

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


a_    :						str    		=      logging.get_logger(__name__)




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =["input_features", "is_longer"]

    def __init__(			self :      int							,    snake_case__ :      List[Any]=6_4							,    snake_case__ :      Any=4_8_0_0_0							,    snake_case__ :      Tuple=4_8_0							,    snake_case__ :      Union[str, Any]=1_0							,    snake_case__ :      Dict=1_0_2_4							,    snake_case__ :      str=0.0							,    snake_case__ :      Any=False							,    snake_case__ :      float = 0							,    snake_case__ :      float = 1_4_0_0_0							,    snake_case__ :      int = None							,    snake_case__ :      str = "fusion"							,    snake_case__ :      str = "repeatpad"							,    **snake_case__ :      List[Any]							,    ):

     """simple docstring"""






     super().__init__(
         feature_size=snake_case__							,    sampling_rate=snake_case__							,    padding_value=snake_case__							,    return_attention_mask=snake_case__							,    **snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			top_db
     SCREAMING_SNAKE_CASE								=			truncation
     SCREAMING_SNAKE_CASE								=			padding
     SCREAMING_SNAKE_CASE								=			fft_window_size
     SCREAMING_SNAKE_CASE								=			(fft_window_size >> 1) + 1
     SCREAMING_SNAKE_CASE								=			hop_length
     SCREAMING_SNAKE_CASE								=			max_length_s
     SCREAMING_SNAKE_CASE								=			max_length_s * sampling_rate
     SCREAMING_SNAKE_CASE								=			sampling_rate
     SCREAMING_SNAKE_CASE								=			frequency_min
     SCREAMING_SNAKE_CASE								=			frequency_max
     SCREAMING_SNAKE_CASE								=			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'							,    )
     SCREAMING_SNAKE_CASE								=			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 :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			copy.deepcopy(self.__dict__    )
     SCREAMING_SNAKE_CASE								=			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							,    snake_case__ :      np.array							,    snake_case__ :      Optional[np.array] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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 :      str							,    snake_case__ :      Any							,    snake_case__ :      List[Any]							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE								=			torch.tensor(mel[None, None, :]    )
     SCREAMING_SNAKE_CASE								=			torch.nn.functional.interpolate(
         snake_case__							,    size=[chunk_frames, 6_4]							,    mode='bilinear'							,    align_corners=snake_case__    )
     SCREAMING_SNAKE_CASE								=			mel_shrink[0][0].numpy()
     SCREAMING_SNAKE_CASE								=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]							,    axis=0    )
     return mel_fusion

    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      np.array							,    snake_case__ :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      str    ):

     """simple docstring"""






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

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

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

     return input_mel, longer




    def __call__(			self :      Optional[int]							,    snake_case__ :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,    snake_case__ :      str = None							,    snake_case__ :      Optional[str] = None							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[Union[str, TensorType]] = None							,    **snake_case__ :      List[str]							,    ):

     """simple docstring"""






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

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

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

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

     # convert to mel spectrogram, truncate and pad if needed.
     SCREAMING_SNAKE_CASE								=			[
         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
     ]

     SCREAMING_SNAKE_CASE								=			[]
     SCREAMING_SNAKE_CASE								=			[]
     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
      SCREAMING_SNAKE_CASE								=			np.random.randint(0							,    len(snake_case__    )    )
      SCREAMING_SNAKE_CASE								=			True

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

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

     SCREAMING_SNAKE_CASE								=			{'input_features': input_mel, 'is_longer': is_longer}
     SCREAMING_SNAKE_CASE								=			BatchFeature(snake_case__    )

     if return_tensors is not None:
      SCREAMING_SNAKE_CASE								=			input_features.convert_to_tensors(snake_case__    )

     return input_features




# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)

