






'''simple docstring'''







import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

			from transformers import (
			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    MODEL_MAPPING,
			    DeiTForImageClassification,
			    DeiTForImageClassificationWithTeacher,
			    DeiTForMaskedImageModeling,
			    DeiTModel,
			)
			from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DeiTImageProcessor

class _lowercase	:




							def __init__(							self:				List[str]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Dict=13			,							UpperCamelCase__:				int=30			,							UpperCamelCase__:				Tuple=2			,							UpperCamelCase__:				Any=3			,							UpperCamelCase__:				List[str]=True			,							UpperCamelCase__:				Optional[Any]=True			,							UpperCamelCase__:				Optional[int]=32			,							UpperCamelCase__:				Tuple=5			,							UpperCamelCase__:				Any=4			,							UpperCamelCase__:				str=37			,							UpperCamelCase__:				Union[str, Any]="gelu"			,							UpperCamelCase__:				str=0.1			,							UpperCamelCase__:				List[Any]=0.1			,							UpperCamelCase__:				Union[str, Any]=10			,							UpperCamelCase__:				Optional[Any]=0.02			,							UpperCamelCase__:				Optional[Any]=3			,							UpperCamelCase__:				Any=None			,							UpperCamelCase__:				Any=2			,							):
														lowerCamelCase__					:	str         =       parent
														lowerCamelCase__					:	List[str]         =       batch_size
														lowerCamelCase__					:	Tuple         =       image_size
														lowerCamelCase__					:	Any         =       patch_size
														lowerCamelCase__					:	Dict         =       num_channels
														lowerCamelCase__					:	List[str]         =       is_training
														lowerCamelCase__					:	Dict         =       use_labels
														lowerCamelCase__					:	List[str]         =       hidden_size
														lowerCamelCase__					:	str         =       num_hidden_layers
														lowerCamelCase__					:	Any         =       num_attention_heads
														lowerCamelCase__					:	str         =       intermediate_size
														lowerCamelCase__					:	str         =       hidden_act
														lowerCamelCase__					:	int         =       hidden_dropout_prob
														lowerCamelCase__					:	Dict         =       attention_probs_dropout_prob
														lowerCamelCase__					:	int         =       type_sequence_label_size
														lowerCamelCase__					:	Tuple         =       initializer_range
														lowerCamelCase__					:	Optional[int]         =       scope
														lowerCamelCase__					:	Tuple         =       encoder_stride

														# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
														lowerCamelCase__					:	str         =       (image_size // patch_size) ** 2
														lowerCamelCase__					:	int         =       num_patches + 2




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Any         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	Optional[int]         =       None
														if self.use_labels:
																					lowerCamelCase__					:	Tuple         =       ids_tensor([self.batch_size]			,							self.type_sequence_label_size     )

														lowerCamelCase__					:	Optional[int]         =       self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				Dict     ):
														return DeiTConfig(
														    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			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							is_decoder=UpperCamelCase__			,							initializer_range=self.initializer_range			,							encoder_stride=self.encoder_stride			,							)




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Optional[Any]     ):
														lowerCamelCase__					:	Any         =       DeiTModel(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	Tuple         =       model(UpperCamelCase__     )
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)     )




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Tuple     ):
														lowerCamelCase__					:	List[str]         =       DeiTForMaskedImageModeling(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	Any         =       model(UpperCamelCase__     )
														self.parent.assertEqual(
														    result.reconstruction.shape			,							(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

														# test greyscale images
														lowerCamelCase__					:	List[str]         =       1
														lowerCamelCase__					:	Optional[int]         =       DeiTForMaskedImageModeling(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()

														lowerCamelCase__					:	List[str]         =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__     )
														self.parent.assertEqual(result.reconstruction.shape			,							(self.batch_size, 1, self.image_size, self.image_size)     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				List[str]     ):
														lowerCamelCase__					:	str         =       self.type_sequence_label_size
														lowerCamelCase__					:	Union[str, Any]         =       DeiTForImageClassification(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	Optional[int]         =       model(UpperCamelCase__			,							labels=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)     )

