





'''simple docstring'''

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				:      Optional[Any]						      =logging.get_logger(__name__)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""input_features"""]
 def __init__(							self :List[str]		,			lowercase_ :Tuple=80		,			lowercase_ :int=1_60_00		,			lowercase_ :Union[str, Any]=1_60		,			lowercase_ :List[Any]=30		,			lowercase_ :str=4_00		,			lowercase_ :Optional[int]=0.0		,			lowercase_ :Dict=False		,			**lowercase_ :Optional[Any]		,			)->				str:
     super().__init__(
         feature_size=lowercase_		,			sampling_rate=lowercase_		,			padding_value=lowercase_		,			return_attention_mask=lowercase_		,			**lowercase_		,			)
     A__							=     n_fft
     A__							=     hop_length
     A__							=     chunk_length
     A__							=     chunk_length * sampling_rate
     A__							=     self.n_samples // hop_length
     A__							=     sampling_rate
     A__							=     mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2		,			num_mel_filters=lowercase_		,			min_frequency=0.0		,			max_frequency=8_0_0_0.0		,			sampling_rate=lowercase_		,			norm="slaney"		,			mel_scale="slaney"		,			)
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :np.array				)->				np.ndarray:
     A__							=     spectrogram(
         lowercase_		,			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"		,			)
     A__							=     log_spec[:, :-1]
     A__							=     np.maximum(lowercase_		,			log_spec.max() - 8.0				)
     A__							=     (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 	UpperCAmelCase_				(							lowercase_ :List[np.ndarray]		,			lowercase_ :List[np.ndarray]		,			lowercase_ :float = 0.0				)->				List[np.ndarray]:
     if attention_mask is not None:
         A__							=     np.array(lowercase_		,			np.intaa				)
         A__							=     []

         for vector, length in zip(lowercase_		,			attention_mask.sum(-1				)				):
             A__							=     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7				)
             if length < normed_slice.shape[0]:
                 A__							=     padding_value

             normed_input_values.append(lowercase_				)
     else:
         A__							=     [(x - x.mean()) / np.sqrt(x.var() + 1E-7				) for x in input_values]

     return normed_input_values
 def __call__(							self :Tuple		,			lowercase_ :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,			lowercase_ :bool = True		,			lowercase_ :Optional[int] = None		,			lowercase_ :Optional[Union[str, TensorType]] = None		,			lowercase_ :Optional[bool] = None		,			lowercase_ :Optional[str] = "max_length"		,			lowercase_ :Optional[int] = None		,			lowercase_ :Optional[int] = None		,			lowercase_ :Optional[bool] = None		,			**lowercase_ :int		,			)->				BatchFeature:

     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__							=     isinstance(lowercase_		,			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__							=     is_batched_numpy or (
         isinstance(lowercase_		,			(list, tuple)				) and (isinstance(raw_speech[0]		,			(np.ndarray, tuple, list)				))
     )

     if is_batched:
         A__							=     [np.asarray([speech]		,			dtype=np.floataa				).T for speech in raw_speech]
     elif not is_batched and not isinstance(lowercase_		,			np.ndarray				):
         A__							=     np.asarray(lowercase_		,			dtype=np.floataa				)
     elif isinstance(lowercase_		,			np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
         A__							=     raw_speech.astype(np.floataa				)

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

     A__							=     BatchFeature({"input_features": raw_speech}				)

     # convert into correct format for padding

     A__							=     self.pad(
         lowercase_		,			padding=lowercase_		,			max_length=max_length if max_length else self.n_samples		,			truncation=lowercase_		,			pad_to_multiple_of=lowercase_		,			return_attention_mask=return_attention_mask or do_normalize		,			)

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

     # make sure list is in array format
     A__							=     padded_inputs.get("input_features"				).transpose(2		,			0		,			1				)

     A__							=     [self._np_extract_fbank_features(lowercase_				) for waveform in input_features[0]]

     if isinstance(input_features[0]		,			lowercase_				):
         A__							=     [np.asarray(lowercase_		,			dtype=np.floataa				) for feature in input_features]
     else:
         A__							=     input_features

     if return_attention_mask:
         # rescale from sample (48000) to feature (3000)
         A__							=     padded_inputs["attention_mask"][:, :: self.hop_length]

     if return_tensors is not None:
         A__							=     padded_inputs.convert_to_tensors(lowercase_				)

     return padded_inputs



 def 	UpperCAmelCase_				(							self :Dict				)->				Dict[str, Any]:
     A__							=     copy.deepcopy(self.__dict__				)
     A__							=     self.__class__.__name__
     if "mel_filters" in output:
         del output["mel_filters"]
     return output










'''simple docstring'''

