





'''simple docstring'''




import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ConditionalDetrImageProcessor
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     def __init__(   self							:			Optional[int]   ,       __snake_case							:			Any   ,       __snake_case							:			Tuple=7   ,       __snake_case							:			List[Any]=3   ,       __snake_case							:			Dict=30   ,       __snake_case							:			Optional[Any]=4_00   ,       __snake_case							:			Tuple=True   ,       __snake_case							:			Any=None   ,       __snake_case							:			Dict=True   ,       __snake_case							:			str=[0.5, 0.5, 0.5]   ,       __snake_case							:			List[Any]=[0.5, 0.5, 0.5]   ,       __snake_case							:			Optional[int]=True   ,       __snake_case							:			Any=1 / 2_55   ,       __snake_case							:			Optional[Any]=True   ,       )->	Dict:
        snake_case 						=   size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
        snake_case 						=   parent
        snake_case 						=   batch_size
        snake_case 						=   num_channels
        snake_case 						=   min_resolution
        snake_case 						=   max_resolution
        snake_case 						=   do_resize
        snake_case 						=   size
        snake_case 						=   do_normalize
        snake_case 						=   image_mean
        snake_case 						=   image_std
        snake_case 						=   do_rescale
        snake_case 						=   rescale_factor
        snake_case 						=   do_pad



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[str]:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
        }



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			Any   ,       __snake_case							:			Tuple=False						)->	int:


        if not batched:
           snake_case 						=   image_inputs[0]
           if isinstance(snake_case_   ,       Image.Image						):
              snake_case 						=   image.size
           else:
              snake_case 						=   image.shape[1], image.shape[2]
           if w < h:
              snake_case 						=   int(self.size["""shortest_edge"""] * h / w						)
              snake_case 						=   self.size["""shortest_edge"""]
           elif w > h:
              snake_case 						=   self.size["""shortest_edge"""]
              snake_case 						=   int(self.size["""shortest_edge"""] * w / h						)
           else:
              snake_case 						=   self.size["""shortest_edge"""]
              snake_case 						=   self.size["""shortest_edge"""]

        else:
           snake_case 						=   []
           for image in image_inputs:
              snake_case 						=   self.get_expected_values([image]						)
              expected_values.append((expected_height, expected_width)						)
           snake_case 						=   max(snake_case_   ,       key=lambda __snake_case						: item[0]						)[0]
           snake_case 						=   max(snake_case_   ,       key=lambda __snake_case						: item[1]						)[1]

        return expected_height, expected_width




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






     """simple docstring"""






     snake_case_						      =   ConditionalDetrImageProcessor if is_vision_available() else None



     def      lowerCAmelCase			(   self							:			int						)->	Any:
        snake_case 						=   ConditionalDetrImageProcessingTester(self						)



     @property
     def      lowerCAmelCase			(   self							:			List[str]						)->	Any:
        return self.image_processor_tester.prepare_image_processor_dict()



     def      lowerCAmelCase			(   self							:			Dict						)->	Union[str, Any]:
        snake_case 						=   self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(snake_case_   ,       """image_mean"""						)						)
        self.assertTrue(hasattr(snake_case_   ,       """image_std"""						)						)
        self.assertTrue(hasattr(snake_case_   ,       """do_normalize"""						)						)
        self.assertTrue(hasattr(snake_case_   ,       """do_resize"""						)						)
        self.assertTrue(hasattr(snake_case_   ,       """size"""						)						)



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	List[Any]:
        snake_case 						=   self.image_processing_class.from_dict(self.image_processor_dict						)
        self.assertEqual(image_processor.size   ,       {"""shortest_edge""": 18, """longest_edge""": 13_33}						)
        self.assertEqual(image_processor.do_pad   ,       snake_case_						)

        snake_case 						=   self.image_processing_class.from_dict(
            self.image_processor_dict   ,       size=42   ,       max_size=84   ,       pad_and_return_pixel_mask=snake_case_						)
        self.assertEqual(image_processor.size   ,       {"""shortest_edge""": 42, """longest_edge""": 84}						)
        self.assertEqual(image_processor.do_pad   ,       snake_case_						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[str]:
        pass



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Optional[Any]:
        snake_case 						=   self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        snake_case 						=   prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=snake_case_						)
        for image in image_inputs:
           self.assertIsInstance(snake_case_   ,       Image.Image						)

        # Test not batched input
        snake_case 						=   image_processing(image_inputs[0]   ,       return_tensors="""pt"""						).pixel_values

        snake_case 						=   self.image_processor_tester.get_expected_values(snake_case_						)

        self.assertEqual(
            encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