														# test greyscale images
														lowerCamelCase__					:	List[Any]         =       1
														lowerCamelCase__					:	List[Any]         =       DeiTForImageClassification(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()

														lowerCamelCase__					:	int         =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
														lowerCamelCase__					:	Optional[Any]         =       model(UpperCamelCase__			,							labels=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	List[str]         =       self.prepare_config_and_inputs()
														(
														    (
														    lowerCamelCase__
														)  ,		(
														    lowerCamelCase__
														)  ,		(
														    lowerCamelCase__
														)  ,		
														)					:	Union[str, Any]         =       config_and_inputs
														lowerCamelCase__					:	List[Any]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (
							    (
							        DeiTModel,
							        DeiTForImageClassification,
							        DeiTForImageClassificationWithTeacher,
							        DeiTForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							a				      =       (
							    {
							        """feature-extraction""": DeiTModel,
							        """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	List[str]         =       DeiTModelTester(self     )
														lowerCamelCase__					:	List[Any]         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				int     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""DeiT does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														pass




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )
																					self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module)     )
																					lowerCamelCase__					:	Any         =       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__			,							nn.Linear     )     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[Any]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	List[str]         =       inspect.signature(model.forward     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	Tuple         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	Dict         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	List[Any]         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				Dict=False     ):
														lowerCamelCase__					:	List[Any]         =       super()._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__			,							return_labels=UpperCamelCase__     )

														if return_labels:
																					if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																												del inputs_dict["labels"]

														return inputs_dict




							def 		lowerCamelCase_				(							self:				Dict     ):
														if not self.model_tester.is_training:
																					return

														lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()
														lowerCamelCase__					:	List[str]         =       True

														for model_class in self.all_model_classes:
																					# DeiTForImageClassificationWithTeacher supports inference-only
																					if (
																					    model_class in get_values(UpperCamelCase__     )
																					    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																					):
																												continue
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )
																					model.to(UpperCamelCase__     )
																					model.train()
																					lowerCamelCase__					:	Any         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__			,							return_labels=UpperCamelCase__     )
																					lowerCamelCase__					:	str         =       model(**UpperCamelCase__     ).loss
																					loss.backward()




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()
														if not self.model_tester.is_training:
																					return

														lowerCamelCase__					:	Tuple         =       False
														lowerCamelCase__					:	Any         =       True

														for model_class in self.all_model_classes:
																					if model_class in get_values(UpperCamelCase__     ) or not model_class.supports_gradient_checkpointing:
																												continue
																					# DeiTForImageClassificationWithTeacher supports inference-only
																					if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																												continue
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )
																					model.gradient_checkpointing_enable()
																					model.to(UpperCamelCase__     )
																					model.train()
																					lowerCamelCase__					:	Optional[int]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__			,							return_labels=UpperCamelCase__     )
																					lowerCamelCase__					:	int         =       model(**UpperCamelCase__     ).loss
																					loss.backward()




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__					:	str         =       [
														    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
														    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
														    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
														]

														for model_class in self.all_model_classes:
																					if (
																					    model_class
																					    not in [
																					        *get_values(UpperCamelCase__     ),
																					        *get_values(UpperCamelCase__     ),
																					    ]
																					    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																					):
																												continue

																					for problem_type in problem_types:
																												with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''     ):
																																			lowerCamelCase__					:	int         =       problem_type["""title"""]
																																			lowerCamelCase__					:	Any         =       problem_type["""num_labels"""]

																																			lowerCamelCase__					:	List[Any]         =       model_class(UpperCamelCase__     )
																																			model.to(UpperCamelCase__     )
																																			model.train()

																																			lowerCamelCase__					:	Dict         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__			,							return_labels=UpperCamelCase__     )

																																			if problem_type["num_labels"] > 1:
																																										lowerCamelCase__					:	List[Any]         =       inputs["""labels"""].unsqueeze(1     ).repeat(1			,							problem_type["""num_labels"""]     )

																																			lowerCamelCase__					:	List[Any]         =       inputs["""labels"""].to(problem_type["""dtype"""]     )

																																			# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																																			# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																																			# they have the same size." which is a symptom something in wrong for the regression problem.
																																			# See https://github.com/huggingface/transformers/issues/11780
																																			with warnings.catch_warnings(record=UpperCamelCase__     ) as warning_list:
																																										lowerCamelCase__					:	List[Any]         =       model(**UpperCamelCase__     ).loss
																																			for w in warning_list:
																																										if "Using a target size that is different to the input size" in str(w.message     ):
																																																	raise ValueError(
																																																	    F'''Something is going wrong in the regression problem: intercepted {w.message}'''     )

