



from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor


class   __lowerCAmelCase  :
					"""simple docstring"""






					def __init__(  self       :							Any					,       _snake_case       :							str					,       _snake_case       :							Optional[Any]=13					,       _snake_case       :							Dict=30					,       _snake_case       :							Union[str, Any]=2					,       _snake_case       :							Union[str, Any]=3					,       _snake_case       :							Any=True					,       _snake_case       :							List[str]=True					,       _snake_case       :							str=32					,       _snake_case       :							str=2					,       _snake_case       :							Union[str, Any]=4					,       _snake_case       :							Union[str, Any]=37					,       _snake_case       :							Union[str, Any]="gelu"					,       _snake_case       :							Union[str, Any]=0.1					,       _snake_case       :							int=0.1					,       _snake_case       :							Union[str, Any]=10					,       _snake_case       :							Any=0.02					,       _snake_case       :							List[Any]=3					,       _snake_case       :							str=0.6					,       _snake_case       :							Tuple=None					,       ):






								"""simple docstring"""


								A__	  =     parent
								A__	  =     batch_size
								A__	  =     image_size
								A__	  =     patch_size
								A__	  =     num_channels
								A__	  =     is_training
								A__	  =     use_labels
								A__	  =     hidden_size
								A__	  =     num_hidden_layers
								A__	  =     num_attention_heads
								A__	  =     intermediate_size
								A__	  =     hidden_act
								A__	  =     hidden_dropout_prob
								A__	  =     attention_probs_dropout_prob
								A__	  =     type_sequence_label_size
								A__	  =     initializer_range
								A__	  =     mask_ratio
								A__	  =     scope

								# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
								# (we add 1 for the [CLS] token)
								A__	  =     (image_size // patch_size) ** 2
								A__	  =     int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


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

								A__	  =     None
								if self.use_labels:
											A__	  =     ids_tensor([self.batch_size]					,       self.type_sequence_label_size       )

								A__	  =     self.get_config()

								return config, pixel_values, labels



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								return ViTMAEConfig(
								    image_size=self.image_size					,       patch_size=self.patch_size					,       num_channels=self.num_channels					,       hidden_size=self.hidden_size					,       num_hidden_layers=self.num_hidden_layers					,       num_attention_heads=self.num_attention_heads					,       intermediate_size=self.intermediate_size					,       decoder_hidden_size=self.hidden_size					,       decoder_num_hidden_layers=self.num_hidden_layers					,       decoder_num_attention_heads=self.num_attention_heads					,       decoder_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					,       is_decoder=_snake_case					,       initializer_range=self.initializer_range					,       mask_ratio=self.mask_ratio					,       )



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Any					,       _snake_case       :							Dict					,       _snake_case       :							Any       ):






								"""simple docstring"""


								A__	  =     TFViTMAEModel(config=_snake_case       )
								A__	  =     model(_snake_case					,       training=_snake_case       )
								self.parent.assertEqual(result.last_hidden_state.shape					,       (self.batch_size, self.seq_length, self.hidden_size)       )



					def      _a				(  self       :							Any					,       _snake_case       :							Dict					,       _snake_case       :							int					,       _snake_case       :							Any       ):






								"""simple docstring"""


								A__	  =     TFViTMAEForPreTraining(_snake_case       )
								A__	  =     model(_snake_case					,       training=_snake_case       )
								# expected sequence length = num_patches
								A__	  =     (self.image_size // self.patch_size) ** 2
								A__	  =     self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape					,       (self.batch_size, num_patches, expected_num_channels)       )

								# test greyscale images
								A__	  =     1
								A__	  =     TFViTMAEForPreTraining(_snake_case       )

								A__	  =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
								A__	  =     model(_snake_case					,       training=_snake_case       )
								A__	  =     self.patch_size**2
								self.parent.assertEqual(result.logits.shape					,       (self.batch_size, num_patches, expected_num_channels)       )



					def      _a				(  self       :							int       ):






