






"""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 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							['vqvae']


							def __init__(			self      :						Optional[int]			,    snake_case      :						AutoencoderKL			,    snake_case      :						UNetaDConditionModel			,    snake_case      :						Mel			,    snake_case      :						Union[DDIMScheduler, DDPMScheduler]			,    ):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													return 50 if isinstance(self.scheduler			,    snake_case							) else 1000


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						str = None			,    snake_case      :						np.ndarray = None			,    snake_case      :						int = 0			,    snake_case      :						int = 0			,    snake_case      :						int = None			,    snake_case      :						torch.Generator = None			,    snake_case      :						float = 0			,    snake_case      :						float = 0			,    snake_case      :						torch.Generator = None			,    snake_case      :						float = 0			,    snake_case      :						torch.Tensor = None			,    snake_case      :						torch.Tensor = None			,    snake_case      :						Dict=True			,    ):
													'''simple docstring'''





													A__						:  Dict		  =  steps or self.get_default_steps()
													self.scheduler.set_timesteps(snake_case							)
													A__						:  int		  =  step_generator or generator
													# For backwards compatibility
													if type(self.unet.config.sample_size							) == int:
																			A__						:  List[str]		  =  (self.unet.config.sample_size, self.unet.config.sample_size)
													if noise is None:
																			A__						:  Optional[Any]		  =  randn_tensor(
																			    (
																			        batch_size,
																			        self.unet.config.in_channels,
																			        self.unet.config.sample_size[0],
																			        self.unet.config.sample_size[1],
																			    )			,    generator=snake_case			,    device=self.device			,    )
													A__						:  Union[str, Any]		  =  noise
													A__						:  Optional[int]		  =  None

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

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

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

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

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

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

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

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

													A__						:  Union[str, Any]		  =  (images / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  images.cpu().permute(0			,    2			,    3			,    1							).numpy()
													A__						:  Optional[Any]		  =  (images * 255).round().astype("""uint8"""							)
													A__						:  List[str]		  =  list(
													    (Image.fromarray(_[:, :, 0]							) for _ in images)
													    if images.shape[3] == 1
													    else (Image.fromarray(snake_case			,    mode="""RGB"""							).convert("""L"""							) for _ in images)							)

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

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


							@torch.no_grad()
							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						List[Image.Image]			,    snake_case      :						int = 50							):
													'''simple docstring'''





													assert isinstance(self.scheduler			,    snake_case							)
													self.scheduler.set_timesteps(snake_case							)
													A__						:  Union[str, Any]		  =  np.array(
													    [np.frombuffer(image.tobytes()			,    dtype="""uint8"""							).reshape((1, image.height, image.width)							) for image in images]							)
													A__						:  Optional[int]		  =  (sample / 255) * 2 - 1
													A__						:  List[Any]		  =  torch.Tensor(snake_case							).to(self.device							)

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

													return sample


							@staticmethod
							def 						_UpperCamelCase       (			snake_case      :						torch.Tensor			,    snake_case      :						torch.Tensor			,    snake_case      :						float							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  acos(torch.dot(torch.flatten(snake_case							)			,    torch.flatten(snake_case							)							) / torch.norm(snake_case							) / torch.norm(snake_case							)							)
													return sin((1 - alpha) * theta							) * xa / sin(snake_case							) + sin(alpha * theta							) * xa / sin(snake_case							)














"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_    =							logging.get_logger(__name__)

A_    =							{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'roc_bert'


							def __init__(			self      :						Tuple			,    snake_case      :						Union[str, Any]=3_0522			,    snake_case      :						Optional[Any]=768			,    snake_case      :						Any=12			,    snake_case      :						Tuple=12			,    snake_case      :						Any=3072			,    snake_case      :						Tuple="gelu"			,    snake_case      :						Optional[int]=0.1			,    snake_case      :						List[str]=0.1			,    snake_case      :						List[str]=512			,    snake_case      :						int=2			,    snake_case      :						Optional[Any]=0.02			,    snake_case      :						str=1e-12			,    snake_case      :						int=True			,    snake_case      :						Optional[Any]=0			,    snake_case      :						str="absolute"			,    snake_case      :						int=None			,    snake_case      :						int=True			,    snake_case      :						Optional[int]=True			,    snake_case      :						List[Any]=768			,    snake_case      :						Dict=910			,    snake_case      :						Tuple=512			,    snake_case      :						Tuple=2_4858			,    snake_case      :						Optional[Any]=True			,    **snake_case      :						Any			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  vocab_size
													A__						:  Union[str, Any]		  =  max_position_embeddings
													A__						:  Any		  =  hidden_size
													A__						:  Optional[int]		  =  num_hidden_layers
													A__						:  Optional[Any]		  =  num_attention_heads
													A__						:  int		  =  intermediate_size
													A__						:  Optional[Any]		  =  hidden_act
													A__						:  Any		  =  hidden_dropout_prob
													A__						:  Tuple		  =  attention_probs_dropout_prob
													A__						:  Union[str, Any]		  =  initializer_range
													A__						:  str		  =  type_vocab_size
													A__						:  Any		  =  layer_norm_eps
													A__						:  List[str]		  =  use_cache
													A__						:  List[str]		  =  enable_pronunciation
													A__						:  int		  =  enable_shape
													A__						:  Tuple		  =  pronunciation_embed_dim
													A__						:  str		  =  pronunciation_vocab_size
													A__						:  Optional[Any]		  =  shape_embed_dim
													A__						:  str		  =  shape_vocab_size
													A__						:  List[str]		  =  concat_input
													A__						:  str		  =  position_embedding_type
													A__						:  str		  =  classifier_dropout
													super().__init__(pad_token_id=snake_case			,    **snake_case							)






