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 						lowercase    :




							def __init__(						self   :					Optional[Any]	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Any=13	,							_UpperCamelCase   :					str=32	,							_UpperCamelCase   :					List[str]=3	,							_UpperCamelCase   :					int=4	,							_UpperCamelCase   :					Dict=[10, 20, 30, 40]	,							_UpperCamelCase   :					int=[2, 2, 3, 2]	,							_UpperCamelCase   :					Optional[Any]=True	,							_UpperCamelCase   :					str=True	,							_UpperCamelCase   :					List[Any]=37	,							_UpperCamelCase   :					List[str]="gelu"	,							_UpperCamelCase   :					List[Any]=10	,							_UpperCamelCase   :					List[str]=0.0_2	,							_UpperCamelCase   :					Tuple=["stage2", "stage3", "stage4"]	,							_UpperCamelCase   :					Union[str, Any]=[2, 3, 4]	,							_UpperCamelCase   :					int=None	,							)					->				Optional[int]:



												'''simple docstring'''

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




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



												'''simple docstring'''

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

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

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




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[int]:



												'''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=_UpperCamelCase	,							initializer_range=self.initializer_range	,							out_features=self.out_features	,							out_indices=self.out_indices	,							num_labels=self.num_labels	,							)




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextModel(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )
												# 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 // 32, self.image_size // 32)	,							)




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Optional[int]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextBackbone(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )

												# 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
												SCREAMING_SNAKE_CASE    			=       None
												SCREAMING_SNAKE_CASE    			=       ConvNextBackbone(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )

												# 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 	__snake_case(						self   :					List[str]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       a				,       unittest.TestCase     ):
							lowercase__						:       Union[str, Any]								=							(
							    (
							        ConvNextModel,
							        ConvNextForImageClassification,
							        ConvNextBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase__						:       List[Any]								=							(
							    {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__						:       Any								=							True
							lowercase__						:       str								=							False
							lowercase__						:       List[Any]								=							False
							lowercase__						:       List[str]								=							False
							lowercase__						:       List[str]								=							False




							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							has_text_modality=_UpperCamelCase	,							hidden_size=37      )




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



												'''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 	__snake_case(						self   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												return




							@unittest.skip(reason="ConvNext does not use inputs_embeds"      )
							def 	__snake_case(						self   :					int      )					->				str:



												'''simple docstring'''

												pass




							@unittest.skip(reason="ConvNext does not support input and output embeddings"      )
							def 	__snake_case(						self   :					Optional[int]      )					->				Union[str, Any]:



												'''simple docstring'''

												pass




							@unittest.skip(reason="ConvNext does not use feedforward chunking"      )
							def 	__snake_case(						self   :					List[Any]      )					->				Dict:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

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

																	SCREAMING_SNAKE_CASE    			=       ["pixel_values"]
																	self.assertListEqual(arg_names[:1]	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					Any      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												def check_hidden_states_output(_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Dict      ):
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	model.to(_UpperCamelCase      )
																	model.eval()

																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE    			=       model(**self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase      )      )

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

																	SCREAMING_SNAKE_CASE    			=       self.model_tester.num_stages
																	self.assertEqual(len(_UpperCamelCase      )	,							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]	,							)

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       True
																	check_hidden_states_output(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )

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

																	check_hidden_states_output(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase      )





							@slow
							def 	__snake_case(						self   :					List[str]      )					->				Optional[int]:



												'''simple docstring'''

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





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
					return image


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




							@cached_property
							def 	__snake_case(						self   :					Dict      )					->				Any:



												'''simple docstring'''

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





							@slow
							def 	__snake_case(						self   :					int      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"      ).to(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.default_image_processor
												SCREAMING_SNAKE_CASE    			=       prepare_img()
												SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      ).to(_UpperCamelCase      )

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      )

												# verify the logits
												SCREAMING_SNAKE_CASE    			=       torch.Size((1, 1_000)      )
												self.assertEqual(outputs.logits.shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]      ).to(_UpperCamelCase      )

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



@require_torch
class 						lowercase    (     unittest.TestCase				,       a     ):
							lowercase__						:       Any								=							(ConvNextBackbone,) if is_torch_available() else ()
							lowercase__						:       Optional[int]								=							ConvNextConfig

							lowercase__						:       Union[str, Any]								=							False




							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ConvNextModelTester(self      )





