import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    assert isinstance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype



@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory'''						,    [False, True]			)
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	tmp_path / '''cache'''
    __UpperCamelCase  :List[Any]    						=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        __UpperCamelCase  :Optional[int]    						=	SqlDatasetReader(
            '''dataset'''						,    '''sqlite:///''' + sqlite_path						,    cache_dir=SCREAMING_SNAKE_CASE						,    keep_in_memory=SCREAMING_SNAKE_CASE			).read()
    _check_sql_dataset(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)



@require_sqlalchemy
@pytest.mark.parametrize(
    '''features'''						,    [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]						,    )
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	tmp_path / '''cache'''
    __UpperCamelCase  :Tuple    						=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    __UpperCamelCase  :int    						=	features.copy() if features else default_expected_features
    __UpperCamelCase  :List[str]    						=	(
        Features({feature: Value(SCREAMING_SNAKE_CASE			) for feature, dtype in features.items()}			) if features is not None else None
    )
    __UpperCamelCase  :Optional[Any]    						=	SqlDatasetReader('''dataset'''						,    '''sqlite:///''' + sqlite_path						,    features=SCREAMING_SNAKE_CASE						,    cache_dir=SCREAMING_SNAKE_CASE			).read()
    _check_sql_dataset(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE			)			) as con:
        __UpperCamelCase  :Union[str, Any]    						=	con.cursor()
        cur.execute('''SELECT * FROM dataset'''			)
        for row in cur:
            yield row



@require_sqlalchemy
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	tmp_path / '''cache'''
    __UpperCamelCase  :List[Any]    						=	os.path.join(SCREAMING_SNAKE_CASE						,    '''tmp.sql'''			)
    __UpperCamelCase  :Tuple    						=	SqlDatasetReader('''dataset'''						,    '''sqlite:///''' + sqlite_path						,    cache_dir=SCREAMING_SNAKE_CASE			).read()
    SqlDatasetWriter(SCREAMING_SNAKE_CASE						,    '''dataset'''						,    '''sqlite:///''' + output_sqlite_path						,    num_proc=1			).write()

    __UpperCamelCase  :List[str]    						=	iter_sql_file(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Tuple    						=	iter_sql_file(SCREAMING_SNAKE_CASE			)

    for rowa, rowa in zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
        assert rowa == rowa



@require_sqlalchemy
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	tmp_path / '''cache'''
    __UpperCamelCase  :int    						=	os.path.join(SCREAMING_SNAKE_CASE						,    '''tmp.sql'''			)
    __UpperCamelCase  :str    						=	SqlDatasetReader('''dataset'''						,    '''sqlite:///''' + sqlite_path						,    cache_dir=SCREAMING_SNAKE_CASE			).read()
    SqlDatasetWriter(SCREAMING_SNAKE_CASE						,    '''dataset'''						,    '''sqlite:///''' + output_sqlite_path						,    num_proc=2			).write()

    __UpperCamelCase  :Optional[int]    						=	iter_sql_file(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[Any]    						=	iter_sql_file(SCREAMING_SNAKE_CASE			)

    for rowa, rowa in zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
        assert rowa == rowa



@require_sqlalchemy
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	tmp_path / '''cache'''
    __UpperCamelCase  :int    						=	os.path.join(SCREAMING_SNAKE_CASE						,    '''tmp.sql'''			)
    __UpperCamelCase  :List[Any]    						=	SqlDatasetReader('''dataset'''						,    '''sqlite:///''' + sqlite_path						,    cache_dir=SCREAMING_SNAKE_CASE			).read()
    with pytest.raises(SCREAMING_SNAKE_CASE			):
        SqlDatasetWriter(SCREAMING_SNAKE_CASE						,    '''dataset'''						,    '''sqlite:///''' + output_sqlite_path						,    num_proc=0			).write()






import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Union[str, Any]      			=   None
    a__			:		Tuple      			=   None







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return self.feat_extract_tester.prepare_feat_extract_dict()







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        self.assertTrue(hasattr(__lowercase				,				'''feature_size'''))
        self.assertTrue(hasattr(__lowercase				,				'''sampling_rate'''))
        self.assertTrue(hasattr(__lowercase				,				'''padding_value'''))