																																			loss.backward()




							@slow
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	Optional[Any]         =       DeiTModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       List[str]:
							lowerCamelCase__					:	List[Any]         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



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




							@cached_property
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														return (
														    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     )
														    if is_vision_available()
														    else None
														)




							@slow
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[int]         =       DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     ).to(
														    UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       self.default_image_processor
														lowerCamelCase__					:	Optional[int]         =       prepare_img()
														lowerCamelCase__					:	str         =       image_processor(images=UpperCamelCase__			,							return_tensors="""pt"""     ).to(UpperCamelCase__     )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__					:	List[str]         =       model(**UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	Union[str, Any]         =       torch.Size((1, 1_000)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       torch.tensor([-1.0_266, 0.1_912, -1.2_861]     ).to(UpperCamelCase__     )

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




							@slow
							@require_accelerate
							@require_torch_gpu
							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[Any]         =       DeiTModel.from_pretrained(
														    """facebook/deit-base-distilled-patch16-224"""			,							torch_dtype=torch.floataa			,							device_map="""auto"""     )
														lowerCamelCase__					:	List[Any]         =       self.default_image_processor

														lowerCamelCase__					:	List[str]         =       prepare_img()
														lowerCamelCase__					:	Tuple         =       image_processor(images=UpperCamelCase__			,							return_tensors="""pt"""     )
														lowerCamelCase__					:	Optional[Any]         =       inputs.pixel_values.to(UpperCamelCase__     )

														# forward pass to make sure inference works in fp16
														with torch.no_grad():
																					lowerCamelCase__					:	int         =       model(UpperCamelCase__     )


'''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


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

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						["input_features", "is_longer"]




      def __init__(       self						:				str						,					A						:				int=64						,					A						:				Dict=48000						,					A						:				str=480						,					A						:				List[Any]=10						,					A						:				Optional[Any]=1024						,					A						:				Tuple=0.0						,					A						:				List[Any]=False						,					A						:				float = 0						,					A						:				float = 14000						,					A						:				int = None						,					A						:				str = "fusion"						,					A						:				str = "repeatpad"						,					**A						:				Dict						,					):
             super().__init__(
                 feature_size=A						,					sampling_rate=A						,					padding_value=A						,					return_attention_mask=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    top_db
             _UpperCAmelCase     :  Dict							      =    truncation
             _UpperCAmelCase     :  List[Any]							      =    padding
             _UpperCAmelCase     :  Optional[Any]							      =    fft_window_size
             _UpperCAmelCase     :  Dict							      =    (fft_window_size >> 1) + 1
             _UpperCAmelCase     :  Any							      =    hop_length
             _UpperCAmelCase     :  Tuple							      =    max_length_s
             _UpperCAmelCase     :  str							      =    max_length_s * sampling_rate
             _UpperCAmelCase     :  Any							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    frequency_min
             _UpperCAmelCase     :  str							      =    frequency_max
             _UpperCAmelCase     :  Union[str, Any]							      =    mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins						,					num_mel_filters=A						,					min_frequency=A						,					max_frequency=A						,					sampling_rate=A						,					norm=A						,					mel_scale="htk"						,					)
             _UpperCAmelCase     :  Tuple							      =    mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins						,					num_mel_filters=A						,					min_frequency=A						,					max_frequency=A						,					sampling_rate=A						,					norm="slaney"						,					mel_scale="slaney"						,					)




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    copy.deepcopy(self.__dict__       )
             _UpperCAmelCase     :  Dict							      =    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      _A	(       self						:				Optional[Any]						,					A						:				np.array						,					A						:				Optional[np.array] = None       ):
             _UpperCAmelCase     :  Dict							      =    spectrogram(
                 A						,					window_function(self.fft_window_size						,					"hann"       )						,					frame_length=self.fft_window_size						,					hop_length=self.hop_length						,					power=2.0						,					mel_filters=A						,					log_mel="dB"						,					)
             return log_mel_spectrogram.T




      def      _A	(       self						:				str						,					A						:				str						,					A						:				List[str]						,					A						:				List[Any]       ):
             _UpperCAmelCase     :  List[str]							      =    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
                    _UpperCAmelCase     :  Optional[Any]							      =    [0]
             if len(ranges[2]       ) == 0:
                    # if the audio is too short, we just use the first chunk
                    _UpperCAmelCase     :  Tuple							      =    [0]
             # randomly choose index for each part
             _UpperCAmelCase     :  Dict							      =    np.random.choice(ranges[0]       )
             _UpperCAmelCase     :  str							      =    np.random.choice(ranges[1]       )
             _UpperCAmelCase     :  Tuple							      =    np.random.choice(ranges[2]       )

