






'''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							=logging.get_logger(__name__)




class     A    (      UpperCamelCase__						):






 """simple docstring"""





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

       UpperCamelCase_				:   List[Any]					    =							mel[idx_front : idx_front + chunk_frames, :]
       UpperCamelCase_				:   Dict					    =							mel[idx_middle : idx_middle + chunk_frames, :]
       UpperCamelCase_				:   Union[str, Any]					    =							mel[idx_back : idx_back + chunk_frames, :]

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



       if waveform.shape[0] > max_length:
             if truncation == "rand_trunc":
                   UpperCamelCase_				:   List[Any]					    =							True
                   # random crop to max_length (for compatibility) -> this should be handled by self.pad
                   UpperCamelCase_				:   Union[str, Any]					    =							len(__A ) - max_length
                   UpperCamelCase_				:   Union[str, Any]					    =							np.random.randint(0			,     overflow + 1 )
                   UpperCamelCase_				:   Optional[int]					    =							waveform[idx : idx + max_length]
                   UpperCamelCase_				:   Optional[int]					    =							self._np_extract_fbank_features(__A			,     self.mel_filters_slaney )[None, :]
             elif truncation == "fusion":
                   UpperCamelCase_				:   str					    =							self._np_extract_fbank_features(__A			,     self.mel_filters )
                   UpperCamelCase_				:   Optional[Any]					    =							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_				:   str					    =							np.stack([mel, mel, mel, mel]			,     axis=0 )
                         UpperCamelCase_				:   int					    =							False
                   else:
                         UpperCamelCase_				:   Dict					    =							self._random_mel_fusion(__A			,     __A			,     __A )
                         UpperCamelCase_				:   Any					    =							True
             else:
                   raise NotImplementedError(F"data_truncating {truncation} not implemented" )

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

             if truncation == "fusion":
                   UpperCamelCase_				:   Optional[Any]					    =							self._np_extract_fbank_features(__A			,     self.mel_filters )
                   UpperCamelCase_				:   Any					    =							np.stack([input_mel, input_mel, input_mel, input_mel]			,     axis=0 )
             else:
                   UpperCamelCase_				:   Union[str, Any]					    =							self._np_extract_fbank_features(__A			,     self.mel_filters_slaney )[None, :]

       return input_mel, longer





 def __call__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   Optional[Any]					    =							truncation if truncation is not None else self.truncation
       UpperCamelCase_				:   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[int]					    =							is_batched_numpy or (
           isinstance(__A			,     (list, tuple) ) and (isinstance(raw_speech[0]			,     (np.ndarray, tuple, list) ))
       )

       if is_batched:
             UpperCamelCase_				:   Union[str, Any]					    =							[np.asarray(__A			,     dtype=np.floataa ) for speech in raw_speech]
       elif not is_batched and not isinstance(__A			,     np.ndarray ):
             UpperCamelCase_				:   Optional[Any]					    =							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_				:   Optional[int]					    =							[np.asarray(__A )]

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

       UpperCamelCase_				:   Optional[Any]					    =							[]
       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_				:   Dict					    =							np.random.randint(0			,     len(__A ) )
             UpperCamelCase_				:   List[str]					    =							True

       if isinstance(input_mel[0]			,     __A ):
             UpperCamelCase_				:   int					    =							[np.asarray(__A			,     dtype=np.floataa ) for feature in input_mel]

       # is_longer is a list of bool
       UpperCamelCase_				:   Union[str, Any]					    =							[[longer] for longer in is_longer]

       UpperCamelCase_				:   List[str]					    =							{"""input_features""": input_mel, """is_longer""": is_longer}
       UpperCamelCase_				:   int					    =							BatchFeature(__A )

       if return_tensors is not None:
             UpperCamelCase_				:   Tuple					    =							input_features.convert_to_tensors(__A )

       return input_features














'''simple docstring'''

import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase							="path-to-your-trained-model"
UpperCamelCase							=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCamelCase							="A photo of sks dog in a bucket"
UpperCamelCase							=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")






