



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import (
			    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    FlaubertConfig,
			    TFFlaubertForMultipleChoice,
			    TFFlaubertForQuestionAnsweringSimple,
			    TFFlaubertForSequenceClassification,
			    TFFlaubertForTokenClassification,
			    TFFlaubertModel,
			    TFFlaubertWithLMHeadModel,
			)
class 			_lowerCAmelCase   :






			"""simple docstring"""





			def __init__(				self	:       Dict			,     lowercase	:       Tuple			,     )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				parent
									__lowercase					=				13
									__lowercase					=				7
									__lowercase					=				True
									__lowercase					=				True
									__lowercase					=				True
									__lowercase					=				True
									__lowercase					=				True
									__lowercase					=				False
									__lowercase					=				False
									__lowercase					=				False
									__lowercase					=				2
									__lowercase					=				99
									__lowercase					=				0
									__lowercase					=				32
									__lowercase					=				2
									__lowercase					=				4
									__lowercase					=				0.1
									__lowercase					=				0.1
									__lowercase					=				512
									__lowercase					=				16
									__lowercase					=				2
									__lowercase					=				0.02
									__lowercase					=				3
									__lowercase					=				4
									__lowercase					=				"""last"""
									__lowercase					=				True
									__lowercase					=				None
									__lowercase					=				0





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size   )
									__lowercase					=				random_attention_mask([self.batch_size, self.seq_length]			,     dtype=tf.floataa   )

									__lowercase					=				None
									if self.use_input_lengths:
															__lowercase					=				(
															    ids_tensor([self.batch_size]			,     vocab_size=2   ) + self.seq_length - 2
															)  # small variation of seq_length

									__lowercase					=				None
									if self.use_token_type_ids:
															__lowercase					=				ids_tensor([self.batch_size, self.seq_length]			,     self.n_langs   )

									__lowercase					=				None
									__lowercase					=				None
									__lowercase					=				None
									if self.use_labels:
															__lowercase					=				ids_tensor([self.batch_size]			,     self.type_sequence_label_size   )
															__lowercase					=				ids_tensor([self.batch_size, self.seq_length]			,     self.num_labels   )
															__lowercase					=				ids_tensor([self.batch_size]			,     2			,     dtype=tf.floataa   )
															__lowercase					=				ids_tensor([self.batch_size]			,     self.num_choices   )

									__lowercase					=				FlaubertConfig(
									    vocab_size=self.vocab_size			,     n_special=self.n_special			,     emb_dim=self.hidden_size			,     n_layers=self.num_hidden_layers			,     n_heads=self.num_attention_heads			,     dropout=self.hidden_dropout_prob			,     attention_dropout=self.attention_probs_dropout_prob			,     gelu_activation=self.gelu_activation			,     sinusoidal_embeddings=self.sinusoidal_embeddings			,     asm=self.asm			,     causal=self.causal			,     n_langs=self.n_langs			,     max_position_embeddings=self.max_position_embeddings			,     initializer_range=self.initializer_range			,     summary_type=self.summary_type			,     use_proj=self.use_proj			,     bos_token_id=self.bos_token_id			,     )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)





			def 		snake_case__			(				self	:       Dict			,     lowercase	:       Optional[Any]			,     lowercase	:       Optional[int]			,     lowercase	:       Union[str, Any]			,     lowercase	:       List[str]			,     lowercase	:       int			,     lowercase	:       List[Any]			,     lowercase	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       Tuple			,     )						->					Dict:
									"""simple docstring"""







									__lowercase					=				TFFlaubertModel(config=lowercase   )
									__lowercase					=				{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
									__lowercase					=				model(lowercase   )

									__lowercase					=				[input_ids, input_mask]
									__lowercase					=				model(lowercase   )
									self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)   )





			def 		snake_case__			(				self	:       Any			,     lowercase	:       Union[str, Any]			,     lowercase	:       List[str]			,     lowercase	:       List[str]			,     lowercase	:       Dict			,     lowercase	:       Optional[Any]			,     lowercase	:       Any			,     lowercase	:       str			,     lowercase	:       List[str]			,     lowercase	:       str			,     )						->					Dict:
									"""simple docstring"""







