



import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    'files'		,					[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]		,					)
def        lowercase_							(A : Optional[int]		,					A : Tuple      ):
       snake_case__							:	List[str]   	=	tmp_path_factory.mktemp('dset_infos_dir'      )
       if "full:README.md" in files:
              with open(dataset_infos_dir / 'README.md'		,					'w'      ) as f:
                     f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
       if "empty:README.md" in files:
              with open(dataset_infos_dir / 'README.md'		,					'w'      ) as f:
                     f.write(''      )
    # we want to support dataset_infos.json for backward compatibility
       if "dataset_infos.json" in files:
              with open(dataset_infos_dir / 'dataset_infos.json'		,					'w'      ) as f:
                     f.write('{"default": {"dataset_size": 42}}'      )
       snake_case__							:	Optional[Any]   	=	DatasetInfosDict.from_directory(A      )
       assert dataset_infos
       assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
    'dataset_info'		,					[
        DatasetInfo(),
        DatasetInfo(
            description='foo'		,					features=Features({'a': Value('int32'      )}      )		,					builder_name='builder'		,					config_name='config'		,					version='1.0.0'		,					splits=[{'name': 'train'}]		,					download_size=4_2		,					),
    ]		,					)
def        lowercase_							(A : List[Any]		,					A : DatasetInfo      ):
       snake_case__							:	Any   	=	str(A      )
       dataset_info.write_to_directory(A      )
       snake_case__							:	List[str]   	=	DatasetInfo.from_directory(A      )
       assert dataset_info == reloaded
       assert os.path.exists(os.path.join(A		,					'dataset_info.json'      )      )
def        lowercase_							():
       snake_case__							:	Tuple   	=	DatasetInfo(
           description='foo'		,					citation='bar'		,					homepage='https://foo.bar'		,					license='CC0'		,					features=Features({'a': Value('int32'      )}      )		,					post_processed={}		,					supervised_keys=()		,					task_templates=[]		,					builder_name='builder'		,					config_name='config'		,					version='1.0.0'		,					splits=[{'name': 'train', 'num_examples': 4_2}]		,					download_checksums={}		,					download_size=1_3_3_7		,					post_processing_size=4_4_2		,					dataset_size=1_2_3_4		,					size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4		,					)
       snake_case__							:	int   	=	dataset_info._to_yaml_dict()
       assert sorted(A      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
       for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
              assert key in dataset_info_yaml_dict
              assert isinstance(dataset_info_yaml_dict[key]		,					(list, dict, int, str)      )
       snake_case__							:	str   	=	yaml.safe_dump(A      )
       snake_case__							:	Dict   	=	yaml.safe_load(A      )
       assert dataset_info_yaml_dict == reloaded
def        lowercase_							():
       snake_case__							:	Union[str, Any]   	=	DatasetInfo()
       snake_case__							:	Tuple   	=	dataset_info._to_yaml_dict()
       assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    'dataset_infos_dict'		,					[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'		,					features=Features({'a': Value('int32'      )}      )		,					builder_name='builder'		,					config_name='config'		,					version='1.0.0'		,					splits=[{'name': 'train'}]		,					download_size=4_2		,					)
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=4_2      ),
                'v2': DatasetInfo(dataset_size=1_3_3_7      ),
            }      ),
    ]		,					)
def        lowercase_							(A : str		,					A : DatasetInfosDict      ):
       snake_case__							:	Optional[int]   	=	str(A      )
       dataset_infos_dict.write_to_directory(A      )
       snake_case__							:	str   	=	DatasetInfosDict.from_directory(A      )

       # the config_name of the dataset_infos_dict take over the attribute
       for config_name, dataset_info in dataset_infos_dict.items():
              snake_case__							:	List[Any]   	=	config_name
              # the yaml representation doesn't include fields like description or citation
              # so we just test that we can recover what we can from the yaml
              snake_case__							:	List[str]   	=	DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
       assert dataset_infos_dict == reloaded

       if dataset_infos_dict:
              assert os.path.exists(os.path.join(A		,					'README.md'      )      )











import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


a_   :Any        =      random.Random()
def        lowercase_							(A : int		,					A : Union[str, Any]=1.0		,					A : List[str]=None		,					A : Any=None      ):
       if rng is None:
              snake_case__							:	List[str]   	=	global_rng

       snake_case__							:	int   	=	[]
       for batch_idx in range(shape[0]      ):
              values.append([]      )
              for _ in range(shape[1]      ):
                     values[-1].append(rng.random() * scale      )

