





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCamelCase      							=   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def  __UpperCAmelCase(   lowercase_   ):
       if isinstance(lowercase_			,     torch.Tensor   ):
              return image
       elif isinstance(lowercase_			,     PIL.Image.Image   ):
              _lowerCamelCase				:  List[str]						=		[image]

       _lowerCamelCase				:  Optional[int]						=		[trans(img.convert('''RGB'''   )   ) for img in image]
       _lowerCamelCase				:  Dict						=		torch.stack(lowercase_   )
       return image







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()

             # make sure scheduler can always be converted to DDIM
             _lowerCamelCase				:  Optional[Any]						=		DDIMScheduler.from_config(scheduler.config)

             self.register_modules(unet=a__   ,				scheduler=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if strength < 0 or strength > 1:
                    raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}""")


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		min(int(num_inference_steps * strength)   ,				a__)

             _lowerCamelCase				:  Tuple						=		max(num_inference_steps - init_timestep   ,				0)
             _lowerCamelCase				:  int						=		self.scheduler.timesteps[t_start:]

             return timesteps, num_inference_steps - t_start


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__=None):



             """simple docstring"""





             if not isinstance(a__   ,				(torch.Tensor, PIL.Image.Image, list)):
                    raise ValueError(
                        F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a__)}""")

             _lowerCamelCase				:  Any						=		image.to(device=a__   ,				dtype=a__)

             if isinstance(a__   ,				a__) and len(a__) != batch_size:
                    raise ValueError(
                        F"""You have passed a list of generators of length {len(a__)}, but requested an effective batch"""
                        F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

             _lowerCamelCase				:  Any						=		init_latents.shape
             _lowerCamelCase				:  Tuple						=		randn_tensor(a__   ,				generator=a__   ,				device=a__   ,				dtype=a__)

             # get latents
             print('''add noise to latents at timestep'''   ,				a__)
             _lowerCamelCase				:  List[Any]						=		self.scheduler.add_noise(a__   ,				a__   ,				a__)
             _lowerCamelCase				:  List[Any]						=		init_latents

             return latents



      @torch.no_grad()
      def __call__(		self   ,				a__ = None   ,				a__ = 0.8   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = None   ,				a__ = "pil"   ,				a__ = True   ,				):



             """simple docstring"""





             self.check_inputs(a__)

             # 2. Preprocess image
             _lowerCamelCase				:  Union[str, Any]						=		preprocess(a__)

             # 3. set timesteps
             self.scheduler.set_timesteps(a__   ,				device=self.device)
             _lowerCamelCase,	_lowerCamelCase				:  Any						=		self.get_timesteps(a__   ,				a__   ,				self.device)
             _lowerCamelCase				:  Union[str, Any]						=		timesteps[:1].repeat(a__)

             # 4. Prepare latent variables
             _lowerCamelCase				:  int						=		self.prepare_latents(a__   ,				a__   ,				a__   ,				self.unet.dtype   ,				self.device   ,				a__)
             _lowerCamelCase				:  int						=		latents

             # 5. Denoising loop
             for t in self.progress_bar(a__):
                    # 1. predict noise model_output
                    _lowerCamelCase				:  Dict						=		self.unet(a__   ,				a__).sample

                    # 2. predict previous mean of image x_t-1 and add variance depending on eta
                    # eta corresponds to η in paper and should be between [0, 1]
                    # do x_t -> x_t-1
                    _lowerCamelCase				:  Tuple						=		self.scheduler.step(
                        a__   ,				a__   ,				a__   ,				eta=a__   ,				use_clipped_model_output=a__   ,				generator=a__   ,				).prev_sample

             _lowerCamelCase				:  str						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  Dict						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  Any						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image, latent_timestep.item())

             return ImagePipelineOutput(images=a__)








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

import numpy as np

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      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   ["""input_features"""]