        # Test batched
        snake_case 						=   self.image_processor_tester.get_expected_values(snake_case_   ,       batched=snake_case_						)

        snake_case 						=   image_processing(snake_case_   ,       return_tensors="""pt"""						).pixel_values
        self.assertEqual(
            encoded_images.shape   ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   ,       )



     def      lowerCAmelCase			(   self							:			int						)->	Any:
        snake_case 						=   self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        snake_case 						=   prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=snake_case_   ,       numpify=snake_case_						)
        for image in image_inputs:
           self.assertIsInstance(snake_case_   ,       np.ndarray						)

        # Test not batched input
        snake_case 						=   image_processing(image_inputs[0]   ,       return_tensors="""pt"""						).pixel_values

        snake_case 						=   self.image_processor_tester.get_expected_values(snake_case_						)

        self.assertEqual(
            encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

        # Test batched
        snake_case 						=   image_processing(snake_case_   ,       return_tensors="""pt"""						).pixel_values

        snake_case 						=   self.image_processor_tester.get_expected_values(snake_case_   ,       batched=snake_case_						)

        self.assertEqual(
            encoded_images.shape   ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   ,       )



     def      lowerCAmelCase			(   self							:			List[Any]						)->	str:
        snake_case 						=   self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        snake_case 						=   prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=snake_case_   ,       torchify=snake_case_						)
        for image in image_inputs:
           self.assertIsInstance(snake_case_   ,       torch.Tensor						)

        # Test not batched input
        snake_case 						=   image_processing(image_inputs[0]   ,       return_tensors="""pt"""						).pixel_values

        snake_case 						=   self.image_processor_tester.get_expected_values(snake_case_						)

        self.assertEqual(
            encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

        # Test batched
        snake_case 						=   image_processing(snake_case_   ,       return_tensors="""pt"""						).pixel_values

        snake_case 						=   self.image_processor_tester.get_expected_values(snake_case_   ,       batched=snake_case_						)

        self.assertEqual(
            encoded_images.shape   ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   ,       )



     @slow
     def      lowerCAmelCase			(   self							:			str						)->	Union[str, Any]:
        snake_case 						=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
        with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""   ,       """r"""						) as f:
           snake_case 						=   json.loads(f.read()						)

        snake_case 						=   {"""image_id""": 3_97_69, """annotations""": target}

        # encode them
        snake_case 						=   ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""						)
        snake_case 						=   image_processing(images=snake_case_   ,       annotations=snake_case_   ,       return_tensors="""pt"""						)

        # verify pixel values
        snake_case 						=   torch.Size([1, 3, 8_00, 10_66]						)
        self.assertEqual(encoding["""pixel_values"""].shape   ,       snake_case_						)

        snake_case 						=   torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,       snake_case_   ,       atol=1e-4						)						)

        # verify area
        snake_case 						=   torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,       snake_case_						)						)
        # verify boxes
        snake_case 						=   torch.Size([6, 4]						)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,       snake_case_						)
        snake_case 						=   torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,       snake_case_   ,       atol=1e-3						)						)
        # verify image_id
        snake_case 						=   torch.tensor([3_97_69]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,       snake_case_						)						)
        # verify is_crowd
        snake_case 						=   torch.tensor([0, 0, 0, 0, 0, 0]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,       snake_case_						)						)
        # verify class_labels
        snake_case 						=   torch.tensor([75, 75, 63, 65, 17, 17]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,       snake_case_						)						)
        # verify orig_size
        snake_case 						=   torch.tensor([4_80, 6_40]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,       snake_case_						)						)
        # verify size
        snake_case 						=   torch.tensor([8_00, 10_66]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,       snake_case_						)						)



     @slow
     def      lowerCAmelCase			(   self							:			List[Any]						)->	Any:
        snake_case 						=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
        with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""   ,       """r"""						) as f:
           snake_case 						=   json.loads(f.read()						)

        snake_case 						=   {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

        snake_case 						=   pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""						)

        # encode them
        snake_case 						=   ConditionalDetrImageProcessor(format="""coco_panoptic"""						)
        snake_case 						=   image_processing(images=snake_case_   ,       annotations=snake_case_   ,       masks_path=snake_case_   ,       return_tensors="""pt"""						)

        # verify pixel values
        snake_case 						=   torch.Size([1, 3, 8_00, 10_66]						)
        self.assertEqual(encoding["""pixel_values"""].shape   ,       snake_case_						)

        snake_case 						=   torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,       snake_case_   ,       atol=1e-4						)						)

