




'''simple docstring'''



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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




def   lowercase__(			__UpperCamelCase:					Dict    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						image.size
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						image.resize((w, h)      ,resample=PIL_INTERPOLATION['lanczos']    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.array(__UpperCamelCase    ).astype(np.floataa    ) / 2_5_5.0
      SCREAMING_SNAKE_CASE   :      int    					=						image[None].transpose(0      ,3      ,1      ,2    )
      SCREAMING_SNAKE_CASE   :      int    					=						torch.from_numpy(__UpperCamelCase    )
      return 2.0 * image - 1.0






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A,							A,							A,							):







             '''simple docstring'''







             super().__init__()
             self.register_modules(vqvae=A,							unet=A,							scheduler=A  )







       @torch.no_grad()
       def __call__(      self,							A = None,							A = 1,							A = 100,							A = 0.0,							A = None,							A = "pil",							A = True,							):







             '''simple docstring'''







             if isinstance(A,							PIL.Image.Image  ):
                   SCREAMING_SNAKE_CASE   :      int    					=						1
             elif isinstance(A,							torch.Tensor  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image.shape[0]
             else:
                   raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A  )}"  )

             if isinstance(A,							PIL.Image.Image  ):
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						preprocess(A  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Tuple    					=						image.shape[-2:]

             # in_channels should be 6: 3 for latents, 3 for low resolution image
             SCREAMING_SNAKE_CASE   :      Tuple    					=						(batch_size, self.unet.config.in_channels // 2, height, width)
             SCREAMING_SNAKE_CASE   :      Any    					=						next(self.unet.parameters()  ).dtype

             SCREAMING_SNAKE_CASE   :      int    					=						randn_tensor(A,							generator=A,							device=self.device,							dtype=A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image.to(device=self.device,							dtype=A  )

             # set timesteps and move to the correct device
             self.scheduler.set_timesteps(A,							device=self.device  )
             SCREAMING_SNAKE_CASE   :      str    					=						self.scheduler.timesteps

             # scale the initial noise by the standard deviation required by the scheduler
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						latents * self.scheduler.init_noise_sigma

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
             # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
             # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
             # and should be between [0, 1]
             SCREAMING_SNAKE_CASE   :      str    					=						'eta' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						{}
             if accepts_eta:
                   SCREAMING_SNAKE_CASE   :      Any    					=						eta

             for t in self.progress_bar(A  ):
                   # concat latents and low resolution image in the channel dimension.
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						torch.cat([latents, image],							dim=1  )
                   SCREAMING_SNAKE_CASE   :      int    					=						self.scheduler.scale_model_input(A,							A  )
                   # predict the noise residual
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						self.unet(A,							A  ).sample
                   # compute the previous noisy sample x_t -> x_t-1
                   SCREAMING_SNAKE_CASE   :      str    					=						self.scheduler.step(A,							A,							A,							**A  ).prev_sample

             # decode the image latents with the VQVAE
             SCREAMING_SNAKE_CASE   :      Dict    					=						self.vqvae.decode(A  ).sample
             SCREAMING_SNAKE_CASE   :      Dict    					=						torch.clamp(A,							-1.0,							1.0  )
             SCREAMING_SNAKE_CASE   :      str    					=						image / 2 + 0.5
             SCREAMING_SNAKE_CASE   :      List[str]    					=						image.cpu().permute(0,							2,							3,							1  ).numpy()

             if output_type == "pil":
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.numpy_to_pil(A  )

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=A  )








'''simple docstring'''



from math import ceil
from typing import List, Optional, Union

import numpy as np

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


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				str        =						['''audio_values''', '''audio_mask''']







       def __init__(      self,							A=2_048,							A=1,							A=[16, 16],							A=128,							A=44_100,							A=86,							A=2_048,							A=0.0,							**A,							):







             '''simple docstring'''







             super().__init__(
                 feature_size=A,							sampling_rate=A,							padding_value=A,							**A,							)

             SCREAMING_SNAKE_CASE   :      str    					=						spectrogram_length
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						num_channels
             SCREAMING_SNAKE_CASE   :      List[str]    					=						patch_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feature_size // self.patch_size[1]
             SCREAMING_SNAKE_CASE   :      Dict    					=						n_fft
             SCREAMING_SNAKE_CASE   :      Tuple    					=						sampling_rate // hop_length_to_sampling_rate
             SCREAMING_SNAKE_CASE   :      str    					=						sampling_rate
             SCREAMING_SNAKE_CASE   :      int    					=						padding_value
             SCREAMING_SNAKE_CASE   :      Any    					=						mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2,							num_mel_filters=A,							min_frequency=0.0,							max_frequency=2_20_50.0,							sampling_rate=A,							norm='slaney',							mel_scale='slaney',							).T







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						spectrogram(
                 A,							window_function(self.n_fft,							'hann'  ),							frame_length=self.n_fft,							hop_length=self.hop_length,							power=2.0,							mel_filters=self.mel_filters.T,							log_mel='dB',							db_range=80.0,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						log_spec[:, :-1]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						log_spec - 20.0
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.clip(log_spec / 40.0,							-2.0,							0.0  ) + 1.0
             return log_spec







       def __call__(      self,							A,							A = None,							A = True,							A = None,							A = False,							A = False,							**A,							):







             '''simple docstring'''







             if sampling_rate is not None:
                   if sampling_rate != self.sampling_rate:
                         raise ValueError(
                             'This feature extractor is set to support sampling rate'
                             F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                             F" 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   :      List[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}"  )
             SCREAMING_SNAKE_CASE   :      int    					=						is_batched_numpy or (
                 isinstance(A,							(list, tuple)  ) and (isinstance(raw_speech[0],							(np.ndarray, tuple, list)  ))
             )
             if is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([speech],							dtype=np.floataa  ).T for speech in raw_speech]
             elif not is_batched and not isinstance(A,							np.ndarray  ):
                   SCREAMING_SNAKE_CASE   :      Any    					=						np.asarray(A,							dtype=np.floataa  )
             elif isinstance(A,							np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						raw_speech.astype(np.floataa  )
             # always return batch
             if not is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([raw_speech]  ).T]

             # Convert audio signals to log mel spectrograms, truncate by time axis
             SCREAMING_SNAKE_CASE   :      int    					=						[
                 self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
             ]
             if isinstance(audio_features[0],							A  ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray(A,							dtype=np.floataa  ) for feature in audio_features]

             # Create audio attention mask
             SCREAMING_SNAKE_CASE   :      Tuple    					=						max(
                 [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
                       (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                       + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                       for feature in audio_features
                   ]
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(A  ).astype(np.floataa  )

             # convert into correct format for padding
             SCREAMING_SNAKE_CASE   :      Tuple    					=						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.ones([len(A  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						padded_audio_features * self.padding_value
             for i in range(len(A  )  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						audio_features[i]
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						feature

             # return as BatchFeature
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      Any    					=						{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						{'audio_values': padded_audio_features}

             SCREAMING_SNAKE_CASE   :      str    					=						BatchFeature(data=A,							tensor_type=A  )
             return encoded_inputs