								"""simple docstring"""


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







@require_tf
class   __lowerCAmelCase  (		UpperCAmelCase_				,  UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Any      							=     (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
					A__					:		Tuple      							=     {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

					A__					:		Dict      							=     False
					A__					:		Dict      							=     False
					A__					:		str      							=     False
					A__					:		List[Any]      							=     False



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     TFViTMAEModelTester(self       )
								A__	  =     ConfigTester(self					,       config_class=_snake_case					,       has_text_modality=_snake_case					,       hidden_size=37       )



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					@unittest.skip(reason='ViTMAE does not use inputs_embeds'       )
					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								pass



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__	  =     model_class(_snake_case       )
											self.assertIsInstance(model.get_input_embeddings()					,       (tf.keras.layers.Layer)       )
											A__	  =     model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_snake_case					,       tf.keras.layers.Layer       )       )



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

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

											A__	  =     ['pixel_values']
											self.assertListEqual(arg_names[:1]					,       _snake_case       )



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*_snake_case       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								np.random.seed(2       )

								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

								A__	  =     int((config.image_size // config.patch_size) ** 2       )
								A__	  =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								for model_class in self.all_model_classes:
											A__	  =     model_class(_snake_case       )
											A__	  =     self._prepare_for_class(_snake_case					,       _snake_case       )

											A__	  =     model(_snake_case					,       noise=_snake_case       )

											A__	  =     copy.deepcopy(self._prepare_for_class(_snake_case					,       _snake_case       )       )
											A__	  =     model(**_snake_case					,       noise=_snake_case       )
											A__	  =     outputs_dict[0].numpy()
											A__	  =     outputs_keywords[0].numpy()

											self.assertLess(np.sum(np.abs(output_dict - output_keywords       )       )					,       1E-6       )



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								np.random.seed(2       )

								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

								A__	  =     int((config.image_size // config.patch_size) ** 2       )
								A__	  =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								def prepare_numpy_arrays(_snake_case       :							str       ):
											A__	  =     {}
											for k, v in inputs_dict.items():
														if tf.is_tensor(_snake_case       ):
																	A__	  =     v.numpy()
														else:
																	A__	  =     np.array(_snake_case       )

											return inputs_np_dict

								for model_class in self.all_model_classes:
											A__	  =     model_class(_snake_case       )

											A__	  =     self._prepare_for_class(_snake_case					,       _snake_case       )
											A__	  =     prepare_numpy_arrays(_snake_case       )

											A__	  =     model(_snake_case					,       noise=_snake_case       )
											A__	  =     model(**_snake_case					,       noise=_snake_case       )
											self.assert_outputs_same(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							Dict					,       _snake_case       :							Any					,       _snake_case       :							Optional[Any]       ):






								"""simple docstring"""


								np.random.seed(2       )

								A__	  =     int((tf_model.config.image_size // tf_model.config.patch_size) ** 2       )
								A__	  =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
								A__	  =     tf.constant(_snake_case       )

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								A__	  =     tf_noise

								super().check_pt_tf_models(_snake_case					,       _snake_case					,       _snake_case       )



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								np.random.seed(2       )

								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

								A__	  =     {
								    module_member
								    for model_class in self.all_model_classes
								    for module in (import_module(model_class.__module__       ),)
								    for module_member_name in dir(_snake_case       )
								    if module_member_name.endswith('MainLayer'       )
								    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
								    and module_member_name[: -len('MainLayer'       )] == model_class.__name__[: -len('Model'       )]
								    for module_member in (getattr(_snake_case					,       _snake_case       ),)
								    if isinstance(_snake_case					,       _snake_case       )
								    and tf.keras.layers.Layer in module_member.__bases__
								    and getattr(_snake_case					,       '_keras_serializable'					,       _snake_case       )
								}