        # verify area
        snake_case 						=   torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,       snake_case_						)						)
        # verify boxes
        snake_case 						=   torch.Size([6, 4]						)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,       snake_case_						)
        snake_case 						=   torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,       snake_case_   ,       atol=1e-3						)						)
        # verify image_id
        snake_case 						=   torch.tensor([3_97_69]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,       snake_case_						)						)
        # verify is_crowd
        snake_case 						=   torch.tensor([0, 0, 0, 0, 0, 0]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,       snake_case_						)						)
        # verify class_labels
        snake_case 						=   torch.tensor([17, 17, 63, 75, 75, 93]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,       snake_case_						)						)
        # verify masks
        snake_case 						=   82_28_73
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()   ,       snake_case_						)
        # verify orig_size
        snake_case 						=   torch.tensor([4_80, 6_40]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,       snake_case_						)						)
        # verify size
        snake_case 						=   torch.tensor([8_00, 10_66]						)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,       snake_case_						)						)


"""simple docstring"""



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


_UpperCamelCase								=		logging.get_logger(__name__)
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[int]					     =				['input_features', 'is_longer']





			def __init__(			self   ,      snake_case_=64   ,      snake_case_=48_000   ,      snake_case_=480   ,      snake_case_=10   ,      snake_case_=1_024   ,      snake_case_=0.0   ,      snake_case_=False   ,      snake_case_ = 0   ,      snake_case_ = 14_000   ,      snake_case_ = None   ,      snake_case_ = "fusion"   ,      snake_case_ = "repeatpad"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    feature_size=snake_case_   ,      sampling_rate=snake_case_   ,      padding_value=snake_case_   ,      return_attention_mask=snake_case_   ,      **snake_case_   ,      )
										A__       :     List[str]								=			top_db
										A__       :     Optional[int]								=			truncation
										A__       :     str								=			padding
										A__       :     str								=			fft_window_size
										A__       :     Any								=			(fft_window_size >> 1) + 1
										A__       :     Optional[Any]								=			hop_length
										A__       :     Any								=			max_length_s
										A__       :     int								=			max_length_s * sampling_rate
										A__       :     Any								=			sampling_rate
										A__       :     List[str]								=			frequency_min
										A__       :     int								=			frequency_max
										A__       :     Tuple								=			mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,      num_mel_filters=snake_case_   ,      min_frequency=snake_case_   ,      max_frequency=snake_case_   ,      sampling_rate=snake_case_   ,      norm=snake_case_   ,      mel_scale="""htk"""   ,      )
										A__       :     List[Any]								=			mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,      num_mel_filters=snake_case_   ,      min_frequency=snake_case_   ,      max_frequency=snake_case_   ,      sampling_rate=snake_case_   ,      norm="""slaney"""   ,      mel_scale="""slaney"""   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			copy.deepcopy(self.__dict__							)
										A__       :     int								=			self.__class__.__name__
										if "mel_filters" in output:
																	del output["mel_filters"]
										if "mel_filters_slaney" in output:
																	del output["mel_filters_slaney"]
										return output





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     Any								=			spectrogram(
										    snake_case_   ,      window_function(self.fft_window_size   ,      """hann"""							)   ,      frame_length=self.fft_window_size   ,      hop_length=self.hop_length   ,      power=2.0   ,      mel_filters=snake_case_   ,      log_mel="""dB"""   ,      )
										return log_mel_spectrogram.T





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			np.array_split(list(range(0   ,      total_frames - chunk_frames + 1							)							)   ,      3							)
										if len(ranges[1]							) == 0:
																	# if the audio is too short, we just use the first chunk
																	A__       :     Optional[Any]								=			[0]
										if len(ranges[2]							) == 0:
																	# if the audio is too short, we just use the first chunk
																	A__       :     str								=			[0]
										# randomly choose index for each part
										A__       :     int								=			np.random.choice(ranges[0]							)
										A__       :     Dict								=			np.random.choice(ranges[1]							)
										A__       :     Dict								=			np.random.choice(ranges[2]							)

										A__       :     Any								=			mel[idx_front : idx_front + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_middle : idx_middle + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_back : idx_back + chunk_frames, :]

										A__       :     Dict								=			torch.tensor(mel[None, None, :]							)
										A__       :     Dict								=			torch.nn.functional.interpolate(
										    snake_case_   ,      size=[chunk_frames, 64]   ,      mode="""bilinear"""   ,      align_corners=snake_case_							)
										A__       :     List[Any]								=			mel_shrink[0][0].numpy()
										A__       :     Dict								=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,      axis=0							)
										return mel_fusion