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


_lowerCamelCase					:   Union[str, Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							["""input_features""", """is_longer"""]




							def __init__(						self   :					str	,							_UpperCamelCase   :					Optional[int]=64	,							_UpperCamelCase   :					Any=48_000	,							_UpperCamelCase   :					Optional[Any]=480	,							_UpperCamelCase   :					List[Any]=10	,							_UpperCamelCase   :					Any=1_024	,							_UpperCamelCase   :					List[Any]=0.0	,							_UpperCamelCase   :					Any=False	,							_UpperCamelCase   :					float = 0	,							_UpperCamelCase   :					float = 14_000	,							_UpperCamelCase   :					int = None	,							_UpperCamelCase   :					str = "fusion"	,							_UpperCamelCase   :					str = "repeatpad"	,							**_UpperCamelCase   :					Optional[Any]	,							)					->				Dict:



												'''simple docstring'''

												super().__init__(
												    feature_size=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							padding_value=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							**_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       top_db
												SCREAMING_SNAKE_CASE    			=       truncation
												SCREAMING_SNAKE_CASE    			=       padding
												SCREAMING_SNAKE_CASE    			=       fft_window_size
												SCREAMING_SNAKE_CASE    			=       (fft_window_size >> 1) + 1
												SCREAMING_SNAKE_CASE    			=       hop_length
												SCREAMING_SNAKE_CASE    			=       max_length_s
												SCREAMING_SNAKE_CASE    			=       max_length_s * sampling_rate
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       frequency_min
												SCREAMING_SNAKE_CASE    			=       frequency_max
												SCREAMING_SNAKE_CASE    			=       mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins	,							num_mel_filters=_UpperCamelCase	,							min_frequency=_UpperCamelCase	,							max_frequency=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							norm=_UpperCamelCase	,							mel_scale="htk"	,							)
												SCREAMING_SNAKE_CASE    			=       mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins	,							num_mel_filters=_UpperCamelCase	,							min_frequency=_UpperCamelCase	,							max_frequency=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							norm="slaney"	,							mel_scale="slaney"	,							)




							def 	__snake_case(						self   :					str      )					->				Dict[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       copy.deepcopy(self.__dict__      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					np.array	,							_UpperCamelCase   :					Optional[np.array] = None      )					->				np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       spectrogram(
												    _UpperCamelCase	,							window_function(self.fft_window_size	,							"hann"      )	,							frame_length=self.fft_window_size	,							hop_length=self.hop_length	,							power=2.0	,							mel_filters=_UpperCamelCase	,							log_mel="dB"	,							)
												return log_mel_spectrogram.T




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					str      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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
																	SCREAMING_SNAKE_CASE    			=       [0]
												if len(ranges[2]      ) == 0:
																	# if the audio is too short, we just use the first chunk
																	SCREAMING_SNAKE_CASE    			=       [0]
												# randomly choose index for each part
												SCREAMING_SNAKE_CASE    			=       np.random.choice(ranges[0]      )
												SCREAMING_SNAKE_CASE    			=       np.random.choice(ranges[1]      )
												SCREAMING_SNAKE_CASE    			=       np.random.choice(ranges[2]      )

												SCREAMING_SNAKE_CASE    			=       mel[idx_front : idx_front + chunk_frames, :]
												SCREAMING_SNAKE_CASE    			=       mel[idx_middle : idx_middle + chunk_frames, :]
												SCREAMING_SNAKE_CASE    			=       mel[idx_back : idx_back + chunk_frames, :]

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




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					np.array	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[str]      )					->				np.array:



												'''simple docstring'''

												if waveform.shape[0] > max_length:
																	if truncation == "rand_trunc":
																						SCREAMING_SNAKE_CASE    			=       True
																						# random crop to max_length (for compatibility) -> this should be handled by self.pad
																						SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) - max_length
																						SCREAMING_SNAKE_CASE    			=       np.random.randint(0	,							overflow + 1      )
																						SCREAMING_SNAKE_CASE    			=       waveform[idx : idx + max_length]
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters_slaney      )[None, :]
																	elif truncation == "fusion":
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters      )
																						SCREAMING_SNAKE_CASE    			=       max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																						SCREAMING_SNAKE_CASE    			=       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.
																											SCREAMING_SNAKE_CASE    			=       np.stack([mel, mel, mel, mel]	,							axis=0      )
																											SCREAMING_SNAKE_CASE    			=       False
																						else:
																											SCREAMING_SNAKE_CASE    			=       self._random_mel_fusion(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																											SCREAMING_SNAKE_CASE    			=       True
																	else:
																						raise NotImplementedError(F"data_truncating {truncation} not implemented"      )

