




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

import numpy as np
import torch

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


__lowerCAmelCase		:				List[Any]       			=		logging.get_logger(__name__)




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					["""input_features""", """is_longer"""]





				def __init__(      self				:     Dict					,      __lowerCamelCase				:     List[Any]=64					,      __lowerCamelCase				:     Union[str, Any]=4_80_00					,      __lowerCamelCase				:     Optional[int]=4_80					,      __lowerCamelCase				:     List[Any]=10					,      __lowerCamelCase				:     str=10_24					,      __lowerCamelCase				:     Optional[Any]=0.0					,      __lowerCamelCase				:     int=False					,      __lowerCamelCase				:     float = 0					,      __lowerCamelCase				:     float = 1_40_00					,      __lowerCamelCase				:     int = None					,      __lowerCamelCase				:     str = "fusion"					,      __lowerCamelCase				:     str = "repeatpad"					,      **__lowerCamelCase				:     Any					,      )					->     Optional[int]:
							super().__init__(
							    feature_size=__lowerCamelCase					,      sampling_rate=__lowerCamelCase					,      padding_value=__lowerCamelCase					,      return_attention_mask=__lowerCamelCase					,      **__lowerCamelCase					,      )
							a    =			top_db
							a    =			truncation
							a    =			padding
							a    =			fft_window_size
							a    =			(fft_window_size >> 1) + 1
							a    =			hop_length
							a    =			max_length_s
							a    =			max_length_s * sampling_rate
							a    =			sampling_rate
							a    =			frequency_min
							a    =			frequency_max
							a    =			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"					,      )
							a    =			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 							__UpperCAmelCase							(      self				:     Any							)					->     Dict[str, Any]:
							a    =			copy.deepcopy(self.__dict__							)
							a    =			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				:     str					,      __lowerCamelCase				:     np.array					,      __lowerCamelCase				:     Optional[np.array] = None							)					->     np.ndarray:
							a    =			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 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Dict							)					->     List[Any]:
							a    =			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
										a    =			[0]
							if len(ranges[2]							) == 0:
										# if the audio is too short, we just use the first chunk
										a    =			[0]
							# randomly choose index for each part
							a    =			np.random.choice(ranges[0]							)
							a    =			np.random.choice(ranges[1]							)
							a    =			np.random.choice(ranges[2]							)

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

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





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

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

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

							return input_mel, longer





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

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

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

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

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

							a    =			[]
							a    =			[]
							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
										a    =			np.random.randint(0					,      len(__lowerCamelCase							)							)
										a    =			True

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

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

							a    =			{"input_features": input_mel, "is_longer": is_longer}
							a    =			BatchFeature(__lowerCamelCase							)

							if return_tensors is not None:
										a    =			input_features.convert_to_tensors(__lowerCamelCase							)

							return input_features












import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)






