





import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
			from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=1_3       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=[1_0, 2_0, 3_0, 4_0]       ,  __lowerCAmelCase=[2, 2, 3, 2]       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=3_7       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=1_0       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=["stage2", "stage3", "stage4"]       ,  __lowerCAmelCase=[2, 3, 4]       ,  __lowerCAmelCase=None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		image_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		num_stages
				lowerCamelCase__								=		hidden_sizes
				lowerCamelCase__								=		depths
				lowerCamelCase__								=		is_training
				lowerCamelCase__								=		use_labels
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		num_labels
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		out_features
				lowerCamelCase__								=		out_indices
				lowerCamelCase__								=		scope





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				lowerCamelCase__								=		None
				if self.use_labels:
					lowerCamelCase__								=		ids_tensor([self.batch_size]       ,  self.num_labels							)

				lowerCamelCase__								=		self.get_config()
				return config, pixel_values, labels





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return ConvNextConfig(
				    num_channels=self.num_channels       ,  hidden_sizes=self.hidden_sizes       ,  depths=self.depths       ,  num_stages=self.num_stages       ,  hidden_act=self.hidden_act       ,  is_decoder=__lowerCAmelCase       ,  initializer_range=self.initializer_range       ,  out_features=self.out_features       ,  out_indices=self.out_indices       ,  num_labels=self.num_labels       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		ConvNextModel(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase							)
				# expected last hidden states: B, C, H // 32, W // 32
				self.parent.assertEqual(
				    result.last_hidden_state.shape       ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		ConvNextForImageClassification(__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  labels=__lowerCAmelCase							)
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		ConvNextBackbone(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase							)

				# verify hidden states
				self.parent.assertEqual(len(result.feature_maps							)       ,  len(config.out_features							)							)
				self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,  [self.batch_size, self.hidden_sizes[1], 4, 4]							)

				# verify channels
				self.parent.assertEqual(len(model.channels							)       ,  len(config.out_features							)							)
				self.parent.assertListEqual(model.channels       ,  config.hidden_sizes[1:]							)

				# verify backbone works with out_features=None
				lowerCamelCase__								=		None
				lowerCamelCase__								=		ConvNextBackbone(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase							)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps							)       ,  1							)
				self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,  [self.batch_size, self.hidden_sizes[-1], 1, 1]							)

				# verify channels
				self.parent.assertEqual(len(model.channels							)       ,  1							)
				self.parent.assertListEqual(model.channels       ,  [config.hidden_sizes[-1]]							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.prepare_config_and_inputs()
				lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__								=		config_and_inputs
				lowerCamelCase__								=		{'''pixel_values''': pixel_values}
				return config, inputs_dict






@require_torch
class 					__A    (    lowerCAmelCase			,				lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(
			    (
			        ConvNextModel,
			        ConvNextForImageClassification,
			        ConvNextBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCAmelCase_							     =						(
			    {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
			    if is_torch_available()
			    else {}
			)

			lowerCAmelCase_							     =						True
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						False





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ConvNextModelTester(self							)
				lowerCamelCase__								=		ConfigTester(self       ,  config_class=__lowerCAmelCase       ,  has_text_modality=__lowerCAmelCase       ,  hidden_size=3_7							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return





			@unittest.skip(reason='''ConvNext does not use inputs_embeds'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			@unittest.skip(reason='''ConvNext does not support input and output embeddings'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			@unittest.skip(reason='''ConvNext does not use feedforward chunking'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					lowerCamelCase__								=		model_class(__lowerCAmelCase							)
					lowerCamelCase__								=		inspect.signature(model.forward							)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCamelCase__								=		[*signature.parameters.keys()]

					lowerCamelCase__								=		['''pixel_values''']
					self.assertListEqual(arg_names[:1]       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				def check_hidden_states_output(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		model_class(__lowerCAmelCase							)
					model.to(__lowerCAmelCase							)
					model.eval()

					with torch.no_grad():
						lowerCamelCase__								=		model(**self._prepare_for_class(__lowerCAmelCase       ,  __lowerCAmelCase							)							)

					lowerCamelCase__								=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					lowerCamelCase__								=		self.model_tester.num_stages
					self.assertEqual(len(__lowerCAmelCase							)       ,  expected_num_stages + 1							)

					# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]							)       ,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,  )

				lowerCamelCase__				,	lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					lowerCamelCase__								=		True
					check_hidden_states_output(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					lowerCamelCase__								=		True

					check_hidden_states_output(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase							)





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__								=		ConvNextModel.from_pretrained(__lowerCAmelCase							)
					self.assertIsNotNone(__lowerCAmelCase							)





def lowerCAmelCase__()				->      Any:







	'''simple docstring'''







	lowerCamelCase__								=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
	return image






@require_torch
@require_vision
class 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			@cached_property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''							) if is_vision_available() else None





