

"""simple docstring"""


import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

__A						   =			collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__A						   =			"https://storage.googleapis.com/cvdf-datasets/mnist/"






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Tuple:
		__lowerCAmelCase:       Union[str, Any]          =			numpy.dtype(numpy.uintaa ).newbyteorder(">" )
		return numpy.frombuffer(bytestream.read(4 )					, dtype=__SCREAMING_SNAKE_CASE )[0]






@deprecated(__SCREAMING_SNAKE_CASE					, "Please use tf.data to implement this functionality." )
def     a__    (    __SCREAMING_SNAKE_CASE )	->	List[Any]:
		print("Extracting"					, f.name )
		with gzip.GzipFile(fileobj=__SCREAMING_SNAKE_CASE ) as bytestream:
				__lowerCAmelCase:       str          =			_readaa(__SCREAMING_SNAKE_CASE )
				if magic != 2_0_5_1:
						raise ValueError(
						    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
				__lowerCAmelCase:       List[str]          =			_readaa(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       int          =			_readaa(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Tuple          =			_readaa(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Tuple          =			bytestream.read(rows * cols * num_images )
				__lowerCAmelCase:       List[str]          =			numpy.frombuffer(__SCREAMING_SNAKE_CASE					, dtype=numpy.uinta )
				__lowerCAmelCase:       List[str]          =			data.reshape(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, 1 )
				return data






@deprecated(__SCREAMING_SNAKE_CASE					, "Please use tf.one_hot on tensors." )
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	str:
		__lowerCAmelCase:       Union[str, Any]          =			labels_dense.shape[0]
		__lowerCAmelCase:       Optional[int]          =			numpy.arange(__SCREAMING_SNAKE_CASE ) * num_classes
		__lowerCAmelCase:       Optional[int]          =			numpy.zeros((num_labels, num_classes) )
		__lowerCAmelCase:       Union[str, Any]          =			1
		return labels_one_hot






@deprecated(__SCREAMING_SNAKE_CASE					, "Please use tf.data to implement this functionality." )
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=False					, __SCREAMING_SNAKE_CASE=1_0 )	->	Optional[Any]:
		print("Extracting"					, f.name )
		with gzip.GzipFile(fileobj=__SCREAMING_SNAKE_CASE ) as bytestream:
				__lowerCAmelCase:       List[Any]          =			_readaa(__SCREAMING_SNAKE_CASE )
				if magic != 2_0_4_9:
						raise ValueError(
						    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
				__lowerCAmelCase:       str          =			_readaa(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Optional[Any]          =			bytestream.read(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       int          =			numpy.frombuffer(__SCREAMING_SNAKE_CASE					, dtype=numpy.uinta )
				if one_hot:
						return _dense_to_one_hot(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
				return labels





class     snake_case	:

				@deprecated(
				    UpperCamelCase__     ,  "Please use alternatives such as official/mnist/_DataSet.py"
				    " from tensorflow/models."     ,  )
				def __init__(	self							:				Optional[int]     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				List[str]=False     ,  UpperCamelCase__							:				List[str]=False     ,  UpperCamelCase__							:				int=dtypes.floataa     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				int=None     ,  )->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       str          =			random_seed.get_seed(UpperCamelCase__)
						# If op level seed is not set, use whatever graph level seed is returned
						numpy.random.seed(seeda if seed is None else seeda)
						__lowerCAmelCase:       int          =			dtypes.as_dtype(UpperCamelCase__).base_dtype
						if dtype not in (dtypes.uinta, dtypes.floataa):
								raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
						if fake_data:
								__lowerCAmelCase:       Optional[int]          =			1_0_0_0_0
								__lowerCAmelCase:       List[str]          =			one_hot
						else:
								assert (
								    images.shape[0] == labels.shape[0]
								), f"images.shape: {images.shape} labels.shape: {labels.shape}"
								__lowerCAmelCase:       Optional[int]          =			images.shape[0]