       return values




@require_torch
class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], _snake_case  :  List[str], _snake_case  :  Tuple=7, _snake_case  :  Union[str, Any]=4_0_0, _snake_case  :  Any=2_0_0_0, _snake_case  :  Dict=1, _snake_case  :  Optional[Any]=0.0, _snake_case  :  List[Any]=1_6_0_0_0, _snake_case  :  List[Any]=True, _snake_case  :  List[Any]=8_0, _snake_case  :  Dict=1_6, _snake_case  :  str=6_4, _snake_case  :  Tuple="hann_window", _snake_case  :  Union[str, Any]=8_0, _snake_case  :  Optional[Any]=7_6_0_0, _snake_case  :  str=1e-10, _snake_case  :  Any=True, )   ->Union[str, Any]:
            snake_case__							:	Optional[int]   	=	parent
            snake_case__							:	Optional[Any]   	=	batch_size
            snake_case__							:	List[Any]   	=	min_seq_length
            snake_case__							:	List[Any]   	=	max_seq_length
            snake_case__							:	Any   	=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            snake_case__							:	Tuple   	=	feature_size
            snake_case__							:	List[Any]   	=	padding_value
            snake_case__							:	Any   	=	sampling_rate
            snake_case__							:	Dict   	=	do_normalize
            snake_case__							:	Union[str, Any]   	=	num_mel_bins
            snake_case__							:	Any   	=	hop_length
            snake_case__							:	Any   	=	win_length
            snake_case__							:	Any   	=	win_function
            snake_case__							:	Optional[int]   	=	fmin
            snake_case__							:	int   	=	fmax
            snake_case__							:	Union[str, Any]   	=	mel_floor
            snake_case__							:	Union[str, Any]   	=	return_attention_mask






     def    lowercase_   (       self  :  Optional[int]					)   ->List[str]:
            return {
                "feature_size": self.feature_size,
                "padding_value": self.padding_value,
                "sampling_rate": self.sampling_rate,
                "do_normalize": self.do_normalize,
                "num_mel_bins": self.num_mel_bins,
                "hop_length": self.hop_length,
                "win_length": self.win_length,
                "win_function": self.win_function,
                "fmin": self.fmin,
                "fmax": self.fmax,
                "mel_floor": self.mel_floor,
                "return_attention_mask": self.return_attention_mask,
            }






     def    lowercase_   (       self  :  Any, _snake_case  :  Optional[Any]=False, _snake_case  :  List[str]=False					)   ->Union[str, Any]:
            def _flatten(_snake_case  :  List[str]					):
                   return list(itertools.chain(*_snake_case					)					)

            if equal_length:
                   snake_case__							:	Any   	=	floats_list((self.batch_size, self.max_seq_length)					)
            else:
                   # make sure that inputs increase in size
                   snake_case__							:	int   	=	[
                       _flatten(floats_list((x, self.feature_size)					)					)
                       for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff					)
                   ]

            if numpify:
                   snake_case__							:	Any   	=	[np.asarray(_snake_case					) for x in speech_inputs]

            return speech_inputs






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str=False, _snake_case  :  Dict=False					)   ->List[str]:
            if equal_length:
                   snake_case__							:	Optional[Any]   	=	[floats_list((self.max_seq_length, self.num_mel_bins)					) for _ in range(self.batch_size					)]
            else:
                   # make sure that inputs increase in size
                   snake_case__							:	List[str]   	=	[
                       floats_list((x, self.num_mel_bins)					)
                       for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff					)
                   ]

            if numpify:
                   snake_case__							:	int   	=	[np.asarray(_snake_case					) for x in speech_inputs]

            return speech_inputs




@require_torch
class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	SpeechTaFeatureExtractor






     def    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	List[str]   	=	SpeechTaFeatureExtractionTester(self					)






     def    lowercase_   (       self  :  Any, _snake_case  :  Dict					)   ->Any:
            self.assertTrue(np.all(np.mean(_snake_case, axis=0					) < 1e-3					)					)
            self.assertTrue(np.all(np.abs(np.var(_snake_case, axis=0					) - 1					) < 1e-3					)					)






     def    lowercase_   (       self  :  List[Any]					)   ->Union[str, Any]:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            # create three inputs of length 800, 1000, and 1200
            snake_case__							:	int   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Tuple   	=	[np.asarray(_snake_case					) for speech_input in speech_inputs]