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''							).to(__lowerCAmelCase							)

				lowerCamelCase__								=		self.default_image_processor
				lowerCamelCase__								=		prepare_img()
				lowerCamelCase__								=		image_processor(images=__lowerCAmelCase       ,  return_tensors='''pt'''							).to(__lowerCAmelCase							)

				# forward pass
				with torch.no_grad():
					lowerCamelCase__								=		model(**__lowerCAmelCase							)

				# verify the logits
				lowerCamelCase__								=		torch.Size((1, 1_0_0_0)							)
				self.assertEqual(outputs.logits.shape       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		torch.tensor([-0.0260, -0.4739, 0.1911]							).to(__lowerCAmelCase							)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,  __lowerCAmelCase       ,  atol=1E-4							)							)






@require_torch
class 					__A    (    unittest.TestCase			,				lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(ConvNextBackbone,) if is_torch_available() else ()
			lowerCAmelCase_							     =						ConvNextConfig

			lowerCAmelCase_							     =						False





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ConvNextModelTester(self							)










import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
			from transformers import SpeechaTextFeatureExtractor

_a 						=      random.Random()





def lowerCAmelCase__(__snake_case       ,__snake_case=1.0       ,__snake_case=None       ,__snake_case=None							)				->      List[Any]:







	'''simple docstring'''







	if rng is None:
		lowerCamelCase__								=		global_rng

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

	return values






@require_torch
@require_torchaudio
class 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=4_0_0       ,  __lowerCAmelCase=2_0_0_0       ,  __lowerCAmelCase=2_4       ,  __lowerCAmelCase=2_4       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=1_6_0_0_0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		min_seq_length
				lowerCamelCase__								=		max_seq_length
				lowerCamelCase__								=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				lowerCamelCase__								=		feature_size
				lowerCamelCase__								=		num_mel_bins
				lowerCamelCase__								=		padding_value
				lowerCamelCase__								=		sampling_rate
				lowerCamelCase__								=		return_attention_mask
				lowerCamelCase__								=		do_normalize





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return {
				    "feature_size": self.feature_size,
				    "num_mel_bins": self.num_mel_bins,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				    "do_normalize": self.do_normalize,
				}





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=False							):



				'''simple docstring'''







				def _flatten(__lowerCAmelCase							):
					return list(itertools.chain(*__lowerCAmelCase							)							)

				if equal_length:
					lowerCamelCase__								=		[floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
				else:
					# make sure that inputs increase in size
					lowerCamelCase__								=		[
					    floats_list((x, self.feature_size)							)
					    for x in range(self.min_seq_length       ,  self.max_seq_length       ,  self.seq_length_diff							)
					]
				if numpify:
					lowerCamelCase__								=		[np.asarray(__lowerCAmelCase							) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class 					__A    (    lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						SpeechaTextFeatureExtractor if is_speech_available() else None





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		SpeechaTextFeatureExtractionTester(self							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				self.assertTrue(np.all(np.mean(__lowerCAmelCase       ,  axis=0							) < 1E-3							)							)
				self.assertTrue(np.all(np.abs(np.var(__lowerCAmelCase       ,  axis=0							) - 1							) < 1E-3							)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
				# create three inputs of length 800, 1000, and 1200
				lowerCamelCase__								=		[floats_list((1, x)							)[0] for x in range(8_0_0       ,  1_4_0_0       ,  2_0_0							)]
				lowerCamelCase__								=		[np.asarray(__lowerCAmelCase							) for speech_input in speech_inputs]

				# Test feature size
				lowerCamelCase__								=		feature_extractor(__lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  return_tensors='''np'''							).input_features
				self.assertTrue(input_features.ndim == 3							)
				self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size							)

				# Test not batched input
				lowerCamelCase__								=		feature_extractor(speech_inputs[0]       ,  return_tensors='''np'''							).input_features
				lowerCamelCase__								=		feature_extractor(np_speech_inputs[0]       ,  return_tensors='''np'''							).input_features
				self.assertTrue(np.allclose(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=1E-3							)							)

				# Test batched
				lowerCamelCase__								=		feature_extractor(__lowerCAmelCase       ,  return_tensors='''np'''							).input_features
				lowerCamelCase__								=		feature_extractor(__lowerCAmelCase       ,  return_tensors='''np'''							).input_features
				for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase       ,  __lowerCAmelCase							):
					self.assertTrue(np.allclose(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=1E-3							)							)

