





from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, 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 tensorflow as tf

       from transformers import (
           TFConvBertForMaskedLM,
           TFConvBertForMultipleChoice,
           TFConvBertForQuestionAnswering,
           TFConvBertForSequenceClassification,
           TFConvBertForTokenClassification,
           TFConvBertModel,
       )


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     Optional[int]		, A					:     str		, A					:     List[Any]=1_3		, A					:     Tuple=7		, A					:     List[Any]=True		, A					:     Dict=True		, A					:     List[str]=True		, A					:     str=True		, A					:     List[str]=9_9		, A					:     Optional[int]=3_2		, A					:     Union[str, Any]=2		, A					:     Optional[int]=4		, A					:     List[str]=3_7		, A					:     str="gelu"		, A					:     List[Any]=0.1		, A					:     Union[str, Any]=0.1		, A					:     Any=5_1_2		, A					:     Tuple=1_6		, A					:     int=2		, A					:     Any=0.02		, A					:     Any=3		, A					:     int=4		, A					:     List[Any]=None		, )       ->int:
         lowerCamelCase__       :						List[Any]        =							parent
         lowerCamelCase__       :						Optional[int]        =							1_3
         lowerCamelCase__       :						Optional[int]        =							7
         lowerCamelCase__       :						List[str]        =							True
         lowerCamelCase__       :						Any        =							True
         lowerCamelCase__       :						int        =							True
         lowerCamelCase__       :						Union[str, Any]        =							True
         lowerCamelCase__       :						str        =							9_9
         lowerCamelCase__       :						Any        =							3_8_4
         lowerCamelCase__       :						Union[str, Any]        =							2
         lowerCamelCase__       :						List[Any]        =							4
         lowerCamelCase__       :						Optional[int]        =							3_7
         lowerCamelCase__       :						List[str]        =							'''gelu'''
         lowerCamelCase__       :						Union[str, Any]        =							0.1
         lowerCamelCase__       :						Union[str, Any]        =							0.1
         lowerCamelCase__       :						int        =							5_1_2
         lowerCamelCase__       :						Dict        =							1_6
         lowerCamelCase__       :						Optional[int]        =							2
         lowerCamelCase__       :						Tuple        =							0.02
         lowerCamelCase__       :						List[str]        =							3
         lowerCamelCase__       :						int        =							4
         lowerCamelCase__       :						Optional[Any]        =							1_2_8
         lowerCamelCase__       :						str        =							2
         lowerCamelCase__       :						Dict        =							9
         lowerCamelCase__       :						Optional[Any]        =							1
         lowerCamelCase__       :						Dict        =							None







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[Any]:
         lowerCamelCase__       :						Dict        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )

         lowerCamelCase__       :						List[Any]        =							None
         if self.use_input_mask:
           lowerCamelCase__       :						str        =							random_attention_mask([self.batch_size, self.seq_length]      )

         lowerCamelCase__       :						Any        =							None
         if self.use_token_type_ids:
           lowerCamelCase__       :						Union[str, Any]        =							ids_tensor([self.batch_size, self.seq_length]		, self.type_vocab_size      )

         lowerCamelCase__       :						Optional[int]        =							None
         lowerCamelCase__       :						str        =							None
         lowerCamelCase__       :						Any        =							None
         if self.use_labels:
           lowerCamelCase__       :						Tuple        =							ids_tensor([self.batch_size]		, self.type_sequence_label_size      )
           lowerCamelCase__       :						Optional[Any]        =							ids_tensor([self.batch_size, self.seq_length]		, self.num_labels      )
           lowerCamelCase__       :						Union[str, Any]        =							ids_tensor([self.batch_size]		, self.num_choices      )

         lowerCamelCase__       :						Optional[int]        =							ConvBertConfig(
             vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, type_vocab_size=self.type_vocab_size		, initializer_range=self.initializer_range		, return_dict=A		, )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     List[str]		, A					:     Union[str, Any]		, A					:     int		, A					:     Dict		, A					:     Optional[int]		, A					:     Any		, A					:     int      )       ->Dict:
         lowerCamelCase__       :						List[str]        =							TFConvBertModel(config=A      )
         lowerCamelCase__       :						Tuple        =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         lowerCamelCase__       :						Union[str, Any]        =							[input_ids, input_mask]
         lowerCamelCase__       :						Tuple        =							model(A      )

         lowerCamelCase__       :						List[str]        =							model(A      )

         self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)      )







