
'''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       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						["vqvae"]

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  )					->       int:
														super().__init__()
														self.register_modules(unet=UpperCAmelCase  ,  scheduler=UpperCAmelCase  ,  mel=UpperCAmelCase  ,  vqvae=UpperCAmelCase  )

							def 	lowercase							(self  )					->       int:
														return 50 if isinstance(self.scheduler  ,  UpperCAmelCase  ) else 1000

							@torch.no_grad()
							def __call__(self  ,  UpperCAmelCase = 1  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 0  ,  UpperCAmelCase = 0  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 0  ,  UpperCAmelCase = 0  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 0  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase=True  ,  )					->       Union[
							    Union[AudioPipelineOutput, ImagePipelineOutput],
							    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
							]:
														_snake_case       =  steps or self.get_default_steps()
														self.scheduler.set_timesteps(UpperCAmelCase  )
														_snake_case       =  step_generator or generator
														# For backwards compatibility
														if type(self.unet.config.sample_size  ) == int:
																					_snake_case       =  (self.unet.config.sample_size, self.unet.config.sample_size)
														if noise is None:
																					_snake_case       =  randn_tensor(
																					    (
																					        batch_size,
																					        self.unet.config.in_channels,
																					        self.unet.config.sample_size[0],
																					        self.unet.config.sample_size[1],
																					    )  ,  generator=UpperCAmelCase  ,  device=self.device  ,  )
														_snake_case       =  noise
														_snake_case       =  None

														if audio_file is not None or raw_audio is not None:
																					self.mel.load_audio(UpperCAmelCase  ,  UpperCAmelCase  )
																					_snake_case       =  self.mel.audio_slice_to_image(UpperCAmelCase  )
																					_snake_case       =  np.frombuffer(input_image.tobytes()  ,  dtype="""uint8"""  ).reshape(
																					    (input_image.height, input_image.width)  )
																					_snake_case       =  (input_image / 255) * 2 - 1
																					_snake_case       =  torch.tensor(input_image[np.newaxis, :, :]  ,  dtype=torch.float  ).to(self.device  )

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

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

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

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

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

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

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

														_snake_case       =  (images / 2 + 0.5).clamp(0  ,  1  )
														_snake_case       =  images.cpu().permute(0  ,  2  ,  3  ,  1  ).numpy()
														_snake_case       =  (images * 255).round().astype("""uint8"""  )
														_snake_case       =  list(
														    (Image.fromarray(_[:, :, 0]  ) for _ in images)
														    if images.shape[3] == 1
														    else (Image.fromarray(UpperCAmelCase  ,  mode="""RGB"""  ).convert("""L"""  ) for _ in images)  )

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

														return BaseOutput(**AudioPipelineOutput(np.array(UpperCAmelCase  )[:, np.newaxis, :]  )  ,  **ImagePipelineOutput(UpperCAmelCase  )  )

							@torch.no_grad()
							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = 50  )					->       np.ndarray:
														assert isinstance(self.scheduler  ,  UpperCAmelCase  )
														self.scheduler.set_timesteps(UpperCAmelCase  )
														_snake_case       =  np.array(
														    [np.frombuffer(image.tobytes()  ,  dtype="""uint8"""  ).reshape((1, image.height, image.width)  ) for image in images]  )
														_snake_case       =  (sample / 255) * 2 - 1
														_snake_case       =  torch.Tensor(UpperCAmelCase  ).to(self.device  )

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

														return sample





							@staticmethod
							def 	lowercase							(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       torch.Tensor:
														_snake_case       =  acos(torch.dot(torch.flatten(UpperCAmelCase  )  ,  torch.flatten(UpperCAmelCase  )  ) / torch.norm(UpperCAmelCase  ) / torch.norm(UpperCAmelCase  )  )
														return sin((1 - alpha) * theta  ) * xa / sin(UpperCAmelCase  ) + sin(alpha * theta  ) * xa / sin(UpperCAmelCase  )

'''simple docstring'''
import logging

from transformers.configuration_utils import PretrainedConfig


__lowerCAmelCase												=	logging.getLogger(__name__)


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"masked_bert"

							def __init__(self  ,  UpperCAmelCase=30522  ,  UpperCAmelCase=768  ,  UpperCAmelCase=12  ,  UpperCAmelCase=12  ,  UpperCAmelCase=3072  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-1_2  ,  UpperCAmelCase=0  ,  UpperCAmelCase="topK"  ,  UpperCAmelCase="constant"  ,  UpperCAmelCase=0.0  ,  **UpperCAmelCase  ,  )					->       int:
														super().__init__(pad_token_id=UpperCAmelCase  ,  **UpperCAmelCase  )

														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  hidden_act
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  initializer_range
														_snake_case       =  layer_norm_eps
														_snake_case       =  pruning_method
														_snake_case       =  mask_init
														_snake_case       =  mask_scale