								A__	  =     int((config.image_size // config.patch_size) ** 2       )
								A__	  =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
								A__	  =     tf.convert_to_tensor(_snake_case       )
								inputs_dict.update({'noise': noise}       )

								for main_layer_class in tf_main_layer_classes:
											A__	  =     main_layer_class(_snake_case       )

											A__	  =     {
											    name: tf.keras.Input(tensor.shape[1:]					,       dtype=tensor.dtype       ) for name, tensor in inputs_dict.items()
											}

											A__	  =     tf.keras.Model(_snake_case					,       outputs=main_layer(_snake_case       )       )
											A__	  =     model(_snake_case       )

											with tempfile.TemporaryDirectory() as tmpdirname:
														A__	  =     os.path.join(_snake_case					,       'keras_model.h5'       )
														model.save(_snake_case       )
														A__	  =     tf.keras.models.load_model(
														    _snake_case					,       custom_objects={main_layer_class.__name__: main_layer_class}       )
														assert isinstance(_snake_case					,       tf.keras.Model       )
														A__	  =     model(_snake_case       )
														self.assert_outputs_same(_snake_case					,       _snake_case       )



					@slow
					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								np.random.seed(2       )

								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

								A__	  =     int((config.image_size // config.patch_size) ** 2       )
								A__	  =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								for model_class in self.all_model_classes:
											A__	  =     model_class(_snake_case       )
											A__	  =     self._prepare_for_class(_snake_case					,       _snake_case       )
											A__	  =     model(_snake_case					,       noise=_snake_case       )

											if model_class.__name__ == "TFViTMAEModel":
														A__	  =     outputs.last_hidden_state.numpy()
														A__	  =     0
											else:
														A__	  =     outputs.logits.numpy()
														A__	  =     0

											with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(_snake_case					,       saved_model=_snake_case       )
														A__	  =     model_class.from_pretrained(_snake_case       )
														A__	  =     model(_snake_case					,       noise=_snake_case       )

														if model_class.__name__ == "TFViTMAEModel":
																	A__	  =     after_outputs['last_hidden_state'].numpy()
																	A__	  =     0
														else:
																	A__	  =     after_outputs['logits'].numpy()
																	A__	  =     0

														A__	  =     np.amax(np.abs(out_a - out_a       )       )
														self.assertLessEqual(_snake_case					,       1E-5       )



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								np.random.seed(2       )

								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

								A__	  =     int((config.image_size // config.patch_size) ** 2       )
								A__	  =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

								for model_class in self.all_model_classes:
											A__	  =     model_class(_snake_case       )
											A__	  =     self._prepare_for_class(_snake_case					,       _snake_case       )

											A__	  =     model(_snake_case					,       noise=_snake_case       )
											A__	  =     model.get_config()
											# make sure that returned config is jsonifiable, which is required by keras
											json.dumps(_snake_case       )
											A__	  =     model_class.from_config(model.get_config()       )
											# make sure it also accepts a normal config
											A__	  =     model_class.from_config(model.config       )
											A__	  =     new_model(_snake_case       )  # Build model
											new_model.set_weights(model.get_weights()       )
											A__	  =     new_model(_snake_case					,       noise=_snake_case       )

											self.assert_outputs_same(_snake_case					,       _snake_case       )



					@unittest.skip(
					    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'       )
					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								pass



					@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'       )
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								pass



					@slow
					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'       )
								self.assertIsNotNone(_snake_case       )







def A    (							)    ->   Dict:
			A__	  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
			return image






@require_tf
@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					@cached_property
					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'       ) if is_vision_available() else None



					@slow
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								np.random.seed(2       )

								A__	  =     TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'       )

								A__	  =     self.default_image_processor
								A__	  =     prepare_img()
								A__	  =     image_processor(images=_snake_case					,       return_tensors='tf'       )

								# prepare a noise vector that will be also used for testing the TF model
								# (this way we can ensure that the PT and TF models operate on the same inputs)
								A__	  =     ViTMAEConfig()
								A__	  =     int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
								A__	  =     np.random.uniform(size=(1, num_patches)       )