								# Convert shape from [num examples, rows, columns, depth]
								# to [num examples, rows*columns] (assuming depth == 1)
								if reshape:
										assert images.shape[3] == 1
										__lowerCAmelCase:       Optional[int]          =			images.reshape(
										    images.shape[0]     ,  images.shape[1] * images.shape[2])
								if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										__lowerCAmelCase:       List[str]          =			images.astype(numpy.floataa)
										__lowerCAmelCase:       Optional[int]          =			numpy.multiply(UpperCamelCase__     ,  1.0 / 255.0)
						__lowerCAmelCase:       str          =			images
						__lowerCAmelCase:       str          =			labels
						__lowerCAmelCase:       List[Any]          =			0
						__lowerCAmelCase:       Union[str, Any]          =			0

				@property
				def 							lowercase_     (	self							:				Any)->   Union[str, Any]:



						'''simple docstring'''


						return self._images

				@property
				def 							lowercase_     (	self							:				str)->   int:



						'''simple docstring'''


						return self._labels

				@property
				def 							lowercase_     (	self							:				str)->   Dict:



						'''simple docstring'''


						return self._num_examples

				@property
				def 							lowercase_     (	self							:				Dict)->   Union[str, Any]:



						'''simple docstring'''


						return self._epochs_completed




				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				List[Any]=False     ,  UpperCamelCase__							:				List[str]=True)->   List[Any]:



						'''simple docstring'''


						if fake_data:
								__lowerCAmelCase:       int          =			[1] * 7_8_4
								__lowerCAmelCase:       Optional[Any]          =			[1] + [0] * 9 if self.one_hot else 0
								return (
								    [fake_image for _ in range(UpperCamelCase__)],
								    [fake_label for _ in range(UpperCamelCase__)],
								)
						__lowerCAmelCase:       List[str]          =			self._index_in_epoch
						# Shuffle for the first epoch
						if self._epochs_completed == 0 and start == 0 and shuffle:
								__lowerCAmelCase:       Union[str, Any]          =			numpy.arange(self._num_examples)
								numpy.random.shuffle(UpperCamelCase__)
								__lowerCAmelCase:       Tuple          =			self.images[perma]
								__lowerCAmelCase:       Union[str, Any]          =			self.labels[perma]
						# Go to the next epoch
						if start + batch_size > self._num_examples:
								# Finished epoch
								self._epochs_completed += 1
								# Get the rest examples in this epoch
								__lowerCAmelCase:       Optional[Any]          =			self._num_examples - start
								__lowerCAmelCase:       Any          =			self._images[start : self._num_examples]
								__lowerCAmelCase:       Optional[Any]          =			self._labels[start : self._num_examples]
								# Shuffle the data
								if shuffle:
										__lowerCAmelCase:       Dict          =			numpy.arange(self._num_examples)
										numpy.random.shuffle(UpperCamelCase__)
										__lowerCAmelCase:       Union[str, Any]          =			self.images[perm]
										__lowerCAmelCase:       str          =			self.labels[perm]
								# Start next epoch
								__lowerCAmelCase:       Union[str, Any]          =			0
								__lowerCAmelCase:       Tuple          =			batch_size - rest_num_examples
								__lowerCAmelCase:       str          =			self._index_in_epoch
								__lowerCAmelCase:       str          =			self._images[start:end]
								__lowerCAmelCase:       str          =			self._labels[start:end]
								return (
								    numpy.concatenate((images_rest_part, images_new_part)     ,  axis=0),
								    numpy.concatenate((labels_rest_part, labels_new_part)     ,  axis=0),
								)
						else:
								self._index_in_epoch += batch_size
								__lowerCAmelCase:       Optional[int]          =			self._index_in_epoch
								return self._images[start:end], self._labels[start:end]






