






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


UpperCamelCase__    :   int    		=       transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	Optional[int]:


	"""simple docstring"""



	if isinstance(lowerCamelCase__,	torch.Tensor	):
		return image
	elif isinstance(lowerCamelCase__,	PIL.Image.Image	):
		a												=      [image]

	a												=      [trans(img.convert('''RGB'''	)	) for img in image]
	a												=      torch.stack(lowerCamelCase__	)
	return image







class 					lowerCamelCase_  (						a_  ):
							def __init__(    self		:       Optional[Any]				,__lowerCamelCase		:       Optional[int]				,__lowerCamelCase		:       Union[str, Any]       ):





								'''simple docstring'''



								super().__init__()

								# make sure scheduler can always be converted to DDIM
								a												=      DDIMScheduler.from_config(scheduler.config       )

								self.register_modules(unet=__lowerCamelCase				,scheduler=__lowerCamelCase       )
							def 			SCREAMING_SNAKE_CASE_			(    self		:       Dict				,__lowerCamelCase		:       Optional[Any]       ):





								'''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 			SCREAMING_SNAKE_CASE_			(    self		:       Optional[int]				,__lowerCamelCase		:       str				,__lowerCamelCase		:       Tuple				,__lowerCamelCase		:       List[str]       ):





								'''simple docstring'''



								a												=      min(int(num_inference_steps * strength       )				,__lowerCamelCase       )

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

								return timesteps, num_inference_steps - t_start
							def 			SCREAMING_SNAKE_CASE_			(    self		:       Tuple				,__lowerCamelCase		:       Any				,__lowerCamelCase		:       Tuple				,__lowerCamelCase		:       Dict				,__lowerCamelCase		:       List[str]				,__lowerCamelCase		:       Tuple				,__lowerCamelCase		:       int=None       ):





								'''simple docstring'''



								if not isinstance(__lowerCamelCase				,(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(__lowerCamelCase       )}"""       )

								a												=      image.to(device=__lowerCamelCase				,dtype=__lowerCamelCase       )

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

								a												=      init_latents.shape
								a												=      randn_tensor(__lowerCamelCase				,generator=__lowerCamelCase				,device=__lowerCamelCase				,dtype=__lowerCamelCase       )

								# get latents
								print('''add noise to latents at timestep'''				,__lowerCamelCase       )
								a												=      self.scheduler.add_noise(__lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase       )
								a												=      init_latents

								return latents




							@torch.no_grad()
							def __call__(    self		:       Tuple				,__lowerCamelCase		:       Tuple = None				,__lowerCamelCase		:       Union[str, Any] = 0.8				,__lowerCamelCase		:       str = 1				,__lowerCamelCase		:       Union[str, Any] = None				,__lowerCamelCase		:       int = 0.0				,__lowerCamelCase		:       List[str] = 50				,__lowerCamelCase		:       List[str] = None				,__lowerCamelCase		:       List[str] = "pil"				,__lowerCamelCase		:       str = True				,):





								'''simple docstring'''



								self.check_inputs(__lowerCamelCase       )

								# 2. Preprocess image
								a												=      preprocess(__lowerCamelCase       )

								# 3. set timesteps
								self.scheduler.set_timesteps(__lowerCamelCase				,device=self.device       )
								a						,			a												=      self.get_timesteps(__lowerCamelCase				,__lowerCamelCase				,self.device       )
								a												=      timesteps[:1].repeat(__lowerCamelCase       )

								# 4. Prepare latent variables
								a												=      self.prepare_latents(__lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase				,self.unet.dtype				,self.device				,__lowerCamelCase       )
								a												=      latents

								# 5. Denoising loop
								for t in self.progress_bar(__lowerCamelCase       ):
									# 1. predict noise model_output
									a												=      self.unet(__lowerCamelCase				,__lowerCamelCase       ).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
									a												=      self.scheduler.step(
									    __lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase				,eta=__lowerCamelCase				,use_clipped_model_output=__lowerCamelCase				,generator=__lowerCamelCase				,).prev_sample

								a												=      (image / 2 + 0.5).clamp(0				,1       )
								a												=      image.cpu().permute(0				,2				,3				,1       ).numpy()
								if output_type == "pil":
									a												=      self.numpy_to_pil(__lowerCamelCase       )

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

								return ImagePipelineOutput(images=__lowerCamelCase       )









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   :    Any     =		logging.get_logger(__name__)







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[Any]			       =		["audio_values", "audio_mask"]







		def __init__( self		,      A=20_48		,      A=1		,      A=[16, 16]		,      A=1_28		,      A=4_41_00		,      A=86		,      A=20_48		,      A=0.0		,      **A		,      )  -> Dict:







					'''simple docstring'''







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

					lowerCamelCase    		=  spectrogram_length
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  feature_size // self.patch_size[1]
					lowerCamelCase    		=  n_fft
					lowerCamelCase    		=  sampling_rate // hop_length_to_sampling_rate
					lowerCamelCase    		=  sampling_rate
					lowerCamelCase    		=  padding_value
					lowerCamelCase    		=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2		,      num_mel_filters=A		,      min_frequency=0.0		,      max_frequency=22050.0		,      sampling_rate=A		,      norm="""slaney"""		,      mel_scale="""slaney"""		,      ).T







		def 					__A   ( self		,      A	)  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase    		=  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		,      )
					lowerCamelCase    		=  log_spec[:, :-1]
					lowerCamelCase    		=  log_spec - 20.0
					lowerCamelCase    		=  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		,      )  -> BatchFeature:







					'''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."""	)

					lowerCamelCase    		=  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    		=  is_batched_numpy or (
					    isinstance(A		,      (list, tuple)	) and (isinstance(raw_speech[0]		,      (np.ndarray, tuple, list)	))
					)
					if is_batched:
								lowerCamelCase    		=  [np.asarray([speech]		,      dtype=np.floataa	).T for speech in raw_speech]
					elif not is_batched and not isinstance(A		,      np.ndarray	):
								lowerCamelCase    		=  np.asarray(A		,      dtype=np.floataa	)
					elif isinstance(A		,      np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
								lowerCamelCase    		=  raw_speech.astype(np.floataa	)
					# always return batch
					if not is_batched:
								lowerCamelCase    		=  [np.asarray([raw_speech]	).T]

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

					# Create audio attention mask
					lowerCamelCase    		=  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:
								lowerCamelCase    		=  [
								    (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
								]
								lowerCamelCase    		=  np.array(A	).astype(np.floataa	)

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

					# return as BatchFeature
					if return_attention_mask:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
					else:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features}

					lowerCamelCase    		=  BatchFeature(data=A		,      tensor_type=A	)
					return encoded_inputs

