assert writer._schema == pa.schema(_lowerCamelCase				,				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, 10]					)
@pytest.mark.parametrize(
    "fields"				,				[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]					)
def      _a				(						SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    List[str]					):
					__lowerCAmelCase           =       pa.BufferOutputStream()
					__lowerCAmelCase           =       pa.schema(_lowerCamelCase					) if fields else None
					with ArrowWriter(stream=_lowerCamelCase				,				schema=_lowerCamelCase				,				writer_batch_size=_lowerCamelCase					) 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]}					)					)
										__lowerCAmelCase           =       writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										__lowerCAmelCase           =       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
					assert writer._schema == pa.schema(_lowerCamelCase				,				metadata=writer._schema.metadata					)
					_check_output(output.getvalue()				,				expected_num_chunks=num_examples if writer_batch_size == 1 else 1					)







def      _a				(						):


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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[Any]					):


					if pa.types.is_list(_lowerCamelCase					):
										return get_base_dtype(arr_type.value_type					)
					else:
										return arr_type







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    str					):


					if isinstance(lst[0]				,				_lowerCamelCase					):
										change_first_primitive_element_in_list(lst[0]				,				_lowerCamelCase					)
					else:
										__lowerCAmelCase           =       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      _a				(						SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =       pa.array(TypedSequence(_lowerCamelCase				,				optimized_int_type=_lowerCamelCase					)					)
					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      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase           =       pa.array(OptimizedTypedSequence(_lowerCamelCase				,				col=_lowerCamelCase					)					)
					assert get_base_dtype(arr.type					) == expected_dtype

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







@pytest.mark.parametrize("raise_exception"				,				[False, True]					)
def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    int					):
					__lowerCAmelCase           =       str(tmp_path / "dataset-train.arrow"					)
					try:
										with ArrowWriter(path=_lowerCamelCase					) as writer:
															if raise_exception:
																				raise pa.lib.ArrowInvalid()
															else:
																				writer.stream.close()
					except pa.lib.ArrowInvalid:
										pass
					finally:
										assert writer.stream.closed







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







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







@require_pil
@pytest.mark.parametrize("embed_local_files"				,				[False, True]					)
def      _a				(						SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    Optional[int]					):
					import PIL.Image

					__lowerCAmelCase           =       str(tmp_path / "test_image_rgb.jpg"					)
					PIL.Image.fromarray(np.zeros((5, 5)				,				dtype=np.uinta					)					).save(_lowerCamelCase				,				format="png"					)
					__lowerCAmelCase           =       pa.BufferOutputStream()
					with ParquetWriter(
					    stream=_lowerCamelCase				,				features=Features({"image": Image()}					)				,				embed_local_files=_lowerCamelCase					) as writer:
										writer.write({"image": image_path}					)
										writer.finalize()
					__lowerCAmelCase           =       pa.BufferReader(output.getvalue()					)
					__lowerCAmelCase           =       pq.read_table(_lowerCamelCase					)
					__lowerCAmelCase           =       pa_table.to_pydict()
					if embed_local_files:
										assert isinstance(out["image"][0]["path"]				,				_lowerCamelCase					)
										with open(_lowerCamelCase				,				"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      _a				(						):
					__lowerCAmelCase           =       pa.schema([pa.field("col_1"				,				pa.string()				,				nullable=_lowerCamelCase					)]					)
					__lowerCAmelCase           =       pa.BufferOutputStream()
					with ArrowWriter(stream=_lowerCamelCase					) as writer:
										writer._build_writer(inferred_schema=_lowerCamelCase					)
										assert writer._schema == pa.schema([pa.field("col_1"				,				pa.string()					)]					)










from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__         =      """\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

UpperCamelCase__         =      """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


UpperCamelCase__         =      """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,    _KWARGS_DESCRIPTION				)
class       a__   ( datasets.Metric				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return datasets.MetricInfo(
											    description=_DESCRIPTION		,      citation=_CITATION		,      inputs_description=_KWARGS_DESCRIPTION		,      features=datasets.Features(self._get_feature_types()      )		,      reference_urls=[
											        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
											    ]		,      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											if self.config_name == "multilist":
																return {
																    "predictions": datasets.Sequence(datasets.Value("float"      )      ),
																    "references": datasets.Sequence(datasets.Value("float"      )      ),
																}
											else:
																return {
																    "predictions": datasets.Value("float"      ),
																    "references": datasets.Value("float"      ),
																}






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A=None		,      _A="uniform_average"		,      _A=True      ):




											"""simple docstring"""



											__lowerCAmelCase           =	mean_squared_error(
											    _A		,      _A		,      sample_weight=_A		,      multioutput=_A		,      squared=_A      )

											return {"mse": mse}