             _UpperCAmelCase     :  str							      =    mel[idx_front : idx_front + chunk_frames, :]
             _UpperCAmelCase     :  str							      =    mel[idx_middle : idx_middle + chunk_frames, :]
             _UpperCAmelCase     :  List[Any]							      =    mel[idx_back : idx_back + chunk_frames, :]

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




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

             else:
                    _UpperCAmelCase     :  Optional[Any]							      =    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":
                                  _UpperCAmelCase     :  str							      =    int(max_length / len(A       )       )
                                  _UpperCAmelCase     :  Dict							      =    np.stack(np.tile(A						,					n_repeat + 1       )       )[:max_length]
                           if padding == "repeatpad":
                                  _UpperCAmelCase     :  Dict							      =    int(max_length / len(A       )       )
                                  _UpperCAmelCase     :  List[str]							      =    np.stack(np.tile(A						,					A       )       )
                           _UpperCAmelCase     :  Optional[Any]							      =    np.pad(A						,					(0, max_length - waveform.shape[0])						,					mode="constant"						,					constant_values=0       )

                    if truncation == "fusion":
                           _UpperCAmelCase     :  str							      =    self._np_extract_fbank_features(A						,					self.mel_filters       )
                           _UpperCAmelCase     :  Optional[int]							      =    np.stack([input_mel, input_mel, input_mel, input_mel]						,					axis=0       )
                    else:
                           _UpperCAmelCase     :  List[str]							      =    self._np_extract_fbank_features(A						,					self.mel_filters_slaney       )[None, :]

             return input_mel, longer





      def __call__(       self						:				Union[str, Any]						,					A						:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,					A						:				str = None						,					A						:				Optional[str] = None						,					A						:				Optional[int] = None						,					A						:				Optional[int] = None						,					A						:				Optional[Union[str, TensorType]] = None						,					**A						:				List[str]						,					):
             _UpperCAmelCase     :  int							      =    truncation if truncation is not None else self.truncation
             _UpperCAmelCase     :  Optional[int]							      =    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."       )

             _UpperCAmelCase     :  Any							      =    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}"""       )
             _UpperCAmelCase     :  Optional[Any]							      =    is_batched_numpy or (
                 isinstance(A						,					(list, tuple)       ) and (isinstance(raw_speech[0]						,					(np.ndarray, tuple, list)       ))
             )

             if is_batched:
                    _UpperCAmelCase     :  int							      =    [np.asarray(A						,					dtype=np.floataa       ) for speech in raw_speech]
             elif not is_batched and not isinstance(A						,					np.ndarray       ):
                    _UpperCAmelCase     :  List[str]							      =    np.asarray(A						,					dtype=np.floataa       )
             elif isinstance(A						,					np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
                    _UpperCAmelCase     :  Any							      =    raw_speech.astype(np.floataa       )

             # always return batch
             if not is_batched:
                    _UpperCAmelCase     :  List[str]							      =    [np.asarray(A       )]

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

             _UpperCAmelCase     :  int							      =    []
             _UpperCAmelCase     :  Optional[Any]							      =    []
             for mel, longer in padded_inputs:
                    input_mel.append(A       )
                    is_longer.append(A       )

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

             if isinstance(input_mel[0]						,					A       ):
                    _UpperCAmelCase     :  List[str]							      =    [np.asarray(A						,					dtype=np.floataa       ) for feature in input_mel]

             # is_longer is a list of bool
             _UpperCAmelCase     :  Tuple							      =    [[longer] for longer in is_longer]

             _UpperCAmelCase     :  Optional[Any]							      =    {"input_features": input_mel, "is_longer": is_longer}
             _UpperCAmelCase     :  Tuple							      =    BatchFeature(A       )

             if return_tensors is not None:
                    _UpperCAmelCase     :  List[Any]							      =    input_features.convert_to_tensors(A       )

             return input_features