@deprecated(__SCREAMING_SNAKE_CASE					, "Please write your own downloading logic." )
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	List[str]:
		if not gfile.Exists(__SCREAMING_SNAKE_CASE ):
				gfile.MakeDirs(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       List[str]          =			os.path.join(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		if not gfile.Exists(__SCREAMING_SNAKE_CASE ):
				urllib.request.urlretrieve(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )  # noqa: S310
				with gfile.GFile(__SCREAMING_SNAKE_CASE ) as f:
						__lowerCAmelCase:       List[str]          =			f.size()
				print("Successfully downloaded"					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, "bytes." )
		return filepath






@deprecated(
    __SCREAMING_SNAKE_CASE					, "Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=False					, __SCREAMING_SNAKE_CASE=False					, __SCREAMING_SNAKE_CASE=dtypes.floataa					, __SCREAMING_SNAKE_CASE=True					, __SCREAMING_SNAKE_CASE=5_0_0_0					, __SCREAMING_SNAKE_CASE=None					, __SCREAMING_SNAKE_CASE=DEFAULT_SOURCE_URL					, )	->	Tuple:
		if fake_data:

				def fake():
						return _DataSet(
						    []					, []					, fake_data=__SCREAMING_SNAKE_CASE					, one_hot=__SCREAMING_SNAKE_CASE					, dtype=__SCREAMING_SNAKE_CASE					, seed=__SCREAMING_SNAKE_CASE )

				__lowerCAmelCase:       Tuple          =			fake()
				__lowerCAmelCase:       Optional[int]          =			fake()
				__lowerCAmelCase:       Any          =			fake()
				return _Datasets(train=__SCREAMING_SNAKE_CASE					, validation=__SCREAMING_SNAKE_CASE					, test=__SCREAMING_SNAKE_CASE )

		if not source_url:  # empty string check
				__lowerCAmelCase:       str          =			DEFAULT_SOURCE_URL

		__lowerCAmelCase:       List[Any]          =			"train-images-idx3-ubyte.gz"
		__lowerCAmelCase:       List[Any]          =			"train-labels-idx1-ubyte.gz"
		__lowerCAmelCase:       Optional[int]          =			"t10k-images-idx3-ubyte.gz"
		__lowerCAmelCase:       List[Any]          =			"t10k-labels-idx1-ubyte.gz"

		__lowerCAmelCase:       Union[str, Any]          =			_maybe_download(
		    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, source_url + train_images_file )
		with gfile.Open(__SCREAMING_SNAKE_CASE					, "rb" ) as f:
				__lowerCAmelCase:       Any          =			_extract_images(__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       Optional[Any]          =			_maybe_download(
		    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, source_url + train_labels_file )
		with gfile.Open(__SCREAMING_SNAKE_CASE					, "rb" ) as f:
				__lowerCAmelCase:       Optional[int]          =			_extract_labels(__SCREAMING_SNAKE_CASE					, one_hot=__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       str          =			_maybe_download(
		    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, source_url + test_images_file )
		with gfile.Open(__SCREAMING_SNAKE_CASE					, "rb" ) as f:
				__lowerCAmelCase:       Optional[int]          =			_extract_images(__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       Tuple          =			_maybe_download(
		    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, source_url + test_labels_file )
		with gfile.Open(__SCREAMING_SNAKE_CASE					, "rb" ) as f:
				__lowerCAmelCase:       Optional[Any]          =			_extract_labels(__SCREAMING_SNAKE_CASE					, one_hot=__SCREAMING_SNAKE_CASE )

		if not 0 <= validation_size <= len(__SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase:       List[Any]          =			(
				    "Validation size should be between 0 and "
				    F"{len(__SCREAMING_SNAKE_CASE )}. Received: {validation_size}."
				)
				raise ValueError(__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       Optional[Any]          =			train_images[:validation_size]
		__lowerCAmelCase:       List[str]          =			train_labels[:validation_size]
		__lowerCAmelCase:       str          =			train_images[validation_size:]
		__lowerCAmelCase:       List[str]          =			train_labels[validation_size:]

