


'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 	__lowerCAmelCase		(							lowercase_    ):


  '''simple docstring'''







  def __init__(							self :       str	,_a :       str	,_a :       Optional[int]	,_a :       str	,_a :       Tuple = None	,):

        '''simple docstring'''




        super().__init__()
        self.register_modules(transformer=_a	,vae=_a	,scheduler=_a )

        # create a imagenet -> id dictionary for easier use
        A_						:  List[Any]          =				{}
        if idalabel is not None:
              for key, value in idalabel.items():
                    for label in value.split(""",""" ):
                          A_						:  Tuple          =				int(_a )
              A_						:  Dict          =				dict(sorted(self.labels.items() ) )






  def        _a							(							self :       Any	,_a :       Optional[Any] ):

        '''simple docstring'''




        if not isinstance(_a	,_a ):
              A_						:  Union[str, Any]          =				list(_a )

        for l in label:
              if l not in self.labels:
                    raise ValueError(
                        f'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.' )

        return [self.labels[l] for l in label]






  @torch.no_grad()
  def __call__(							self :       Optional[Any]	,_a :       Optional[Any]	,_a :       Optional[int] = 4.0	,_a :       Optional[int] = None	,_a :       int = 50	,_a :       List[str] = "pil"	,_a :       Optional[Any] = True	,):

        '''simple docstring'''




        A_						:  List[Any]          =				len(_a )
        A_						:  Any          =				self.transformer.config.sample_size
        A_						:  Union[str, Any]          =				self.transformer.config.in_channels

        A_						:  int          =				randn_tensor(
            shape=(batch_size, latent_channels, latent_size, latent_size)	,generator=_a	,device=self.device	,dtype=self.transformer.dtype	,)
        A_						:  Dict          =				torch.cat([latents] * 2 ) if guidance_scale > 1 else latents

        A_						:  Tuple          =				torch.tensor(_a	,device=self.device ).reshape(-1 )
        A_						:  List[str]          =				torch.tensor([1000] * batch_size	,device=self.device )
        A_						:  Union[str, Any]          =				torch.cat([class_labels, class_null]	,0 ) if guidance_scale > 1 else class_labels

        # set step values
        self.scheduler.set_timesteps(_a )

        for t in self.progress_bar(self.scheduler.timesteps ):
              if guidance_scale > 1:
                    A_						:  Optional[int]          =				latent_model_input[: len(_a ) // 2]
                    A_						:  str          =				torch.cat([half, half]	,dim=0 )
              A_						:  Union[str, Any]          =				self.scheduler.scale_model_input(_a	,_a )

              A_						:  List[str]          =				t
              if not torch.is_tensor(_a ):
                    # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                    # This would be a good case for the `match` statement (Python 3.10+)
                    A_						:  Union[str, Any]          =				latent_model_input.device.type == """mps"""
                    if isinstance(_a	,_a ):
                          A_						:  Dict          =				torch.floataa if is_mps else torch.floataa
                    else:
                          A_						:  List[str]          =				torch.intaa if is_mps else torch.intaa
                    A_						:  Optional[Any]          =				torch.tensor([timesteps]	,dtype=_a	,device=latent_model_input.device )
              elif len(timesteps.shape ) == 0:
                    A_						:  Tuple          =				timesteps[None].to(latent_model_input.device )
              # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
              A_						:  str          =				timesteps.expand(latent_model_input.shape[0] )
              # predict noise model_output
              A_						:  str          =				self.transformer(
                  _a	,timestep=_a	,class_labels=_a ).sample

              # perform guidance
              if guidance_scale > 1:
                    A_   ,					A_						:  Tuple          =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                    A_   ,					A_						:  Union[str, Any]          =				torch.split(_a	,len(_a ) // 2	,dim=0 )

                    A_						:  Union[str, Any]          =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                    A_						:  Optional[int]          =				torch.cat([half_eps, half_eps]	,dim=0 )

                    A_						:  int          =				torch.cat([eps, rest]	,dim=1 )

              # learned sigma
              if self.transformer.config.out_channels // 2 == latent_channels:
                    A_   ,					A_						:  int          =				torch.split(_a	,_a	,dim=1 )
              else:
                    A_						:  Union[str, Any]          =				noise_pred