            # Test not batched input
            snake_case__							:	str   	=	feat_extract(speech_inputs[0], return_tensors='np'					).input_values
            snake_case__							:	List[str]   	=	feat_extract(np_speech_inputs[0], return_tensors='np'					).input_values
            self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test batched
            snake_case__							:	Any   	=	feat_extract(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Union[str, Any]   	=	feat_extract(_snake_case, return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case					):
                   self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)






     def    lowercase_   (       self  :  int					)   ->Optional[int]:
            snake_case__							:	List[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Tuple   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]

            snake_case__							:	int   	=	['longest', 'max_length', 'do_not_pad']
            snake_case__							:	List[str]   	=	[None, 1_6_0_0, None]
            for max_length, padding in zip(_snake_case, _snake_case					):
                   snake_case__							:	Optional[int]   	=	feat_extract(_snake_case, padding=_snake_case, max_length=_snake_case, return_tensors='np'					)
                   snake_case__							:	Optional[int]   	=	processed.input_values

                   self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
                   self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6					)
                   self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
                   self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6					)
                   self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[Any]:
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Tuple   	=	range(8_0_0, 1_4_0_0, 2_0_0					)
            snake_case__							:	Optional[Any]   	=	[floats_list((1, x)					)[0] for x in lengths]

            snake_case__							:	Union[str, Any]   	=	['longest', 'max_length', 'do_not_pad']
            snake_case__							:	str   	=	[None, 1_6_0_0, None]

            for max_length, padding in zip(_snake_case, _snake_case					):
                   snake_case__							:	List[str]   	=	feat_extract(_snake_case, max_length=_snake_case, padding=_snake_case					)
                   snake_case__							:	Tuple   	=	processed.input_values

                   self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
                   self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
                   self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)






     def    lowercase_   (       self  :  List[Any]					)   ->Optional[Any]:
            snake_case__							:	Any   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	str   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Optional[Any]   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='max_length', return_tensors='np'					)
            snake_case__							:	int   	=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1]					)
            self._check_zero_mean_unit_variance(input_values[2]					)






     def    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	str   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Dict   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	str   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='longest', return_tensors='np'					)
            snake_case__							:	Dict   	=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
            self._check_zero_mean_unit_variance(input_values[2]					)

            # make sure that if max_length < longest -> then pad to max_length
            self.assertTrue(input_values.shape == (3, 1_0_0_0)					)

            snake_case__							:	Tuple   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	List[str]   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=2_0_0_0, padding='longest', return_tensors='np'					)
            snake_case__							:	Optional[Any]   	=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
            self._check_zero_mean_unit_variance(input_values[2]					)

            # make sure that if max_length > longest -> then pad to longest
            self.assertTrue(input_values.shape == (3, 1_2_0_0)					)






     def    lowercase_   (       self  :  List[str]					)   ->Dict:
            snake_case__							:	Dict   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	List[Any]   	=	np.random.rand(1_0_0					).astype(np.floataa					)
            snake_case__							:	int   	=	np_speech_inputs.tolist()

            for inputs in [py_speech_inputs, np_speech_inputs]:
                   snake_case__							:	int   	=	feature_extractor.pad([{'input_values': inputs}], return_tensors='np'					)
                   self.assertTrue(np_processed.input_values.dtype == np.floataa					)
                   snake_case__							:	Optional[int]   	=	feature_extractor.pad([{'input_values': inputs}], return_tensors='pt'					)
                   self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case__							:	str   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            # create three inputs of length 800, 1000, and 1200
            snake_case__							:	List[Any]   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Dict   	=	[np.asarray(_snake_case					) for speech_input in speech_inputs]

            # Test feature size
            snake_case__							:	Optional[int]   	=	feature_extractor(audio_target=_snake_case, padding=_snake_case, return_tensors='np'					).input_values
            self.assertTrue(input_values.ndim == 3					)
            self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins					)

            # Test not batched input
            snake_case__							:	Dict   	=	feature_extractor(speech_inputs[0], return_tensors='np'					).input_values
            snake_case__							:	Any   	=	feature_extractor(np_speech_inputs[0], return_tensors='np'					).input_values
            self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test batched
            snake_case__							:	Dict   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Dict   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case					):
                   self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test 2-D numpy arrays are batched.
            snake_case__							:	Optional[Any]   	=	[floats_list((1, x)					)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
            snake_case__							:	int   	=	np.asarray(_snake_case					)
            snake_case__							:	Union[str, Any]   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Union[str, Any]   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case					):
                   self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->str:
            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	List[str]   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Tuple   	=	BatchFeature({input_name: speech_inputs}					)