								# forward pass
								A__	  =     model(**_snake_case					,       noise=_snake_case       )

								# verify the logits
								A__	  =     tf.convert_to_tensor([1, 1_96, 7_68]       )
								self.assertEqual(outputs.logits.shape					,       _snake_case       )

								A__	  =     tf.convert_to_tensor(
								    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]       )

								tf.debugging.assert_near(outputs.logits[0, :3, :3]					,       _snake_case					,       atol=1E-4       )









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

import numpy as np

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


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Optional[Any]      							=     ["input_values", "attention_mask"]



					def __init__(  self       :							str					,       _snake_case       :							int = 1					,       _snake_case       :							int = 1_60_00					,       _snake_case       :							float = 0.0					,       _snake_case       :							bool = False					,       _snake_case       :							int = 80					,       _snake_case       :							int = 16					,       _snake_case       :							int = 64					,       _snake_case       :							str = "hann_window"					,       _snake_case       :							float = 1.0					,       _snake_case       :							float = 80					,       _snake_case       :							float = 76_00					,       _snake_case       :							float = 1E-10					,       _snake_case       :							int = 2					,       _snake_case       :							bool = True					,       **_snake_case       :							Union[str, Any]					,       ):






								"""simple docstring"""


								super().__init__(feature_size=_snake_case					,       sampling_rate=_snake_case					,       padding_value=_snake_case					,       **_snake_case       )
								A__	  =     do_normalize
								A__	  =     return_attention_mask

								A__	  =     num_mel_bins
								A__	  =     hop_length
								A__	  =     win_length
								A__	  =     win_function
								A__	  =     frame_signal_scale
								A__	  =     fmin
								A__	  =     fmax
								A__	  =     mel_floor
								A__	  =     reduction_factor

								A__	  =     win_length * sampling_rate // 10_00
								A__	  =     hop_length * sampling_rate // 10_00
								A__	  =     optimal_fft_length(self.sample_size       )
								A__	  =     (self.n_fft // 2) + 1

								A__	  =     window_function(window_length=self.sample_size					,       name=self.win_function					,       periodic=_snake_case       )

								A__	  =     mel_filter_bank(
								    num_frequency_bins=self.n_freqs					,       num_mel_filters=self.num_mel_bins					,       min_frequency=self.fmin					,       max_frequency=self.fmax					,       sampling_rate=self.sampling_rate					,       norm='slaney'					,       mel_scale='slaney'					,       )

								if frame_signal_scale != 1.0:
											warnings.warn(
											    'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'					,       _snake_case					,       )
								if reduction_factor != 2.0:
											warnings.warn(
											    'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'					,       _snake_case					,       )



					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def      _a				(  _snake_case       :							List[np.ndarray]					,       _snake_case       :							List[np.ndarray]					,       _snake_case       :							float = 0.0       ):






								"""simple docstring"""


								if attention_mask is not None:
											A__	  =     np.array(_snake_case					,       np.intaa       )
											A__	  =     []

											for vector, length in zip(_snake_case					,       attention_mask.sum(-1       )       ):
														A__	  =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7       )
														if length < normed_slice.shape[0]:
																	A__	  =     padding_value

														normed_input_values.append(_snake_case       )
								else:
											A__	  =     [(x - x.mean()) / np.sqrt(x.var() + 1E-7       ) for x in input_values]

								return normed_input_values



					def      _a				(  self       :							Tuple					,       _snake_case       :							np.ndarray					,       ):






								"""simple docstring"""


								A__	  =     spectrogram(
								    _snake_case					,       window=self.window					,       frame_length=self.sample_size					,       hop_length=self.sample_stride					,       fft_length=self.n_fft					,       mel_filters=self.mel_filters					,       mel_floor=self.mel_floor					,       log_mel='log10'					,       )
								return log_mel_spec.T



					def __call__(  self       :							List[str]					,       _snake_case       :							Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None					,       _snake_case       :							Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None					,       _snake_case       :							Union[bool, str, PaddingStrategy] = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							bool = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       _snake_case       :							Optional[int] = None					,       **_snake_case       :							Tuple					,       ):