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :str    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Optional[int]    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Union[str, Any]    						=	BatchFeature({input_name: speech_inputs})

        self.assertTrue(all(len(__lowercase) == len(__lowercase) for x, y in zip(__lowercase				,				processed_features[input_name])))

        __UpperCamelCase  :Any    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :Optional[int]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''np''')

        __UpperCamelCase  :Any    						=	processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :Optional[Any]    						=	batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))







    @require_torch
    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Dict    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :Tuple    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :str    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''pt''')

        __UpperCamelCase  :Union[str, Any]    						=	processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :str    						=	batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))







    @require_tf
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        __UpperCamelCase  :str    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''tf''')

        __UpperCamelCase  :int    						=	processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :List[Any]    						=	batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))







    def     UpperCamelCase__     (			self				,				__lowercase=False) ->			Dict:
        def _inputs_have_equal_length(__lowercase):
            __UpperCamelCase  :List[str]    						=	len(input[0])
            for input_slice in input[1:]:
                if len(__lowercase) != length:
                    return False
            return True

        def _inputs_are_equal(__lowercase				,				__lowercase):
            if len(__lowercase) != len(__lowercase):
                return False

            for input_slice_a, input_slice_a in zip(__lowercase				,				__lowercase):
                if not np.allclose(np.asarray(__lowercase)				,				np.asarray(__lowercase)				,				atol=1E-3):
                    return False
            return True

        __UpperCamelCase  :List[str]    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase)
        __UpperCamelCase  :Any    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :Optional[Any]    						=	self.feat_extract_tester.seq_length_diff
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.max_seq_length + pad_diff
        __UpperCamelCase  :Tuple    						=	self.feat_extract_tester.min_seq_length
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_tester.batch_size
        __UpperCamelCase  :Any    						=	self.feat_extract_tester.feature_size

        # test padding for List[int] + numpy
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				padding=__lowercase)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :int    						=	feat_extract.pad(__lowercase				,				padding='''longest''')
        __UpperCamelCase  :int    						=	input_a[input_name]

        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(__lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[-1]))
        __UpperCamelCase  :Dict    						=	input_a[input_name]

        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')
        __UpperCamelCase  :Optional[int]    						=	input_a[input_name]

        # max_length parameter has to be provided when setting `padding="max_length"`
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				padding='''max_length''')[input_name]

        __UpperCamelCase  :Any    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=__lowercase				,				return_tensors='''np''')
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        self.assertFalse(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_are_equal(__lowercase				,				__lowercase))
        self.assertTrue(len(input_a[0]) == pad_min_length)
        self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
        self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
        self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

        if feature_size > 1:
            self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

        # test padding for `pad_to_multiple_of` for List[int] + numpy
        __UpperCamelCase  :int    						=	feat_extract.pad(__lowercase				,				pad_to_multiple_of=10)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :Optional[int]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				pad_to_multiple_of=10)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				pad_to_multiple_of=10				,				max_length=__lowercase)
        __UpperCamelCase  :Any    						=	input_a[input_name]

        __UpperCamelCase  :List[str]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				pad_to_multiple_of=10				,				max_length=__lowercase				,				return_tensors='''np'''				,				)
        __UpperCamelCase  :List[str]    						=	input_a[input_name]

        self.assertTrue(all(len(__lowercase) % 10 == 0 for x in input_a))
        self.assertTrue(_inputs_are_equal(__lowercase				,				__lowercase))

        __UpperCamelCase  :str    						=	pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
        self.assertTrue(all(len(__lowercase) == expected_mult_pad_length for x in input_a))
        self.assertEqual(input_a.shape[:2]				,				(batch_size, expected_mult_pad_length))

        if feature_size > 1:
            self.assertTrue(input_a.shape[2] == feature_size)

        # Check padding value is correct
        __UpperCamelCase  :Optional[Any]    						=	(np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
        self.assertTrue(
            abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
            < 1E-3)
        self.assertTrue(
            abs(
                np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
            < 1E-3)
        self.assertTrue(
            abs(
                np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
            < 1E-3)
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1E-3)
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
            < 1E-3)







    def     UpperCamelCase__     (			self				,				__lowercase=False) ->			Dict:
        def _inputs_have_equal_length(__lowercase):
            __UpperCamelCase  :Dict    						=	len(input[0])
            for input_slice in input[1:]:
                if len(__lowercase) != length:
                    return False
            return True

        def _inputs_are_equal(__lowercase				,				__lowercase):
            if len(__lowercase) != len(__lowercase):
                return False

            for input_slice_a, input_slice_a in zip(__lowercase				,				__lowercase):
                if not np.allclose(np.asarray(__lowercase)				,				np.asarray(__lowercase)				,				atol=1E-3):
                    return False
            return True