									__lowercase					=				TFFlaubertWithLMHeadModel(lowercase   )

									__lowercase					=				{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
									__lowercase					=				model(lowercase   )

									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)   )





			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       List[Any]			,     lowercase	:       str			,     lowercase	:       List[Any]			,     lowercase	:       Union[str, Any]			,     lowercase	:       Dict			,     lowercase	:       int			,     lowercase	:       List[Any]			,     )						->					Dict:
									"""simple docstring"""







									__lowercase					=				TFFlaubertForQuestionAnsweringSimple(lowercase   )

									__lowercase					=				{"""input_ids""": input_ids, """lengths""": input_lengths}

									__lowercase					=				model(lowercase   )

									self.parent.assertEqual(result.start_logits.shape			,     (self.batch_size, self.seq_length)   )
									self.parent.assertEqual(result.end_logits.shape			,     (self.batch_size, self.seq_length)   )





			def 		snake_case__			(				self	:       int			,     lowercase	:       Any			,     lowercase	:       int			,     lowercase	:       Union[str, Any]			,     lowercase	:       Union[str, Any]			,     lowercase	:       Optional[int]			,     lowercase	:       List[str]			,     lowercase	:       Tuple			,     lowercase	:       str			,     lowercase	:       List[str]			,     )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				TFFlaubertForSequenceClassification(lowercase   )

									__lowercase					=				{"""input_ids""": input_ids, """lengths""": input_lengths}

									__lowercase					=				model(lowercase   )

									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)   )





			def 		snake_case__			(				self	:       Any			,     lowercase	:       Dict			,     lowercase	:       Union[str, Any]			,     lowercase	:       str			,     lowercase	:       List[str]			,     lowercase	:       Any			,     lowercase	:       Any			,     lowercase	:       int			,     lowercase	:       str			,     lowercase	:       Optional[Any]			,     )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				self.num_labels
									__lowercase					=				TFFlaubertForTokenClassification(config=lowercase   )
									__lowercase					=				{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									__lowercase					=				model(lowercase   )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)   )





			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       Optional[Any]			,     lowercase	:       str			,     lowercase	:       Dict			,     lowercase	:       str			,     lowercase	:       int			,     lowercase	:       int			,     lowercase	:       Any			,     lowercase	:       Tuple			,     lowercase	:       List[str]			,     )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				self.num_choices
									__lowercase					=				TFFlaubertForMultipleChoice(config=lowercase   )
									__lowercase					=				tf.tile(tf.expand_dims(lowercase			,     1   )			,     (1, self.num_choices, 1)   )
									__lowercase					=				tf.tile(tf.expand_dims(lowercase			,     1   )			,     (1, self.num_choices, 1)   )
									__lowercase					=				tf.tile(tf.expand_dims(lowercase			,     1   )			,     (1, self.num_choices, 1)   )
									__lowercase					=				{
									    """input_ids""": multiple_choice_inputs_ids,
									    """attention_mask""": multiple_choice_input_mask,
									    """token_type_ids""": multiple_choice_token_type_ids,
									}
									__lowercase					=				model(lowercase   )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_choices)   )







			def 		snake_case__			(				self	:       List[Any]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				self.prepare_config_and_inputs()
									(
									    (
									    __lowercase			
									)	,			(
									    __lowercase			
									)	,			(
									    __lowercase			
									)	,			(
									    __lowercase			
									)	,			(
									    __lowercase			
									)	,			(
									    __lowercase			
									)	,			(
									    __lowercase			
									)	,			(
									    __lowercase			
									)	,			(
									    __lowercase			
									)	,						
									)					=				config_and_inputs
									__lowercase					=				{
									    """input_ids""": input_ids,
									    """token_type_ids""": token_type_ids,
									    """langs""": token_type_ids,
									    """lengths""": input_lengths,
									}
									return config, inputs_dict