								"""simple docstring"""


								if audio is None and audio_target is None:
											raise ValueError('You must provide either `audio` or `audio_target` values.'       )

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

								if audio is not None:
											A__	  =     self._process_audio(
											    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       **_snake_case					,       )
								else:
											A__	  =     None

								if audio_target is not None:
											A__	  =     self._process_audio(
											    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       **_snake_case					,       )

											if inputs is None:
														return inputs_target
											else:
														A__	  =     inputs_target['input_values']
														A__	  =     inputs_target.get('attention_mask'       )
														if decoder_attention_mask is not None:
																	A__	  =     decoder_attention_mask

								return inputs



					def      _a				(  self       :							Tuple					,       _snake_case       :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,       _snake_case       :							bool = False					,       _snake_case       :							Union[bool, str, PaddingStrategy] = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							bool = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       **_snake_case       :							Tuple					,       ):






								"""simple docstring"""


								A__	  =     isinstance(_snake_case					,       np.ndarray       ) and len(speech.shape       ) > 1
								if is_batched_numpy and len(speech.shape       ) > 2:
											raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''       )
								A__	  =     is_batched_numpy or (
								    isinstance(_snake_case					,       (list, tuple)       ) and (isinstance(speech[0]					,       (np.ndarray, tuple, list)       ))
								)

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

								# always return batch
								if not is_batched:
											A__	  =     [speech]

								# needed to make pad() work on spectrogram inputs
								A__	  =     self.feature_size

								# convert into correct format for padding
								if is_target:
											A__	  =     [self._extract_mel_features(_snake_case       ) for waveform in speech]
											A__	  =     BatchFeature({'input_values': features}       )
											A__	  =     self.num_mel_bins
								else:
											A__	  =     BatchFeature({'input_values': speech}       )

								A__	  =     self.pad(
								    _snake_case					,       padding=_snake_case					,       max_length=_snake_case					,       truncation=_snake_case					,       pad_to_multiple_of=_snake_case					,       return_attention_mask=_snake_case					,       **_snake_case					,       )

								A__	  =     feature_size_hack

								# convert input values to correct format
								A__	  =     padded_inputs['input_values']
								if not isinstance(input_values[0]					,       np.ndarray       ):
											A__	  =     [np.asarray(_snake_case					,       dtype=np.floataa       ) for array in input_values]
								elif (
								    not isinstance(_snake_case					,       np.ndarray       )
								    and isinstance(input_values[0]					,       np.ndarray       )
								    and input_values[0].dtype is np.dtype(np.floataa       )
								):
											A__	  =     [array.astype(np.floataa       ) for array in input_values]
								elif isinstance(_snake_case					,       np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
											A__	  =     input_values.astype(np.floataa       )

								# convert attention_mask to correct format
								A__	  =     padded_inputs.get('attention_mask'       )
								if attention_mask is not None:
											A__	  =     [np.asarray(_snake_case					,       dtype=np.intaa       ) for array in attention_mask]

								# zero-mean and unit-variance normalization
								if not is_target and self.do_normalize:
											A__	  =     (
											    attention_mask
											    if self._get_padding_strategies(_snake_case					,       max_length=_snake_case       ) is not PaddingStrategy.DO_NOT_PAD
											    else None
											)
											A__	  =     self.zero_mean_unit_var_norm(
											    padded_inputs['input_values']					,       attention_mask=_snake_case					,       padding_value=self.padding_value       )

								if return_tensors is not None:
											A__	  =     padded_inputs.convert_to_tensors(_snake_case       )

								return padded_inputs



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     super().to_dict()

								# Don't serialize these as they are derived from the other properties.
								A__	  =     ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
								for name in names:
											if name in output:
														del output[name]

								return output