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""vqvae"""]
 def __init__(							self :Dict		,			lowercase_ :AutoencoderKL		,			lowercase_ :UNetaDConditionModel		,			lowercase_ :Mel		,			lowercase_ :Union[DDIMScheduler, DDPMScheduler]		,			)->				Optional[Any]:
     super().__init__()
     self.register_modules(unet=lowercase_		,			scheduler=lowercase_		,			mel=lowercase_		,			vqvae=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				int:
     return 50 if isinstance(self.scheduler		,			lowercase_				) else 10_00
 @torch.no_grad()
 def __call__(							self :Optional[Any]		,			lowercase_ :int = 1		,			lowercase_ :str = None		,			lowercase_ :np.ndarray = None		,			lowercase_ :int = 0		,			lowercase_ :int = 0		,			lowercase_ :int = None		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :float = 0		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :torch.Tensor = None		,			lowercase_ :torch.Tensor = None		,			lowercase_ :Tuple=True		,			)->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
     A__							=     steps or self.get_default_steps()
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size				) == int:
         A__							=     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
         A__							=     randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )		,			generator=lowercase_		,			device=self.device		,			)
     A__							=     noise
     A__							=     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(lowercase_		,			lowercase_				)
         A__							=     self.mel.audio_slice_to_image(lowercase_				)
         A__							=     np.frombuffer(input_image.tobytes()		,			dtype="uint8"				).reshape(
             (input_image.height, input_image.width)				)
         A__							=     (input_image / 2_55) * 2 - 1
         A__							=     torch.tensor(input_image[np.newaxis, :, :]		,			dtype=torch.float				).to(self.device				)

         if self.vqvae is not None:
             A__							=     self.vqvae.encode(torch.unsqueeze(lowercase_		,			0				)				).latent_dist.sample(
                 generator=lowercase_				)[0]
             A__							=     self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
             A__							=     self.scheduler.add_noise(lowercase_		,			lowercase_		,			self.scheduler.timesteps[start_step - 1]				)

         A__							=     (
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         A__							=     int(mask_start_secs * pixels_per_second				)
         A__							=     int(mask_end_secs * pixels_per_second				)
         A__							=     self.scheduler.add_noise(lowercase_		,			lowercase_		,			torch.tensor(self.scheduler.timesteps[start_step:]				)				)

     for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
         if isinstance(self.unet		,			lowercase_				):
             A__							=     self.unet(lowercase_		,			lowercase_		,			lowercase_				)["sample"]
         else:
             A__							=     self.unet(lowercase_		,			lowercase_				)["sample"]

         if isinstance(self.scheduler		,			lowercase_				):
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			)["prev_sample"]
         else:
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			generator=lowercase_		,			)["prev_sample"]

         if mask is not None:
             if mask_start > 0:
                 A__							=     mask[:, step, :, :mask_start]
             if mask_end > 0:
                 A__							=     mask[:, step, :, -mask_end:]

     if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         A__							=     1 / self.vqvae.config.scaling_factor * images
         A__							=     self.vqvae.decode(lowercase_				)["sample"]

     A__							=     (images / 2 + 0.5).clamp(0		,			1				)
     A__							=     images.cpu().permute(0		,			2		,			3		,			1				).numpy()
     A__							=     (images * 2_55).round().astype("uint8"				)
     A__							=     list(
         (Image.fromarray(_[:, :, 0]				) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(lowercase_		,			mode="RGB"				).convert("L"				) for _ in images)				)

     A__							=     [self.mel.image_to_audio(lowercase_				) for _ in images]
     if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

     return BaseOutput(**AudioPipelineOutput(np.array(lowercase_				)[:, np.newaxis, :]				)		,			**ImagePipelineOutput(lowercase_				)				)
 @torch.no_grad()
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[Image.Image]		,			lowercase_ :int = 50				)->				np.ndarray:
     assert isinstance(self.scheduler		,			lowercase_				)
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     np.array(
         [np.frombuffer(image.tobytes()		,			dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
     A__							=     (sample / 2_55) * 2 - 1
     A__							=     torch.Tensor(lowercase_				).to(self.device				)

     for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,			(0,)				)				):
         A__							=     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         A__							=     self.scheduler.alphas_cumprod[t]
         A__							=     (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         A__							=     1 - alpha_prod_t
         A__							=     self.unet(lowercase_		,			lowercase_				)["sample"]
         A__							=     (1 - alpha_prod_t_prev) ** 0.5 * model_output
         A__							=     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         A__							=     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

     return sample



 @staticmethod
 def 	UpperCAmelCase_				(							lowercase_ :torch.Tensor		,			lowercase_ :torch.Tensor		,			lowercase_ :float				)->				torch.Tensor:
     A__							=     acos(torch.dot(torch.flatten(lowercase_				)		,			torch.flatten(lowercase_				)				) / torch.norm(lowercase_				) / torch.norm(lowercase_				)				)
     return sin((1 - alpha) * theta				) * xa / sin(lowercase_				) + sin(alpha * theta				) * xa / sin(lowercase_				)