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Optional[Any]		, A					:     str		, A					:     List[str]		, A					:     List[Any]		, A					:     str		, A					:     Any		, A					:     Dict      )       ->Union[str, Any]:
         lowerCamelCase__       :						Optional[Any]        =							TFConvBertForMaskedLM(config=A      )
         lowerCamelCase__       :						List[Any]        =							{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         lowerCamelCase__       :						str        =							model(A      )
         self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size)      )







       def 						__lowerCamelCase   (				self					:     str		, A					:     Optional[int]		, A					:     List[str]		, A					:     Any		, A					:     Optional[Any]		, A					:     Any		, A					:     Dict		, A					:     int      )       ->List[str]:
         lowerCamelCase__       :						int        =							self.num_labels
         lowerCamelCase__       :						int        =							TFConvBertForSequenceClassification(config=A      )
         lowerCamelCase__       :						int        =							{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         lowerCamelCase__       :						Optional[int]        =							model(A      )
         self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)      )







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Union[str, Any]		, A					:     Any		, A					:     List[str]		, A					:     int		, A					:     List[str]		, A					:     Optional[int]		, A					:     List[Any]      )       ->Dict:
         lowerCamelCase__       :						List[Any]        =							self.num_choices
         lowerCamelCase__       :						Dict        =							TFConvBertForMultipleChoice(config=A      )
         lowerCamelCase__       :						Dict        =							tf.tile(tf.expand_dims(A		, 1      )		, (1, self.num_choices, 1)      )
         lowerCamelCase__       :						Any        =							tf.tile(tf.expand_dims(A		, 1      )		, (1, self.num_choices, 1)      )
         lowerCamelCase__       :						Tuple        =							tf.tile(tf.expand_dims(A		, 1      )		, (1, self.num_choices, 1)      )
         lowerCamelCase__       :						Optional[int]        =							{
             '''input_ids''': multiple_choice_inputs_ids,
             '''attention_mask''': multiple_choice_input_mask,
             '''token_type_ids''': multiple_choice_token_type_ids,
         }
         lowerCamelCase__       :						Any        =							model(A      )
         self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_choices)      )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     Tuple		, A					:     Any		, A					:     List[str]		, A					:     int		, A					:     Any		, A					:     Tuple		, A					:     Optional[int]      )       ->Optional[int]:
         lowerCamelCase__       :						Optional[Any]        =							self.num_labels
         lowerCamelCase__       :						int        =							TFConvBertForTokenClassification(config=A      )
         lowerCamelCase__       :						Any        =							{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         lowerCamelCase__       :						Dict        =							model(A      )
         self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.num_labels)      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     List[Any]		, A					:     List[Any]		, A					:     int		, A					:     List[str]		, A					:     Tuple		, A					:     List[Any]		, A					:     Any      )       ->Any:
         lowerCamelCase__       :						Union[str, Any]        =							TFConvBertForQuestionAnswering(config=A      )
         lowerCamelCase__       :						str        =							{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         lowerCamelCase__       :						str        =							model(A      )
         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 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
         lowerCamelCase__       :						str        =							self.prepare_config_and_inputs()
         (
             lowerCamelCase__							
         )       :						int        =							config_and_inputs
         lowerCamelCase__       :						Dict        =							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_tf
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  List[str]			   =   (
           (
               TFConvBertModel,
               TFConvBertForMaskedLM,
               TFConvBertForQuestionAnswering,
               TFConvBertForSequenceClassification,
               TFConvBertForTokenClassification,
               TFConvBertForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       _UpperCAmelCase				:  Optional[Any]			   =   (
           {
               "feature-extraction": TFConvBertModel,
               "fill-mask": TFConvBertForMaskedLM,
               "question-answering": TFConvBertForQuestionAnswering,
               "text-classification": TFConvBertForSequenceClassification,
               "token-classification": TFConvBertForTokenClassification,
               "zero-shot": TFConvBertForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       _UpperCAmelCase				:  Union[str, Any]			   =   False
       _UpperCAmelCase				:  Any			   =   False
       _UpperCAmelCase				:  Optional[Any]			   =   False







