


"""simple docstring"""


import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCamelCase						:				List[str]             =      logging.get_logger(__name__)  # pylint: disable=invalid-name







def  __snake_case       (   UpperCamelCase__	)      ->		Optional[int]:







  """simple docstring"""





  warnings.warn(
      'The preprocess method is deprecated and will be removed in a future version. Please'
      ' use VaeImageProcessor.preprocess instead' ,			UpperCamelCase__ ,			)
  if isinstance(UpperCamelCase__ ,			torch.Tensor	):
    return image
  elif isinstance(UpperCamelCase__ ,			PIL.Image.Image	):
    A					       = [image]

  if isinstance(image[0] ,			PIL.Image.Image	):
    A       , A					       = image[0].size
    A       , A					       = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

    A					       = [np.array(i.resize((w, h) ,			resample=PIL_INTERPOLATION['lanczos']	)	)[None, :] for i in image]
    A					       = np.concatenate(UpperCamelCase__ ,			axis=0	)
    A					       = np.array(UpperCamelCase__	).astype(np.floataa	) / 255.0
    A					       = image.transpose(0 ,			3 ,			1 ,			2	)
    A					       = 2.0 * image - 1.0
    A					       = torch.from_numpy(UpperCamelCase__	)
  elif isinstance(image[0] ,			torch.Tensor	):
    A					       = torch.cat(UpperCamelCase__ ,			dim=0	)
  return image







def  __snake_case       (   UpperCamelCase__	)      ->		Dict:







  """simple docstring"""





  if isinstance(UpperCamelCase__ ,			torch.Tensor	):
    return mask
  elif isinstance(UpperCamelCase__ ,			PIL.Image.Image	):
    A					       = [mask]

  if isinstance(mask[0] ,			PIL.Image.Image	):
    A       , A					       = mask[0].size
    A       , A					       = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    A					       = [np.array(m.convert('L'	).resize((w, h) ,			resample=PIL_INTERPOLATION['nearest']	)	)[None, :] for m in mask]
    A					       = np.concatenate(UpperCamelCase__ ,			axis=0	)
    A					       = mask.astype(np.floataa	) / 255.0
    A					       = 0
    A					       = 1
    A					       = torch.from_numpy(UpperCamelCase__	)
  elif isinstance(mask[0] ,			torch.Tensor	):
    A					       = torch.cat(UpperCamelCase__ ,			dim=0	)
  return mask



class 			lowerCamelCase__						(   UpperCAmelCase_			):
      lowerCAmelCase       =     42
      lowerCAmelCase       =     42







      def __init__(							self   :    Optional[int]			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Tuple		):
        super().__init__()
        self.register_modules(unet=_lowercase			,			scheduler=_lowercase		)







      @torch.no_grad()
      def __call__(							self   :    Any			,			_lowercase   :    Union[torch.Tensor, PIL.Image.Image]			,			_lowercase   :    Union[torch.Tensor, PIL.Image.Image]			,			_lowercase   :    int = 250			,			_lowercase   :    float = 0.0			,			_lowercase   :    int = 10			,			_lowercase   :    int = 10			,			_lowercase   :    Optional[Union[torch.Generator, List[torch.Generator]]] = None			,			_lowercase   :    Optional[str] = "pil"			,			_lowercase   :    bool = True			,			):
        A					       = image

        A					       = _preprocess_image(_lowercase		)
        A					       = original_image.to(device=self.device			,			dtype=self.unet.dtype		)
        A					       = _preprocess_mask(_lowercase		)
        A					       = mask_image.to(device=self.device			,			dtype=self.unet.dtype		)

        A					       = original_image.shape[0]

        # sample gaussian noise to begin the loop
        if isinstance(_lowercase			,			_lowercase		) and len(_lowercase		) != batch_size:
          raise ValueError(
              f'You have passed a list of generators of length {len(_lowercase		)}, but requested an effective batch'
              f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

        A					       = original_image.shape
        A					       = randn_tensor(_lowercase			,			generator=_lowercase			,			device=self.device			,			dtype=self.unet.dtype		)

        # set step values
        self.scheduler.set_timesteps(_lowercase			,			_lowercase			,			_lowercase			,			self.device		)
        A					       = eta

        A					       = self.scheduler.timesteps[0] + 1
        A					       = generator[0] if isinstance(_lowercase			,			_lowercase		) else generator
        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
          if t < t_last:
            # predict the noise residual
            A					       = self.unet(_lowercase			,			_lowercase		).sample
            # compute previous image: x_t -> x_t-1
            A					       = self.scheduler.step(_lowercase			,			_lowercase			,			_lowercase			,			_lowercase			,			_lowercase			,			_lowercase		).prev_sample

          else:
            # compute the reverse: x_t-1 -> x_t
            A					       = self.scheduler.undo_step(_lowercase			,			_lowercase			,			_lowercase		)
          A					       = t

        A					       = (image / 2 + 0.5).clamp(0			,			1		)
        A					       = image.cpu().permute(0			,			2			,			3			,			1		).numpy()
        if output_type == "pil":
          A					       = self.numpy_to_pil(_lowercase		)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=_lowercase		)









"""simple docstring"""


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


UpperCamelCase						:				Tuple             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """lilt"""







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Dict=30_522			,			_lowercase   :    Any=768			,			_lowercase   :    Union[str, Any]=12			,			_lowercase   :    List[str]=12			,			_lowercase   :    str=3_072			,			_lowercase   :    int="gelu"			,			_lowercase   :    Union[str, Any]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    Optional[Any]=512			,			_lowercase   :    Optional[Any]=2			,			_lowercase   :    Tuple=0.0_2			,			_lowercase   :    int=1e-12			,			_lowercase   :    Any=0			,			_lowercase   :    List[str]="absolute"			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Optional[int]=1_024			,			**_lowercase   :    Union[str, Any]			,			):
								super().__init__(pad_token_id=_lowercase			,			**_lowercase		)

								A					       = vocab_size
								A					       = hidden_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = hidden_act
								A					       = intermediate_size
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = type_vocab_size
								A					       = initializer_range
								A					       = layer_norm_eps
								A					       = position_embedding_type
								A					       = classifier_dropout
								A					       = channel_shrink_ratio
								A					       = max_ad_position_embeddings