      def __init__(		self   ,				a__=80   ,				a__=1_6000   ,				a__=160   ,				a__=30   ,				a__=400   ,				a__=0.0   ,				a__=False   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(
                 feature_size=a__   ,				sampling_rate=a__   ,				padding_value=a__   ,				return_attention_mask=a__   ,				**a__   ,				)
             _lowerCamelCase				:  int						=		n_fft
             _lowerCamelCase				:  List[Any]						=		hop_length
             _lowerCamelCase				:  Tuple						=		chunk_length
             _lowerCamelCase				:  Optional[Any]						=		chunk_length * sampling_rate
             _lowerCamelCase				:  Optional[Any]						=		self.n_samples // hop_length
             _lowerCamelCase				:  List[str]						=		sampling_rate
             _lowerCamelCase				:  Optional[int]						=		mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2   ,				num_mel_filters=a__   ,				min_frequency=0.0   ,				max_frequency=8000.0   ,				sampling_rate=a__   ,				norm='''slaney'''   ,				mel_scale='''slaney'''   ,				)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		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   ,				log_mel='''log10'''   ,				)
             _lowerCamelCase				:  str						=		log_spec[:, :-1]
             _lowerCamelCase				:  Union[str, Any]						=		np.maximum(a__   ,				log_spec.max() - 8.0)
             _lowerCamelCase				:  Optional[int]						=		(log_spec + 4.0) / 4.0
             return log_spec


      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def 					__snake_case      (		a__   ,				a__   ,				a__ = 0.0):



             """simple docstring"""





             if attention_mask is not None:
                    _lowerCamelCase				:  Dict						=		np.array(a__   ,				np.intaa)
                    _lowerCamelCase				:  Any						=		[]

                    for vector, length in zip(a__   ,				attention_mask.sum(-1)):
                           _lowerCamelCase				:  Any						=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
                           if length < normed_slice.shape[0]:
                                  _lowerCamelCase				:  Optional[int]						=		padding_value

                           normed_input_values.append(a__)
             else:
                    _lowerCamelCase				:  Optional[int]						=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

             return normed_input_values


      def __call__(		self   ,				a__   ,				a__ = True   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				a__ = "max_length"   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             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.''')

             _lowerCamelCase				:  Dict						=		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}""")
             _lowerCamelCase				:  int						=		is_batched_numpy or (
                 isinstance(a__   ,				(list, tuple)) and (isinstance(raw_speech[0]   ,				(np.ndarray, tuple, list)))
             )

             if is_batched:
                    _lowerCamelCase				:  List[Any]						=		[np.asarray([speech]   ,				dtype=np.floataa).T for speech in raw_speech]
             elif not is_batched and not isinstance(a__   ,				np.ndarray):
                    _lowerCamelCase				:  str						=		np.asarray(a__   ,				dtype=np.floataa)
             elif isinstance(a__   ,				np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
                    _lowerCamelCase				:  str						=		raw_speech.astype(np.floataa)

             # always return batch
             if not is_batched:
                    _lowerCamelCase				:  List[Any]						=		[np.asarray([raw_speech]).T]

             _lowerCamelCase				:  List[str]						=		BatchFeature({'''input_features''': raw_speech})

             # convert into correct format for padding

             _lowerCamelCase				:  Optional[Any]						=		self.pad(
                 a__   ,				padding=a__   ,				max_length=max_length if max_length else self.n_samples   ,				truncation=a__   ,				pad_to_multiple_of=a__   ,				return_attention_mask=return_attention_mask or do_normalize   ,				)

             # zero-mean and unit-variance normalization
             if do_normalize:
                    _lowerCamelCase				:  Dict						=		self.zero_mean_unit_var_norm(
                        padded_inputs['''input_features''']   ,				attention_mask=padded_inputs['''attention_mask''']   ,				padding_value=self.padding_value   ,				)
                    _lowerCamelCase				:  int						=		np.stack(padded_inputs['''input_features''']   ,				axis=0)

             # make sure list is in array format
             _lowerCamelCase				:  Optional[Any]						=		padded_inputs.get('''input_features''').transpose(2   ,				0   ,				1)

             _lowerCamelCase				:  Tuple						=		[self._np_extract_fbank_features(a__) for waveform in input_features[0]]

             if isinstance(input_features[0]   ,				a__):
                    _lowerCamelCase				:  str						=		[np.asarray(a__   ,				dtype=np.floataa) for feature in input_features]
             else:
                    _lowerCamelCase				:  Dict						=		input_features

             if return_attention_mask:
                    # rescale from sample (48000) to feature (3000)
                    _lowerCamelCase				:  Optional[int]						=		padded_inputs['''attention_mask'''][:, :: self.hop_length]

             if return_tensors is not None:
                    _lowerCamelCase				:  str						=		padded_inputs.convert_to_tensors(a__)

             return padded_inputs



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		copy.deepcopy(self.__dict__)
             _lowerCamelCase				:  int						=		self.__class__.__name__
             if "mel_filters" in output:
                    del output["mel_filters"]
             return output