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Any:
         lowerCamelCase__       :						List[str]        =							TFConvBertModelTester(self      )
         lowerCamelCase__       :						List[Any]        =							ConfigTester(self		, config_class=A		, hidden_size=3_7      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[str]:
         self.config_tester.run_common_tests()







       def 						__lowerCamelCase   (				self					:     int      )       ->List[str]:
         lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
         lowerCamelCase__       :						Union[str, Any]        =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Any:
         lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->str:
         lowerCamelCase__       :						Dict        =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Any:
         lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
         lowerCamelCase__       :						Union[str, Any]        =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A      )







       @slow
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Optional[int]:
         lowerCamelCase__       :						str        =							self.model_tester.prepare_config_and_inputs_for_common()
         lowerCamelCase__       :						int        =							True
         lowerCamelCase__       :						Any        =							True

         if hasattr(A		, '''use_cache'''      ):
           lowerCamelCase__       :						List[str]        =							True

         lowerCamelCase__       :						List[str]        =							getattr(self.model_tester		, '''encoder_seq_length'''		, self.model_tester.seq_length      )
         lowerCamelCase__       :						Optional[int]        =							getattr(self.model_tester		, '''key_length'''		, A      )

         for model_class in self.all_model_classes:
           lowerCamelCase__       :						Any        =							self._prepare_for_class(A		, A      )
           lowerCamelCase__       :						List[Any]        =							model_class(A      )
           lowerCamelCase__       :						Tuple        =							len(model(A      )      )

           with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(A		, saved_model=A      )
             lowerCamelCase__       :						List[Any]        =							os.path.join(A		, '''saved_model'''		, '''1'''      )
             lowerCamelCase__       :						Optional[int]        =							tf.keras.models.load_model(A      )
             lowerCamelCase__       :						str        =							model(A      )

             if self.is_encoder_decoder:
               lowerCamelCase__       :						str        =							outputs['''encoder_hidden_states''']
               lowerCamelCase__       :						str        =							outputs['''encoder_attentions''']
             else:
               lowerCamelCase__       :						Optional[int]        =							outputs['''hidden_states''']
               lowerCamelCase__       :						int        =							outputs['''attentions''']

             self.assertEqual(len(A      )		, A      )

             lowerCamelCase__       :						List[Any]        =							getattr(
                 self.model_tester		, '''expected_num_hidden_layers'''		, self.model_tester.num_hidden_layers + 1      )

             self.assertEqual(len(A      )		, A      )
             self.assertListEqual(
                 list(output_hidden_states[0].shape[-2:]      )		, [self.model_tester.seq_length, self.model_tester.hidden_size]		, )

             self.assertEqual(len(A      )		, self.model_tester.num_hidden_layers      )
             self.assertListEqual(
                 list(output_attentions[0].shape[-3:]      )		, [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]		, )







       @slow
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
         lowerCamelCase__       :						Union[str, Any]        =							TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''      )
         self.assertIsNotNone(A      )







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
         lowerCamelCase__       :						Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()
         lowerCamelCase__       :						int        =							True
         lowerCamelCase__       :						Optional[Any]        =							getattr(self.model_tester		, '''decoder_seq_length'''		, self.model_tester.seq_length      )
         lowerCamelCase__       :						List[str]        =							getattr(self.model_tester		, '''encoder_seq_length'''		, self.model_tester.seq_length      )
         lowerCamelCase__       :						List[Any]        =							getattr(self.model_tester		, '''key_length'''		, A      )
         lowerCamelCase__       :						Dict        =							getattr(self.model_tester		, '''key_length'''		, A      )

         def check_decoder_attentions_output(A					:     List[str]      ):
           lowerCamelCase__       :						Union[str, Any]        =							len(A      )
           self.assertEqual(out_len % 2		, 0      )
           lowerCamelCase__       :						Optional[int]        =							outputs.decoder_attentions
           self.assertEqual(len(A      )		, self.model_tester.num_hidden_layers      )
           self.assertListEqual(
               list(decoder_attentions[0].shape[-3:]      )		, [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]		, )