              # compute previous image: x_t -> x_t-1
              A_						:  Dict          =				self.scheduler.step(_a	,_a	,_a ).prev_sample

        if guidance_scale > 1:
              A_   ,					A_						:  Optional[int]          =				latent_model_input.chunk(2	,dim=0 )
        else:
              A_						:  Optional[Any]          =				latent_model_input

        A_						:  List[str]          =				1 / self.vae.config.scaling_factor * latents
        A_						:  int          =				self.vae.decode(_a ).sample

        A_						:  Union[str, Any]          =				(samples / 2 + 0.5).clamp(0	,1 )

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        A_						:  str          =				samples.cpu().permute(0	,2	,3	,1 ).float().numpy()

        if output_type == "pil":
              A_						:  List[str]          =				self.numpy_to_pil(_a )

        if not return_dict:
              return (samples,)

        return ImagePipelineOutput(images=_a )







'''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


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""input_features""", """is_longer"""]






  def __init__(							self :       Dict	,_a :       Optional[int]=64	,_a :       List[Any]=48000	,_a :       str=480	,_a :       Optional[Any]=10	,_a :       Optional[int]=1024	,_a :       Tuple=0.0	,_a :       str=False	,_a :       float = 0	,_a :       float = 14000	,_a :       int = None	,_a :       str = "fusion"	,_a :       str = "repeatpad"	,**_a :       Tuple	,):

        '''simple docstring'''




        super().__init__(
            feature_size=_a	,sampling_rate=_a	,padding_value=_a	,return_attention_mask=_a	,**_a	,)
        A_						:  Tuple          =				top_db
        A_						:  Tuple          =				truncation
        A_						:  Optional[Any]          =				padding
        A_						:  Optional[int]          =				fft_window_size
        A_						:  Dict          =				(fft_window_size >> 1) + 1
        A_						:  Any          =				hop_length
        A_						:  List[Any]          =				max_length_s
        A_						:  Tuple          =				max_length_s * sampling_rate
        A_						:  Tuple          =				sampling_rate
        A_						:  Optional[int]          =				frequency_min
        A_						:  Tuple          =				frequency_max
        A_						:  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=_a	,mel_scale="""htk"""	,)
        A_						:  Dict          =				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        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  int          =				copy.deepcopy(self.__dict__ )
        A_						:  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        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[np.array] = None ):

        '''simple docstring'''




        A_						:  List[str]          =				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        _a							(							self :       Optional[int]	,_a :       Dict	,_a :       Optional[Any]	,_a :       Optional[int] ):

        '''simple docstring'''




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

        A_						:  Tuple          =				mel[idx_front : idx_front + chunk_frames, :]
        A_						:  Dict          =				mel[idx_middle : idx_middle + chunk_frames, :]
        A_						:  Dict          =				mel[idx_back : idx_back + chunk_frames, :]

        A_						:  Optional[int]          =				torch.tensor(mel[None, None, :] )
        A_						:  Dict          =				torch.nn.functional.interpolate(
            _a	,size=[chunk_frames, 64]	,mode="""bilinear"""	,align_corners=_a )
        A_						:  str          =				mel_shrink[0][0].numpy()
        A_						:  Tuple          =				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,axis=0 )
        return mel_fusion






  def        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[Any]	,_a :       int	,_a :       Dict ):

        '''simple docstring'''




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

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

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

        return input_mel, longer






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

        '''simple docstring'''




        A_						:  List[str]          =				truncation if truncation is not None else self.truncation
        A_						:  List[Any]          =				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_						:  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}' )
        A_						:  int          =				is_batched_numpy or (
            isinstance(_a	,(list, tuple) ) and (isinstance(raw_speech[0]	,(np.ndarray, tuple, list) ))
        )

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

        # always return batch
        if not is_batched:
              A_						:  Any          =				[np.asarray(_a )]

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

        A_						:  int          =				[]
        A_						:  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
              A_						:  List[Any]          =				np.random.randint(0	,len(_a ) )
              A_						:  List[str]          =				True

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

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

        A_						:  Optional[Any]          =				{"""input_features""": input_mel, """is_longer""": is_longer}
        A_						:  int          =				BatchFeature(_a )

        if return_tensors is not None:
              A_						:  int          =				input_features.convert_to_tensors(_a )

        return input_features