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

            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case					)
            snake_case__							:	Union[str, Any]   	=	BatchFeature({input_name: speech_inputs}, tensor_type='np'					)

            snake_case__							:	Dict   	=	processed_features[input_name]

            if len(batch_features_input.shape					) < 3:
                   snake_case__							:	List[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.num_mel_bins)					)






     @require_torch
     def    lowercase_   (       self  :  List[str]					)   ->Any:
            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case					)
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Tuple   	=	feat_extract.model_input_names[0]

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

            snake_case__							:	Tuple   	=	processed_features[input_name]

            if len(batch_features_input.shape					) < 3:
                   snake_case__							:	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.num_mel_bins)					)






     @require_torch
     def    lowercase_   (       self  :  Optional[int]					)   ->Tuple:
            snake_case__							:	Dict   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Union[str, Any]   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	List[str]   	=	BatchFeature({input_name: speech_inputs}					)

            snake_case__							:	int   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	Tuple   	=	feat_extract.pad(_snake_case, padding='longest', return_tensors='np'					)[input_name]
            snake_case__							:	Union[str, Any]   	=	feat_extract.pad(_snake_case, 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					)






     def    lowercase_   (       self  :  int					)   ->Any:
            snake_case__							:	Any   	=	self.feat_extract_dict
            snake_case__							:	List[Any]   	=	True
            snake_case__							:	Union[str, Any]   	=	self.feature_extraction_class(**_snake_case					)
            snake_case__							:	Any   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	List[Any]   	=	[len(_snake_case					) for x in speech_inputs]
            snake_case__							:	Union[str, Any]   	=	feat_extract.model_input_names[0]

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

            snake_case__							:	List[str]   	=	feat_extract.num_mel_bins  # hack!

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






     def    lowercase_   (       self  :  Optional[int]					)   ->str:
            snake_case__							:	int   	=	self.feat_extract_dict
            snake_case__							:	List[str]   	=	True
            snake_case__							:	Tuple   	=	self.feature_extraction_class(**_snake_case					)
            snake_case__							:	List[str]   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	str   	=	[len(_snake_case					) for x in speech_inputs]
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Optional[int]   	=	BatchFeature({input_name: speech_inputs}					)
            snake_case__							:	Optional[Any]   	=	min(_snake_case					)

            snake_case__							:	Union[str, Any]   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	Tuple   	=	feat_extract.pad(
                _snake_case, padding='max_length', max_length=_snake_case, truncation=_snake_case, return_tensors='np'					)
            self.assertIn('attention_mask', _snake_case					)
            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]					)






     def    lowercase_   (       self  :  List[Any], _snake_case  :  Optional[int]					)   ->Optional[Any]:
            from datasets import load_dataset

            snake_case__							:	str   	=	load_dataset('hf-internal-testing/librispeech_asr_dummy', 'clean', split='validation'					)
            # automatic decoding with librispeech
            snake_case__							:	Dict   	=	ds.sort('id'					).select(range(_snake_case					)					)[:num_samples]['audio']

            return [x["array"] for x in speech_samples]






     def    lowercase_   (       self  :  str					)   ->str:
            # fmt: off
            snake_case__							:	List[Any]   	=	torch.tensor(
                [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
                 3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
                 2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
                 4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
                 7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
                 4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]					)
            # fmt: on

            snake_case__							:	Union[str, Any]   	=	self._load_datasamples(1					)
            snake_case__							:	Optional[int]   	=	SpeechTaFeatureExtractor()
            snake_case__							:	List[Any]   	=	feature_extractor(_snake_case, return_tensors='pt'					).input_values
            self.assertEquals(input_values.shape, (1, 9_3_6_8_0)					)
            self.assertTrue(torch.allclose(input_values[0, :3_0], _snake_case, atol=1e-6					)					)






     def    lowercase_   (       self  :  Any					)   ->str:
            # fmt: off
            snake_case__							:	Optional[Any]   	=	torch.tensor(
                [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
                 -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
                 -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
                 -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]					)
            # fmt: on

            snake_case__							:	List[str]   	=	self._load_datasamples(1					)
            snake_case__							:	str   	=	SpeechTaFeatureExtractor()
            snake_case__							:	Optional[Any]   	=	feature_extractor(audio_target=_snake_case, return_tensors='pt'					).input_values
            self.assertEquals(input_values.shape, (1, 3_6_6, 8_0)					)
            self.assertTrue(torch.allclose(input_values[0, 0, :3_0], _snake_case, atol=1e-4					)					)