         def check_encoder_attentions_output(A					:     Optional[Any]      ):
           lowerCamelCase__       :						int        =							[
               t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
           ]
           self.assertEqual(len(A      )		, self.model_tester.num_hidden_layers      )
           self.assertListEqual(
               list(attentions[0].shape[-3:]      )		, [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]		, )

         for model_class in self.all_model_classes:
           lowerCamelCase__       :						int        =							True
           lowerCamelCase__       :						Dict        =							False
           lowerCamelCase__       :						Tuple        =							model_class(A      )
           lowerCamelCase__       :						Dict        =							model(self._prepare_for_class(A		, A      )      )
           lowerCamelCase__       :						Optional[int]        =							len(A      )
           self.assertEqual(config.output_hidden_states		, A      )
           check_encoder_attentions_output(A      )

           if self.is_encoder_decoder:
             lowerCamelCase__       :						List[Any]        =							model_class(A      )
             lowerCamelCase__       :						Tuple        =							model(self._prepare_for_class(A		, A      )      )
             self.assertEqual(config.output_hidden_states		, A      )
             check_decoder_attentions_output(A      )

           # Check that output attentions can also be changed via the config
           del inputs_dict["output_attentions"]
           lowerCamelCase__       :						Optional[int]        =							True
           lowerCamelCase__       :						Union[str, Any]        =							model_class(A      )
           lowerCamelCase__       :						Tuple        =							model(self._prepare_for_class(A		, A      )      )
           self.assertEqual(config.output_hidden_states		, A      )
           check_encoder_attentions_output(A      )

           # Check attention is always last and order is fine
           lowerCamelCase__       :						Optional[Any]        =							True
           lowerCamelCase__       :						Optional[Any]        =							True
           lowerCamelCase__       :						int        =							model_class(A      )
           lowerCamelCase__       :						Tuple        =							model(self._prepare_for_class(A		, A      )      )

           self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		, len(A      )      )
           self.assertEqual(model.config.output_hidden_states		, A      )
           check_encoder_attentions_output(A      )


@require_tf
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Dict:
         lowerCamelCase__       :						Dict        =							TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''      )
         lowerCamelCase__       :						Dict        =							tf.constant([[0, 1, 2, 3, 4, 5]]      )
         lowerCamelCase__       :						int        =							model(A      )[0]

         lowerCamelCase__       :						int        =							[1, 6, 7_6_8]
         self.assertEqual(output.shape		, A      )

         lowerCamelCase__       :						Optional[Any]        =							tf.constant(
             [
                 [
                     [-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32],
                     [0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24],
                     [0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84],
                 ]
             ]      )
         tf.debugging.assert_near(output[:, :3, :3]		, A		, atol=1e-4      )










from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


_A :   List[str]  			=  logging.get_logger(__name__)


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Any			   =   ["audio_values", "audio_mask"]







       def __init__(				self					:     Any		, A					:     Union[str, Any]=2_0_4_8		, A					:     Any=1		, A					:     int=[1_6, 1_6]		, A					:     Any=1_2_8		, A					:     List[Any]=4_4_1_0_0		, A					:     Dict=8_6		, A					:     Dict=2_0_4_8		, A					:     str=0.0		, **A					:     Union[str, Any]		, )       ->List[Any]:
          super().__init__(
              feature_size=A		, sampling_rate=A		, padding_value=A		, **A		, )

          lowerCamelCase__       :						Dict        =							spectrogram_length
          lowerCamelCase__       :						Optional[int]        =							num_channels
          lowerCamelCase__       :						str        =							patch_size
          lowerCamelCase__       :						Any        =							feature_size // self.patch_size[1]
          lowerCamelCase__       :						Union[str, Any]        =							n_fft
          lowerCamelCase__       :						Union[str, Any]        =							sampling_rate // hop_length_to_sampling_rate
          lowerCamelCase__       :						Optional[Any]        =							sampling_rate
          lowerCamelCase__       :						Optional[Any]        =							padding_value
          lowerCamelCase__       :						Union[str, Any]        =							mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2		, num_mel_filters=A		, min_frequency=0.0		, max_frequency=2_20_50.0		, sampling_rate=A		, norm='''slaney'''		, mel_scale='''slaney'''		, ).T







