
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


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

class __snake_case						( lowerCAmelCase	):
    _a						:		List[Any]=							["input_features", "is_longer"]
    def __init__(    self						,snake_case=64						,snake_case=48000						,snake_case=480						,snake_case=10						,snake_case=1024						,snake_case=0.0						,snake_case=False						,snake_case = 0						,snake_case = 14000						,snake_case = None						,snake_case = "fusion"						,snake_case = "repeatpad"						,**snake_case						,):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase				:	List[str]			      = copy.deepcopy(self.__dict__ )
           lowercase				:	Union[str, Any]			      = 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  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case = None ):



           '''simple docstring'''







           lowercase				:	List[str]			      = spectrogram(
               lowerCamelCase__						,window_function(self.fft_window_size						,"""hann""" )						,frame_length=self.fft_window_size						,hop_length=self.hop_length						,power=2.0						,mel_filters=lowerCamelCase__						,log_mel="""dB"""						,)
           return log_mel_spectrogram.T
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case						,snake_case ):



           '''simple docstring'''







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

           lowercase				:	str			      = mel[idx_front : idx_front + chunk_frames, :]
           lowercase				:	int			      = mel[idx_middle : idx_middle + chunk_frames, :]
           lowercase				:	str			      = mel[idx_back : idx_back + chunk_frames, :]

           lowercase				:	Any			      = torch.tensor(mel[None, None, :] )
           lowercase				:	List[str]			      = torch.nn.functional.interpolate(
               lowerCamelCase__						,size=[chunk_frames, 64]						,mode="""bilinear"""						,align_corners=lowerCamelCase__ )
           lowercase				:	Tuple			      = mel_shrink[0][0].numpy()
           lowercase				:	List[Any]			      = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]						,axis=0 )
           return mel_fusion
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case						,snake_case						,snake_case ):



           '''simple docstring'''







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

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

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

           return input_mel, longer
    def __call__(    self						,snake_case						,snake_case = None						,snake_case = None						,snake_case = None						,snake_case = None						,snake_case = None						,**snake_case						,):



           '''simple docstring'''







           lowercase				:	List[str]			      = truncation if truncation is not None else self.truncation
           lowercase				:	str			      = 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.""" )

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

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

           # always return batch
           if not is_batched:
                  lowercase				:	Dict			      = [np.asarray(lowerCamelCase__ )]

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

           lowercase				:	Optional[Any]			      = []
           lowercase				:	Tuple			      = []
           for mel, longer in padded_inputs:
                  input_mel.append(lowerCamelCase__ )
                  is_longer.append(lowerCamelCase__ )

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

           if isinstance(input_mel[0]						,lowerCamelCase__ ):
                  lowercase				:	Tuple			      = [np.asarray(lowerCamelCase__						,dtype=np.floataa ) for feature in input_mel]

           # is_longer is a list of bool
           lowercase				:	str			      = [[longer] for longer in is_longer]

           lowercase				:	Any			      = {'input_features': input_mel, 'is_longer': is_longer}
           lowercase				:	Optional[int]			      = BatchFeature(lowerCamelCase__ )

           if return_tensors is not None:
                  lowercase				:	Optional[int]			      = input_features.convert_to_tensors(lowerCamelCase__ )

           return input_features





'''simple docstring'''
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class 				lowercase__      (				unittest.TestCase  ):





	def __init__(					self   :							int					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[Any]=13					,lowerCamelCase__   :							List[Any]=3					,lowerCamelCase__   :							Optional[Any]=224					,lowerCamelCase__   :							int=30					,lowerCamelCase__   :							str=400					,lowerCamelCase__   :							str=True					,lowerCamelCase__   :							Any=None					,lowerCamelCase__   :							Tuple=True					,lowerCamelCase__   :							Tuple=[0.5, 0.5, 0.5]					,lowerCamelCase__   :							Optional[int]=[0.5, 0.5, 0.5]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  size if size is not None else {'height': 18, 'width': 18}
							_UpperCamelCase   :						List[Any]												=  parent
							_UpperCamelCase   :						Union[str, Any]												=  batch_size
							_UpperCamelCase   :						Dict												=  num_channels
							_UpperCamelCase   :						List[str]												=  image_size
							_UpperCamelCase   :						Optional[Any]												=  min_resolution
							_UpperCamelCase   :						Dict												=  max_resolution
							_UpperCamelCase   :						Tuple												=  do_resize
							_UpperCamelCase   :						Optional[int]												=  size
							_UpperCamelCase   :						Tuple												=  do_normalize
							_UpperCamelCase   :						str												=  image_mean
							_UpperCamelCase   :						int												=  image_std





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}






@require_torch
@require_vision
class 				lowercase__      (				lowercase		,   unittest.TestCase  ):
	lowercase__    							=     ViTImageProcessor if is_vision_available() else None





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  EfficientFormerImageProcessorTester(self					)





	@property
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							return self.image_proc_tester.prepare_image_processor_dict()





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_mean'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_std'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_normalize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_resize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'size'					)					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						str												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							_UpperCamelCase   :						Optional[Any]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,Image.Image					)

							# Test not batched input
							_UpperCamelCase   :						int												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						int												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							_UpperCamelCase   :						Optional[int]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,numpify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,np.ndarray					)

							# Test not batched input
							_UpperCamelCase   :						Union[str, Any]												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						Optional[Any]												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							_UpperCamelCase   :						Any												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,torchify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,torch.Tensor					)

							# Test not batched input
							_UpperCamelCase   :						Dict												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						Any												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)