        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase)
        __UpperCamelCase  :Tuple    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Dict    						=	BatchFeature({input_name: speech_inputs})

        # truncate to smallest
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				truncation=__lowercase)
        __UpperCamelCase  :str    						=	input_a[input_name]

        __UpperCamelCase  :Optional[int]    						=	feat_extract.pad(__lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0]))
        __UpperCamelCase  :List[str]    						=	input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertFalse(_inputs_have_equal_length(__lowercase))

        # truncate to smallest with np
        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np'''				,				truncation=__lowercase				,				)
        __UpperCamelCase  :List[Any]    						=	input_a[input_name]

        __UpperCamelCase  :int    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np''')
        __UpperCamelCase  :Optional[Any]    						=	input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(__lowercase))

        # truncate to middle
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=__lowercase				,				return_tensors='''np'''				,				)
        __UpperCamelCase  :Dict    						=	input_a[input_name]

        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	input_a[input_name]

        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				return_tensors='''np''')
        __UpperCamelCase  :Union[str, Any]    						=	input_a[input_name]

        self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_are_equal(__lowercase				,				__lowercase))

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(__lowercase))
        self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				truncation=__lowercase)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				padding='''longest'''				,				truncation=__lowercase)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				padding='''longest'''				,				truncation=__lowercase)[input_name]

        # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				padding='''max_length'''				,				truncation=__lowercase)[input_name]

        # test truncation for `pad_to_multiple_of` for List[int] + numpy
        __UpperCamelCase  :Dict    						=	12
        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=__lowercase				,				truncation=__lowercase				,				)
        __UpperCamelCase  :List[Any]    						=	input_a[input_name]

        __UpperCamelCase  :Tuple    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=__lowercase				,				)
        __UpperCamelCase  :Any    						=	input_a[input_name]

        # retrieve expected_length as multiple of pad_to_multiple_of
        __UpperCamelCase  :Optional[Any]    						=	len(speech_inputs[0])
        if expected_length % pad_to_multiple_of != 0:
            __UpperCamelCase  :Dict    						=	((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

        self.assertTrue(len(input_a[0]) == expected_length)
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertFalse(_inputs_have_equal_length(__lowercase))







    def     UpperCamelCase__     (			self) ->			Any:
        self._check_padding(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Dict:
        self._check_padding(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Any:
        self._check_truncation(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        self._check_truncation(numpify=__lowercase)







    @require_torch
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :str    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :int    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :List[str]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''pt''')[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1E-2)







    @require_tf
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Any    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Any    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Union[str, Any]    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :str    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''tf''')[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1E-2)







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :List[Any]    						=	self.feat_extract_dict
        __UpperCamelCase  :Dict    						=	True
        __UpperCamelCase  :Dict    						=	self.feature_extraction_class(**__lowercase)
        __UpperCamelCase  :List[Any]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Any    						=	[len(__lowercase) for x in speech_inputs]
        __UpperCamelCase  :int    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[int]    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')
        self.assertIn('''attention_mask'''				,				__lowercase)
        self.assertListEqual(list(processed.attention_mask.shape)				,				list(processed[input_name].shape[:2]))
        self.assertListEqual(processed.attention_mask.sum(-1).tolist()				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_dict
        __UpperCamelCase  :Optional[int]    						=	True
        __UpperCamelCase  :Dict    						=	self.feature_extraction_class(**__lowercase)
        __UpperCamelCase  :List[str]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :List[Any]    						=	[len(__lowercase) for x in speech_inputs]
        __UpperCamelCase  :List[Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :int    						=	BatchFeature({input_name: speech_inputs})
        __UpperCamelCase  :Dict    						=	min(__lowercase)

        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=__lowercase				,				truncation=__lowercase				,				return_tensors='''np''')
        self.assertIn('''attention_mask'''				,				__lowercase)
        self.assertListEqual(
            list(processed_pad.attention_mask.shape)				,				[processed_pad[input_name].shape[0], max_length])
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1).tolist()				,				[max_length for x in speech_inputs])





