





"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


__UpperCamelCase     :						Tuple						       =  logging.get_logger(__name__)







class       a							(   a__  ):
 snake_case__             =       ['''input_features''', '''is_longer''']



 def __init__(     self				,       _snake_case=64				,       _snake_case=4_80_00				,       _snake_case=4_80				,       _snake_case=10				,       _snake_case=10_24				,       _snake_case=0.0				,       _snake_case=False				,       _snake_case = 0				,       _snake_case = 1_40_00				,       _snake_case = None				,       _snake_case = "fusion"				,       _snake_case = "repeatpad"				,       **_snake_case				,       ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= copy.deepcopy(self.__dict__     )
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  lowerCAmelCase    				= spectrogram(
      _snake_case				,       window_function(self.fft_window_size				,       'hann'     )				,       frame_length=self.fft_window_size				,       hop_length=self.hop_length				,       power=2.0				,       mel_filters=_snake_case				,       log_mel='dB'				,       )
  return log_mel_spectrogram.T



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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

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

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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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

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

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

  return input_mel, longer



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







  """simple docstring"""






  lowerCAmelCase    				= truncation if truncation is not None else self.truncation
  lowerCAmelCase    				= 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.'     )

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

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

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

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

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

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

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

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

  lowerCAmelCase    				= {'input_features': input_mel, 'is_longer': is_longer}
  lowerCAmelCase    				= BatchFeature(_snake_case     )

  if return_tensors is not None:
   lowerCAmelCase    				= input_features.convert_to_tensors(_snake_case     )

  return input_features












"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class       a							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       IFInpaintingPipeline
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {'''latents'''}



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self._get_dummy_components()



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )

  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )
  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )

  lowerCAmelCase    				= {
      'prompt': 'A painting of a squirrel eating a burger',
      'image': image,
      'mask_image': mask_image,
      'generator': generator,
      'num_inference_steps': 2,
      'output_type': 'numpy',
  }

  return inputs



 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()				,       reason='XFormers attention is only available with CUDA and `xformers` installed'				,       )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != 'cuda'				,       reason='float16 requires CUDA'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  super().test_save_load_floataa(expected_max_diff=1E-1     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_local()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_inference_batch_single_identical(
      expected_max_diff=1E-2				,       )