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if waveform.shape[0] > max_length:
																	if truncation == "rand_trunc":
																								A__       :     int								=			True
																								# random crop to max_length (for compatibility) -> this should be handled by self.pad
																								A__       :     Tuple								=			len(snake_case_							) - max_length
																								A__       :     Any								=			np.random.randint(0   ,      overflow + 1							)
																								A__       :     int								=			waveform[idx : idx + max_length]
																								A__       :     List[str]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters_slaney							)[None, :]
																	elif truncation == "fusion":
																								A__       :     Optional[int]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters							)
																								A__       :     Optional[int]								=			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																								A__       :     int								=			mel.shape[0]
																								if chunk_frames == total_frames:
																															# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																															# In this case, we just use the whole audio.
																															A__       :     Union[str, Any]								=			np.stack([mel, mel, mel, mel]   ,      axis=0							)
																															A__       :     Dict								=			False
																								else:
																															A__       :     List[Any]								=			self._random_mel_fusion(snake_case_   ,      snake_case_   ,      snake_case_							)
																															A__       :     Optional[Any]								=			True
																	else:
																								raise NotImplementedError(F'''data_truncating {truncation} not implemented'''							)

										else:
																	A__       :     Tuple								=			False
																	# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																	if waveform.shape[0] < max_length:
																								if padding == "repeat":
																															A__       :     Union[str, Any]								=			int(max_length / len(snake_case_							)							)
																															A__       :     List[Any]								=			np.stack(np.tile(snake_case_   ,      n_repeat + 1							)							)[:max_length]
																								if padding == "repeatpad":
																															A__       :     List[Any]								=			int(max_length / len(snake_case_							)							)
																															A__       :     Tuple								=			np.stack(np.tile(snake_case_   ,      snake_case_							)							)
																								A__       :     Tuple								=			np.pad(snake_case_   ,      (0, max_length - waveform.shape[0])   ,      mode="""constant"""   ,      constant_values=0							)

																	if truncation == "fusion":
																								A__       :     str								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters							)
																								A__       :     List[Any]								=			np.stack([input_mel, input_mel, input_mel, input_mel]   ,      axis=0							)
																	else:
																								A__       :     List[Any]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters_slaney							)[None, :]

										return input_mel, longer





			def __call__(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     Optional[Any]								=			truncation if truncation is not None else self.truncation
										A__       :     Union[str, Any]								=			padding if padding else self.padding

										if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																								    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																								    F''' was sampled 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."""							)

										A__       :     List[Any]								=			isinstance(snake_case_   ,      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}'''							)
										A__       :     int								=			is_batched_numpy or (
										    isinstance(snake_case_   ,      (list, tuple)							) and (isinstance(raw_speech[0]   ,      (np.ndarray, tuple, list)							))
										)

										if is_batched:
																	A__       :     Any								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for speech in raw_speech]
										elif not is_batched and not isinstance(snake_case_   ,      np.ndarray							):
																	A__       :     Any								=			np.asarray(snake_case_   ,      dtype=np.floataa							)
										elif isinstance(snake_case_   ,      np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
																	A__       :     Optional[int]								=			raw_speech.astype(np.floataa							)

										# always return batch
										if not is_batched:
																	A__       :     Union[str, Any]								=			[np.asarray(snake_case_							)]

										# convert to mel spectrogram, truncate and pad if needed.
										A__       :     Optional[Any]								=			[
										    self._get_input_mel(snake_case_   ,      max_length if max_length else self.nb_max_samples   ,      snake_case_   ,      snake_case_							)
										    for waveform in raw_speech
										]

										A__       :     List[str]								=			[]
										A__       :     Optional[Any]								=			[]
										for mel, longer in padded_inputs:
																	input_mel.append(snake_case_							)
																	is_longer.append(snake_case_							)

										if truncation == "fusion" and sum(snake_case_							) == 0:
																	# if no audio is longer than 10s, then randomly select one audio to be longer
																	A__       :     Optional[Any]								=			np.random.randint(0   ,      len(snake_case_							)							)
																	A__       :     str								=			True

										if isinstance(input_mel[0]   ,      snake_case_							):
																	A__       :     Optional[Any]								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for feature in input_mel]

										# is_longer is a list of bool
										A__       :     Union[str, Any]								=			[[longer] for longer in is_longer]

										A__       :     str								=			{"""input_features""": input_mel, """is_longer""": is_longer}
										A__       :     Optional[int]								=			BatchFeature(snake_case_							)

										if return_tensors is not None:
																	A__       :     Dict								=			input_features.convert_to_tensors(snake_case_							)

										return input_features