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     np.array      )       ->np.ndarray:
          lowerCamelCase__       :						Any        =							spectrogram(
              A		, window_function(self.n_fft		, '''hann'''      )		, frame_length=self.n_fft		, hop_length=self.hop_length		, power=2.0		, mel_filters=self.mel_filters.T		, log_mel='''dB'''		, db_range=80.0		, )
          lowerCamelCase__       :						Any        =							log_spec[:, :-1]
          lowerCamelCase__       :						int        =							log_spec - 20.0
          lowerCamelCase__       :						int        =							np.clip(log_spec / 40.0		, -2.0		, 0.0      ) + 1.0
          return log_spec







       def __call__(				self					:     Union[str, Any]		, A					:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		, A					:     Optional[Union[str, TensorType]] = None		, A					:     Optional[bool] = True		, A					:     Optional[int] = None		, A					:     bool = False		, A					:     bool = False		, **A					:     str		, )       ->BatchFeature:

          if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                raise ValueError(
                    '''This feature extractor is set to support sampling rate'''
                    F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                    F" with {self.sampling_rate} and not {sampling_rate}."      )
          else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''      )

          lowerCamelCase__       :						List[str]        =							isinstance(A		, np.ndarray      ) and len(raw_speech.shape      ) > 1
          if is_batched_numpy and len(raw_speech.shape      ) > 2:
             raise ValueError(F"Only mono-channel audio is supported for input to {self}"      )
          lowerCamelCase__       :						Any        =							is_batched_numpy or (
              isinstance(A		, (list, tuple)      ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list)      ))
          )
          if is_batched:
             lowerCamelCase__       :						Optional[int]        =							[np.asarray([speech]		, dtype=np.floataa      ).T for speech in raw_speech]
          elif not is_batched and not isinstance(A		, np.ndarray      ):
             lowerCamelCase__       :						Optional[int]        =							np.asarray(A		, dtype=np.floataa      )
          elif isinstance(A		, np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
             lowerCamelCase__       :						Optional[Any]        =							raw_speech.astype(np.floataa      )
          # always return batch
          if not is_batched:
             lowerCamelCase__       :						int        =							[np.asarray([raw_speech]      ).T]

          # Convert audio signals to log mel spectrograms, truncate by time axis
          lowerCamelCase__       :						List[str]        =							[
              self._np_extract_fbank_features(waveform.squeeze()      ).T[: self.spectrogram_length] for waveform in raw_speech
          ]
          if isinstance(audio_features[0]		, A      ):
             lowerCamelCase__       :						Dict        =							[np.asarray(A		, dtype=np.floataa      ) for feature in audio_features]

          # Create audio attention mask
          lowerCamelCase__       :						Optional[Any]        =							max(
              [ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len for feature in audio_features]      )  # The maximum number of audio patches in a batch
          if return_attention_mask:
             lowerCamelCase__       :						List[Any]        =							[
                 (ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             lowerCamelCase__       :						List[str]        =							np.array(A      ).astype(np.floataa      )

          # convert into correct format for padding
          lowerCamelCase__       :						str        =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
          lowerCamelCase__       :						Optional[Any]        =							np.ones([len(A      ), 1, max_time_len, self.feature_size]      ).astype(np.floataa      )
          lowerCamelCase__       :						Tuple        =							padded_audio_features * self.padding_value
          for i in range(len(A      )      ):
             lowerCamelCase__       :						int        =							audio_features[i]
             lowerCamelCase__       :						Optional[int]        =							feature

          # return as BatchFeature
          if return_attention_mask:
             lowerCamelCase__       :						Optional[int]        =							{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
          else:
             lowerCamelCase__       :						Tuple        =							{'''audio_values''': padded_audio_features}

          lowerCamelCase__       :						Union[str, Any]        =							BatchFeature(data=A		, tensor_type=A      )
          return encoded_inputs