		__lowerCAmelCase:       List[str]          =			{"dtype": dtype, "reshape": reshape, "seed": seed}

		__lowerCAmelCase:       Optional[int]          =			_DataSet(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, **__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Tuple          =			_DataSet(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, **__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Dict          =			_DataSet(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, **__SCREAMING_SNAKE_CASE )

		return _Datasets(train=__SCREAMING_SNAKE_CASE					, validation=__SCREAMING_SNAKE_CASE					, test=__SCREAMING_SNAKE_CASE )





"""simple docstring"""


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     snake_case	(				__snake_case  ):
				# to overwrite at feature extractactor specific tests
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  None

				@property
				def 							lowercase_     (	self							:				str)->   Any:



						'''simple docstring'''


						return self.feat_extract_tester.prepare_feat_extract_dict()

				def 							lowercase_     (	self							:				Tuple)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						self.assertTrue(hasattr(UpperCamelCase__     ,  "feature_size"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "sampling_rate"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "padding_value"))

				def 							lowercase_     (	self							:				Dict)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       int          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

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

						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="np")

						__lowerCAmelCase:       Optional[int]          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			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 							lowercase_     (	self							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="pt")

						__lowerCAmelCase:       str          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       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_tf
				def 							lowercase_     (	self							:				str)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="tf")

						__lowerCAmelCase:       int          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			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 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str]=False)->   Optional[int]:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				Tuple):
								__lowerCAmelCase:       Optional[int]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

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

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

						__lowerCAmelCase:       List[str]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

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

						# test padding for List[int] + numpy
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(UpperCamelCase__     ,  padding=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest")
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[-1]))
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

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

						__lowerCAmelCase:       Any          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))
						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
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__)
						__lowerCAmelCase:       str          =			input_a[input_name]

						__lowerCAmelCase:       Dict          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						self.assertTrue(all(len(UpperCamelCase__) % 1_0 == 0 for x in input_a))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))

						__lowerCAmelCase:       Optional[Any]          =			pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
						self.assertTrue(all(len(UpperCamelCase__) == 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
						__lowerCAmelCase:       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 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]=False)->   str:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				List[Any]):
								__lowerCAmelCase:       List[str]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

						def _inputs_are_equal(UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				List[Any]):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

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

						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

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

						# truncate to smallest
						__lowerCAmelCase:       int          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0]))
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

						# truncate to smallest with np
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np"     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np")
						__lowerCAmelCase:       Any          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						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(UpperCamelCase__))

						# truncate to middle
						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  return_tensors="np")
						__lowerCAmelCase:       str          =			input_a[input_name]

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

						# 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(UpperCamelCase__))
						self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

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

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

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

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

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Tuple          =			1_2
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  )
						__lowerCAmelCase:       str          =			input_a[input_name]

						# retrieve expected_length as multiple of pad_to_multiple_of
						__lowerCAmelCase:       Optional[Any]          =			len(speech_inputs[0])
						if expected_length % pad_to_multiple_of != 0:
								__lowerCAmelCase:       Optional[Any]          =			((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(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

				def 							lowercase_     (	self							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				List[Any])->   Tuple:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   Optional[Any]:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				@require_torch
				def 							lowercase_     (	self							:				Dict)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       str          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  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 							lowercase_     (	self							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       List[Any]          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Any          =			feat_extract.pad(UpperCamelCase__     ,  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 							lowercase_     (	self							:				Optional[int])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       Any          =			True
						__lowerCAmelCase:       str          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       Tuple          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Optional[Any]          =			feat_extract.model_input_names[0]

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

						__lowerCAmelCase:       Optional[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						self.assertListEqual(list(processed.attention_mask.shape)     ,  list(processed[input_name].shape[:2]))
						self.assertListEqual(processed.attention_mask.sum(-1).tolist()     ,  UpperCamelCase__)




				def 							lowercase_     (	self							:				List[str])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       str          =			True
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})
						__lowerCAmelCase:       List[str]          =			min(UpperCamelCase__)

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						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])


