



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase						    =  logging.get_logger(__name__)

_lowerCamelCase						    =  {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""roberta"""





     def __init__(       self :							str     ,  UpperCamelCase :							List[str]=5_0_2_6_5     ,  UpperCamelCase :							Any=7_6_8     ,  UpperCamelCase :							List[str]=1_2     ,  UpperCamelCase :							int=1_2     ,  UpperCamelCase :							int=3_0_7_2     ,  UpperCamelCase :							Any="gelu"     ,  UpperCamelCase :							Union[str, Any]=0.1     ,  UpperCamelCase :							Dict=0.1     ,  UpperCamelCase :							List[str]=5_1_2     ,  UpperCamelCase :							List[str]=2     ,  UpperCamelCase :							str=0.0_2     ,  UpperCamelCase :							Union[str, Any]=1E-12     ,  UpperCamelCase :							Union[str, Any]=1     ,  UpperCamelCase :							List[str]=0     ,  UpperCamelCase :							List[str]=2     ,  UpperCamelCase :							List[Any]="absolute"     ,  UpperCamelCase :							List[Any]=True     ,  UpperCamelCase :							List[str]=None     ,  **UpperCamelCase :							Tuple     ,  )->Tuple:
      super().__init__(pad_token_id=UpperCamelCase     ,  bos_token_id=UpperCamelCase     ,  eos_token_id=UpperCamelCase     ,  **UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	vocab_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    str       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	hidden_act
      __SCREAMING_SNAKE_CASE       :    Dict       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	hidden_dropout_prob
      __SCREAMING_SNAKE_CASE       :    str       =	attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Any       =	max_position_embeddings
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	type_vocab_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    List[str]       =	layer_norm_eps
      __SCREAMING_SNAKE_CASE       :    str       =	position_embedding_type
      __SCREAMING_SNAKE_CASE       :    Tuple       =	use_cache
      __SCREAMING_SNAKE_CASE       :    str       =	classifier_dropout






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     @property
     def 	__snake_case    (       self :							int				)->Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       __SCREAMING_SNAKE_CASE       :    List[str]       =	{0: "batch", 1: "choice", 2: "sequence"}
      else:
       __SCREAMING_SNAKE_CASE       :    Any       =	{0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ]				)











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 ):
     lowerCAmelCase					=			["""vqvae"""]





     def __init__(       self :							Tuple     ,  UpperCamelCase :							AutoencoderKL     ,  UpperCamelCase :							UNetaDConditionModel     ,  UpperCamelCase :							Mel     ,  UpperCamelCase :							Union[DDIMScheduler, DDPMScheduler]     ,  )->Tuple:
      super().__init__()
      self.register_modules(unet=UpperCamelCase     ,  scheduler=UpperCamelCase     ,  mel=UpperCamelCase     ,  vqvae=UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]				)->int:
      return 5_0 if isinstance(self.scheduler     ,  UpperCamelCase				) else 1_0_0_0





     @torch.no_grad()
     def __call__(       self :							Dict     ,  UpperCamelCase :							int = 1     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							np.ndarray = None     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = None     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							Any=True     ,  )->Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	steps or self.get_default_steps()
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size				) == int:
       __SCREAMING_SNAKE_CASE       :    Dict       =	(self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
       __SCREAMING_SNAKE_CASE       :    Any       =	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     ,  )
      __SCREAMING_SNAKE_CASE       :    Any       =	noise
      __SCREAMING_SNAKE_CASE       :    Any       =	None

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

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

       if start_step > 0:
        __SCREAMING_SNAKE_CASE       :    List[str]       =	self.scheduler.add_noise(UpperCamelCase     ,  UpperCamelCase     ,  self.scheduler.timesteps[start_step - 1]				)

       __SCREAMING_SNAKE_CASE       :    int       =	(
           self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
       )
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(mask_start_secs * pixels_per_second				)
       __SCREAMING_SNAKE_CASE       :    int       =	int(mask_end_secs * pixels_per_second				)
       __SCREAMING_SNAKE_CASE       :    Any       =	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				):
        __SCREAMING_SNAKE_CASE       :    str       =	self.unet(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)["sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    int       =	self.unet(UpperCamelCase     ,  UpperCamelCase				)["sample"]

       if isinstance(self.scheduler     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    int       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  eta=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    Tuple       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]

       if mask is not None:
        if mask_start > 0:
         __SCREAMING_SNAKE_CASE       :    int       =	mask[:, step, :, :mask_start]
        if mask_end > 0:
         __SCREAMING_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
       __SCREAMING_SNAKE_CASE       :    Any       =	1 / self.vqvae.config.scaling_factor * images
       __SCREAMING_SNAKE_CASE       :    Any       =	self.vqvae.decode(UpperCamelCase				)["sample"]

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

      __SCREAMING_SNAKE_CASE       :    List[str]       =	[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 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Image.Image]     ,  UpperCamelCase :							int = 5_0				)->np.ndarray:
      assert isinstance(self.scheduler     ,  UpperCamelCase				)
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	np.array(
          [np.frombuffer(image.tobytes()     ,  dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	(sample / 2_5_5) * 2 - 1
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.Tensor(UpperCamelCase				).to(self.device				)

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

      return sample






     @staticmethod
     def 	__snake_case    (       UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							float				)->torch.Tensor:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	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				)