												else:
																	SCREAMING_SNAKE_CASE    			=       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":
																											SCREAMING_SNAKE_CASE    			=       int(max_length / len(_UpperCamelCase      )      )
																											SCREAMING_SNAKE_CASE    			=       np.stack(np.tile(_UpperCamelCase	,							n_repeat + 1      )      )[:max_length]
																						if padding == "repeatpad":
																											SCREAMING_SNAKE_CASE    			=       int(max_length / len(_UpperCamelCase      )      )
																											SCREAMING_SNAKE_CASE    			=       np.stack(np.tile(_UpperCamelCase	,							_UpperCamelCase      )      )
																						SCREAMING_SNAKE_CASE    			=       np.pad(_UpperCamelCase	,							(0, max_length - waveform.shape[0])	,							mode="constant"	,							constant_values=0      )

																	if truncation == "fusion":
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters      )
																						SCREAMING_SNAKE_CASE    			=       np.stack([input_mel, input_mel, input_mel, input_mel]	,							axis=0      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters_slaney      )[None, :]

												return input_mel, longer





							def __call__(						self   :					Dict	,							_UpperCamelCase   :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,							_UpperCamelCase   :					str = None	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							**_UpperCamelCase   :					Tuple	,							)					->				BatchFeature:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       truncation if truncation is not None else self.truncation
												SCREAMING_SNAKE_CASE    			=       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."      )

												SCREAMING_SNAKE_CASE    			=       isinstance(_UpperCamelCase	,							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}"      )
												SCREAMING_SNAKE_CASE    			=       is_batched_numpy or (
												    isinstance(_UpperCamelCase	,							(list, tuple)      ) and (isinstance(raw_speech[0]	,							(np.ndarray, tuple, list)      ))
												)

												if is_batched:
																	SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase	,							dtype=np.floataa      ) for speech in raw_speech]
												elif not is_batched and not isinstance(_UpperCamelCase	,							np.ndarray      ):
																	SCREAMING_SNAKE_CASE    			=       np.asarray(_UpperCamelCase	,							dtype=np.floataa      )
												elif isinstance(_UpperCamelCase	,							np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
																	SCREAMING_SNAKE_CASE    			=       raw_speech.astype(np.floataa      )

												# always return batch
												if not is_batched:
																	SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase      )]

												# convert to mel spectrogram, truncate and pad if needed.
												SCREAMING_SNAKE_CASE    			=       [
												    self._get_input_mel(_UpperCamelCase	,							max_length if max_length else self.nb_max_samples	,							_UpperCamelCase	,							_UpperCamelCase      )
												    for waveform in raw_speech
												]

												SCREAMING_SNAKE_CASE    			=       []
												SCREAMING_SNAKE_CASE    			=       []
												for mel, longer in padded_inputs:
																	input_mel.append(_UpperCamelCase      )
																	is_longer.append(_UpperCamelCase      )

												if truncation == "fusion" and sum(_UpperCamelCase      ) == 0:
																	# if no audio is longer than 10s, then randomly select one audio to be longer
																	SCREAMING_SNAKE_CASE    			=       np.random.randint(0	,							len(_UpperCamelCase      )      )
																	SCREAMING_SNAKE_CASE    			=       True

												if isinstance(input_mel[0]	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase	,							dtype=np.floataa      ) for feature in input_mel]

												# is_longer is a list of bool
												SCREAMING_SNAKE_CASE    			=       [[longer] for longer in is_longer]

												SCREAMING_SNAKE_CASE    			=       {"input_features": input_mel, "is_longer": is_longer}
												SCREAMING_SNAKE_CASE    			=       BatchFeature(_UpperCamelCase      )

												if return_tensors is not None:
																	SCREAMING_SNAKE_CASE    			=       input_features.convert_to_tensors(_UpperCamelCase      )

												return input_features