@require_tf
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Dict      =			(
			    (
			        TFFlaubertModel,
			        TFFlaubertWithLMHeadModel,
			        TFFlaubertForSequenceClassification,
			        TFFlaubertForQuestionAnsweringSimple,
			        TFFlaubertForTokenClassification,
			        TFFlaubertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			lowercase__      :  List[str]      =			(
			    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
			)  # TODO (PVP): Check other models whether language generation is also applicable
			lowercase__      :  Any      =			(
			    {
			        """feature-extraction""": TFFlaubertModel,
			        """fill-mask""": TFFlaubertWithLMHeadModel,
			        """question-answering""": TFFlaubertForQuestionAnsweringSimple,
			        """text-classification""": TFFlaubertForSequenceClassification,
			        """token-classification""": TFFlaubertForTokenClassification,
			        """zero-shot""": TFFlaubertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			lowercase__      :  str      =			False
			lowercase__      :  Dict      =			False





			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       Any			,     lowercase	:       Dict			,     lowercase	:       Dict			,     lowercase	:       Tuple			,     lowercase	:       List[str]   )						->					Union[str, Any]:
									"""simple docstring"""







									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith("""Fast"""   )
									):
															# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
															# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
															# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
															return True

									return False





			def 		snake_case__			(				self	:       Optional[Any]   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				TFFlaubertModelTester(self   )
									__lowercase					=				ConfigTester(self			,     config_class=lowercase			,     emb_dim=37   )





			def 		snake_case__			(				self	:       Optional[int]   )						->					str:
									"""simple docstring"""







									self.config_tester.run_common_tests()





			def 		snake_case__			(				self	:       str   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_model(*lowercase   )





			def 		snake_case__			(				self	:       Tuple   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_lm_head(*lowercase   )





			def 		snake_case__			(				self	:       Any   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_qa(*lowercase   )





			def 		snake_case__			(				self	:       int   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_token_classification(*lowercase   )





			def 		snake_case__			(				self	:       Any   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowercase   )







			@slow
			def 		snake_case__			(				self	:       str   )						->					Tuple:
									"""simple docstring"""







									for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__lowercase					=				TFFlaubertModel.from_pretrained(lowercase   )
															self.assertIsNotNone(lowercase   )





@require_tf
@require_sentencepiece
@require_tokenizers
class 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			@slow
			def 		snake_case__			(				self	:       int   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""   )

									__lowercase					=				tf.convert_to_tensor(
									    [[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]]			,     dtype=tf.intaa			,     )  # "J'aime flaubert !"

									__lowercase					=				model(lowercase   )[0]
									__lowercase					=				tf.TensorShape((1, 8, 512)   )
									self.assertEqual(output.shape			,     lowercase   )
									# compare the actual values for a slice.
									__lowercase					=				tf.convert_to_tensor(
									    [
									        [
									            [-1.876_8773, -1.56_6555, 0.2707_2418],
									            [-1.692_0038, -0.587_3505, 1.932_9599],
									            [-2.956_3985, -1.699_3835, 1.797_2052],
									        ]
									    ]			,     dtype=tf.floataa			,     )

									self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,     expected_slice.numpy()			,     atol=1E-4   )   )










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

UpperCamelCase__ =						random.Random()






def  UpperCAmelCase__    (    lowercase__						,       lowercase__=1.0						,       lowercase__=None						,       lowercase__=None   ) ->      str:
						if rng is None:
												__lowercase					=				global_rng

						__lowercase					=				[]
						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 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			def __init__(				self	:       List[Any]			,     lowercase	:       Tuple			,     lowercase	:       Union[str, Any]=7			,     lowercase	:       List[Any]=400			,     lowercase	:       Any=2_000			,     lowercase	:       Optional[int]=24			,     lowercase	:       Any=24			,     lowercase	:       List[str]=0.0			,     lowercase	:       Dict=16_000			,     lowercase	:       Union[str, Any]=True			,     lowercase	:       Dict=True			,     )						->					Any:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Optional[int]   )						->					Any:
									"""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 		snake_case__			(				self	:       List[str]			,     lowercase	:       Tuple=False			,     lowercase	:       int=False   )						->					Optional[Any]:
									"""simple docstring"""







									def _flatten(lowercase	:       Optional[Any]   ):
															return list(itertools.chain(*lowercase   )   )

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





@require_torch
@require_torchaudio
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  int      =			SpeechaTextFeatureExtractor if is_speech_available() else None





			def 		snake_case__			(				self	:       Dict   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				SpeechaTextFeatureExtractionTester(self   )





			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       Optional[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       List[Any]   )						->					str:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									# create three inputs of length 800, 1000, and 1200
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]
									__lowercase					=				[np.asarray(lowercase   ) for speech_input in speech_inputs]