				# Test 2-D numpy arrays are batched.
				lowerCamelCase__								=		[floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
				lowerCamelCase__								=		np.asarray(__lowerCAmelCase							)
				lowerCamelCase__								=		feature_extractor(__lowerCAmelCase       ,  return_tensors='''np'''							).input_features
				lowerCamelCase__								=		feature_extractor(__lowerCAmelCase       ,  return_tensors='''np'''							).input_features
				for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase       ,  __lowerCAmelCase							):
					self.assertTrue(np.allclose(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=1E-3							)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
				lowerCamelCase__								=		[floats_list((1, x)							)[0] for x in range(8_0_0       ,  1_4_0_0       ,  2_0_0							)]

				lowerCamelCase__								=		['''longest''', '''max_length''', '''do_not_pad''']
				lowerCamelCase__								=		[None, 1_6, None]
				for max_length, padding in zip(__lowerCAmelCase       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		feature_extractor(
					    __lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  max_length=__lowerCAmelCase       ,  return_attention_mask=__lowerCAmelCase							)
					lowerCamelCase__								=		inputs.input_features
					lowerCamelCase__								=		inputs.attention_mask
					lowerCamelCase__								=		[np.sum(__lowerCAmelCase							) for x in attention_mask]

					self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
					self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
					self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
				lowerCamelCase__								=		[floats_list((1, x)							)[0] for x in range(8_0_0       ,  1_4_0_0       ,  2_0_0							)]

				lowerCamelCase__								=		['''longest''', '''max_length''', '''do_not_pad''']
				lowerCamelCase__								=		[None, 1_6, None]
				for max_length, padding in zip(__lowerCAmelCase       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		feature_extractor(
					    __lowerCAmelCase       ,  max_length=__lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  return_tensors='''np'''       ,  return_attention_mask=__lowerCAmelCase							)
					lowerCamelCase__								=		inputs.input_features
					lowerCamelCase__								=		inputs.attention_mask
					lowerCamelCase__								=		[np.sum(__lowerCAmelCase							) for x in attention_mask]

					self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
					self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6							)
					self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
					self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6							)
					self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
				lowerCamelCase__								=		[floats_list((1, x)							)[0] for x in range(8_0_0       ,  1_4_0_0       ,  2_0_0							)]
				lowerCamelCase__								=		feature_extractor(
				    __lowerCAmelCase       ,  padding='''max_length'''       ,  max_length=4       ,  truncation=__lowerCAmelCase       ,  return_tensors='''np'''       ,  return_attention_mask=__lowerCAmelCase       ,  )
				lowerCamelCase__								=		inputs.input_features
				lowerCamelCase__								=		inputs.attention_mask
				lowerCamelCase__								=		np.sum(attention_mask == 1       ,  axis=1							)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
				self._check_zero_mean_unit_variance(input_features[1]							)
				self._check_zero_mean_unit_variance(input_features[2]							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
				lowerCamelCase__								=		[floats_list((1, x)							)[0] for x in range(8_0_0       ,  1_4_0_0       ,  2_0_0							)]
				lowerCamelCase__								=		feature_extractor(
				    __lowerCAmelCase       ,  padding='''longest'''       ,  max_length=4       ,  truncation=__lowerCAmelCase       ,  return_tensors='''np'''       ,  return_attention_mask=__lowerCAmelCase       ,  )
				lowerCamelCase__								=		inputs.input_features
				lowerCamelCase__								=		inputs.attention_mask
				lowerCamelCase__								=		np.sum(attention_mask == 1       ,  axis=1							)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
				self._check_zero_mean_unit_variance(input_features[2]							)

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape       ,  (3, 4, 2_4)							)

				lowerCamelCase__								=		[floats_list((1, x)							)[0] for x in range(8_0_0       ,  1_4_0_0       ,  2_0_0							)]
				lowerCamelCase__								=		feature_extractor(
				    __lowerCAmelCase       ,  padding='''longest'''       ,  max_length=1_6       ,  truncation=__lowerCAmelCase       ,  return_tensors='''np'''       ,  return_attention_mask=__lowerCAmelCase       ,  )
				lowerCamelCase__								=		inputs.input_features
				lowerCamelCase__								=		inputs.attention_mask
				lowerCamelCase__								=		np.sum(attention_mask == 1       ,  axis=1							)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
				self._check_zero_mean_unit_variance(input_features[2]							)

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape       ,  (3, 6, 2_4)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				import torch

				lowerCamelCase__								=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
				lowerCamelCase__								=		np.random.rand(1_0_0       ,  3_2							).astype(np.floataa							)
				lowerCamelCase__								=		np_speech_inputs.tolist()

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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				from datasets import load_dataset

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

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		np.array([
				    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
				    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
				    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
				]							)
				# fmt: on

				lowerCamelCase__								=		self._load_datasamples(1							)
				lowerCamelCase__								=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
				lowerCamelCase__								=		feature_extractor(__lowerCAmelCase       ,  return_tensors='''pt'''							).input_features
				self.assertEquals(input_features.shape       ,  (1, 5_8_4, 2_4)							)
				self.assertTrue(np.allclose(input_features[0, 0, :3_0]       ,  __lowerCAmelCase       ,  atol=1E-4							)							)



