






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


UpperCamelCase_	:				Optional[int]										= logging.get_logger(__name__)

UpperCamelCase_	:				List[Any]										= {
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class      _lowercase (     lowerCAmelCase						):
      _a   :     Optional[int]								=   '''luke'''


      def __init__(    self				:	List[Any]							,   a				:	int=5_0_2_6_7							,   a				:	Optional[int]=5_0_0_0_0_0							,   a				:	Union[str, Any]=7_6_8							,   a				:	List[str]=2_5_6							,   a				:	Optional[Any]=1_2							,   a				:	Dict=1_2							,   a				:	List[str]=3_0_7_2							,   a				:	List[str]="gelu"							,   a				:	Union[str, Any]=0.1							,   a				:	str=0.1							,   a				:	Union[str, Any]=5_1_2							,   a				:	int=2							,   a				:	List[str]=0.0_2							,   a				:	Optional[int]=1e-12							,   a				:	int=True							,   a				:	Dict=None							,   a				:	Any=1							,   a				:	Any=0							,   a				:	Optional[int]=2							,   **a				:	Optional[Any]							,   ):





            """simple docstring"""







            super().__init__(pad_token_id=a							,   bos_token_id=a							,   eos_token_id=a							,   **a						)

            __snake_case      :    Tuple  			=vocab_size
            __snake_case      :    Dict  			=entity_vocab_size
            __snake_case      :    str  			=hidden_size
            __snake_case      :    int  			=entity_emb_size
            __snake_case      :    List[str]  			=num_hidden_layers
            __snake_case      :    Any  			=num_attention_heads
            __snake_case      :    Union[str, Any]  			=hidden_act
            __snake_case      :    str  			=intermediate_size
            __snake_case      :    Union[str, Any]  			=hidden_dropout_prob
            __snake_case      :    List[Any]  			=attention_probs_dropout_prob
            __snake_case      :    Any  			=max_position_embeddings
            __snake_case      :    Tuple  			=type_vocab_size
            __snake_case      :    Tuple  			=initializer_range
            __snake_case      :    List[str]  			=layer_norm_eps
            __snake_case      :    Tuple  			=use_entity_aware_attention
            __snake_case      :    Any  			=classifier_dropout










"""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      _lowercase (     lowerCAmelCase						):
      _a   :     Optional[Any]								=   ['''vqvae''']


      def __init__(    self				:	Optional[Any]							,   a				:	AutoencoderKL							,   a				:	UNetaDConditionModel							,   a				:	Mel							,   a				:	Union[DDIMScheduler, DDPMScheduler]							,   ):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            return 5_0 if isinstance(self.scheduler							,   a						) else 1_0_0_0


      @torch.no_grad()
      def __call__(    self				:	Optional[int]							,   a				:	int = 1							,   a				:	str = None							,   a				:	np.ndarray = None							,   a				:	int = 0							,   a				:	int = 0							,   a				:	int = None							,   a				:	torch.Generator = None							,   a				:	float = 0							,   a				:	float = 0							,   a				:	torch.Generator = None							,   a				:	float = 0							,   a				:	torch.Tensor = None							,   a				:	torch.Tensor = None							,   a				:	Optional[int]=True							,   ):





            """simple docstring"""







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

            if audio_file is not None or raw_audio is not None:
                  self.mel.load_audio(a							,   a						)
                  __snake_case      :    List[Any]  			=self.mel.audio_slice_to_image(a						)
                  __snake_case      :    Tuple  			=np.frombuffer(input_image.tobytes()							,   dtype='''uint8'''						).reshape(
                      (input_image.height, input_image.width)						)
                  __snake_case      :    str  			=(input_image / 2_5_5) * 2 - 1
                  __snake_case      :    Optional[int]  			=torch.tensor(input_image[np.newaxis, :, :]							,   dtype=torch.float						).to(self.device						)

                  if self.vqvae is not None:
                        __snake_case      :    Tuple  			=self.vqvae.encode(torch.unsqueeze(a							,   0						)						).latent_dist.sample(
                            generator=a						)[0]
                        __snake_case      :    Optional[int]  			=self.vqvae.config.scaling_factor * input_images

                  if start_step > 0:
                        __snake_case      :    str  			=self.scheduler.add_noise(a							,   a							,   self.scheduler.timesteps[start_step - 1]						)

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

            for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
                  if isinstance(self.unet							,   a						):
                        __snake_case      :    List[str]  			=self.unet(a							,   a							,   a						)['''sample''']
                  else:
                        __snake_case      :    Union[str, Any]  			=self.unet(a							,   a						)['''sample''']

                  if isinstance(self.scheduler							,   a						):
                        __snake_case      :    List[str]  			=self.scheduler.step(
                            model_output=a							,   timestep=a							,   sample=a							,   eta=a							,   generator=a							,   )['''prev_sample''']
                  else:
                        __snake_case      :    List[Any]  			=self.scheduler.step(
                            model_output=a							,   timestep=a							,   sample=a							,   generator=a							,   )['''prev_sample''']

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

            if self.vqvae is not None:
                  # 0.18215 was scaling factor used in training to ensure unit variance
                  __snake_case      :    str  			=1 / self.vqvae.config.scaling_factor * images
                  __snake_case      :    Optional[int]  			=self.vqvae.decode(a						)['''sample''']

            __snake_case      :    int  			=(images / 2 + 0.5).clamp(0							,   1						)
            __snake_case      :    Optional[Any]  			=images.cpu().permute(0							,   2							,   3							,   1						).numpy()
            __snake_case      :    Optional[int]  			=(images * 2_5_5).round().astype('''uint8'''						)
            __snake_case      :    Tuple  			=list(
                (Image.fromarray(_[:, :, 0]						) for _ in images)
                if images.shape[3] == 1
                else (Image.fromarray(a							,   mode='''RGB'''						).convert('''L'''						) for _ in images)						)

            __snake_case      :    Union[str, Any]  			=[self.mel.image_to_audio(a						) for _ in images]
            if not return_dict:
                  return images, (self.mel.get_sample_rate(), audios)

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


      @torch.no_grad()
      def _UpperCamelCase						(    self				:	int							,   a				:	List[Image.Image]							,   a				:	int = 5_0						):





            """simple docstring"""







            assert isinstance(self.scheduler							,   a						)
            self.scheduler.set_timesteps(a						)
            __snake_case      :    Dict  			=np.array(
                [np.frombuffer(image.tobytes()							,   dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
            __snake_case      :    Any  			=(sample / 2_5_5) * 2 - 1
            __snake_case      :    Any  			=torch.Tensor(a						).to(self.device						)

            for t in self.progress_bar(torch.flip(self.scheduler.timesteps							,   (0,)						)						):
                  __snake_case      :    Union[str, Any]  			=t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                  __snake_case      :    Dict  			=self.scheduler.alphas_cumprod[t]
                  __snake_case      :    str  			=(
                      self.scheduler.alphas_cumprod[prev_timestep]
                      if prev_timestep >= 0
                      else self.scheduler.final_alpha_cumprod
                  )
                  __snake_case      :    int  			=1 - alpha_prod_t
                  __snake_case      :    Tuple  			=self.unet(a							,   a						)['''sample''']
                  __snake_case      :    str  			=(1 - alpha_prod_t_prev) ** 0.5 * model_output
                  __snake_case      :    Dict  			=(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                  __snake_case      :    Optional[int]  			=sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample







      @staticmethod
      def _UpperCamelCase						(    a				:	torch.Tensor							,   a				:	torch.Tensor							,   a				:	float						):





            """simple docstring"""







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