									# Test feature size
									__lowercase					=				feature_extractor(lowercase			,     padding=lowercase			,     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
									__lowercase					=				feature_extractor(speech_inputs[0]			,     return_tensors="""np"""   ).input_features
									__lowercase					=				feature_extractor(np_speech_inputs[0]			,     return_tensors="""np"""   ).input_features
									self.assertTrue(np.allclose(lowercase			,     lowercase			,     atol=1E-3   )   )

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

									# Test 2-D numpy arrays are batched.
									__lowercase					=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
									__lowercase					=				np.asarray(lowercase   )
									__lowercase					=				feature_extractor(lowercase			,     return_tensors="""np"""   ).input_features
									__lowercase					=				feature_extractor(lowercase			,     return_tensors="""np"""   ).input_features
									for enc_seq_a, enc_seq_a in zip(lowercase			,     lowercase   ):
															self.assertTrue(np.allclose(lowercase			,     lowercase			,     atol=1E-3   )   )





			def 		snake_case__			(				self	:       List[str]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]

									__lowercase					=				["""longest""", """max_length""", """do_not_pad"""]
									__lowercase					=				[None, 16, None]
									for max_length, padding in zip(lowercase			,     lowercase   ):
															__lowercase					=				feature_extractor(
															    lowercase			,     padding=lowercase			,     max_length=lowercase			,     return_attention_mask=lowercase   )
															__lowercase					=				inputs.input_features
															__lowercase					=				inputs.attention_mask
															__lowercase					=				[np.sum(lowercase   ) 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 		snake_case__			(				self	:       Any   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]

									__lowercase					=				["""longest""", """max_length""", """do_not_pad"""]
									__lowercase					=				[None, 16, None]
									for max_length, padding in zip(lowercase			,     lowercase   ):
															__lowercase					=				feature_extractor(
															    lowercase			,     max_length=lowercase			,     padding=lowercase			,     return_tensors="""np"""			,     return_attention_mask=lowercase   )
															__lowercase					=				inputs.input_features
															__lowercase					=				inputs.attention_mask
															__lowercase					=				[np.sum(lowercase   ) 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 		snake_case__			(				self	:       str   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]
									__lowercase					=				feature_extractor(
									    lowercase			,     padding="""max_length"""			,     max_length=4			,     truncation=lowercase			,     return_tensors="""np"""			,     return_attention_mask=lowercase			,     )
									__lowercase					=				inputs.input_features
									__lowercase					=				inputs.attention_mask
									__lowercase					=				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 		snake_case__			(				self	:       Tuple   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]
									__lowercase					=				feature_extractor(
									    lowercase			,     padding="""longest"""			,     max_length=4			,     truncation=lowercase			,     return_tensors="""np"""			,     return_attention_mask=lowercase			,     )
									__lowercase					=				inputs.input_features
									__lowercase					=				inputs.attention_mask
									__lowercase					=				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, 24)   )

									__lowercase					=				[floats_list((1, x)   )[0] for x in range(800			,     1_400			,     200   )]
									__lowercase					=				feature_extractor(
									    lowercase			,     padding="""longest"""			,     max_length=16			,     truncation=lowercase			,     return_tensors="""np"""			,     return_attention_mask=lowercase			,     )
									__lowercase					=				inputs.input_features
									__lowercase					=				inputs.attention_mask
									__lowercase					=				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, 24)   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									import torch

									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				np.random.rand(100			,     32   ).astype(np.floataa   )
									__lowercase					=				np_speech_inputs.tolist()

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





			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       Union[str, Any]   )						->					int:
									"""simple docstring"""







									from datasets import load_dataset

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

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







			def 		snake_case__			(				self	:       str   )						->					Any:
									"""simple docstring"""







									__lowercase					=				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

									__lowercase					=				self._load_datasamples(1   )
									__lowercase					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__lowercase					=				feature_extractor(lowercase			,     return_tensors="""pt"""   ).input_features
									self.assertEquals(input_features.shape			,     (1, 584, 24)   )
									self.assertTrue(np.allclose(input_features[0, 0, :30]			,     lowercase			,     atol=1E-4   )   )





