'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_lowerCAmelCase  :Tuple  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Tuple  				=    {
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   str							=						"data2vec-vision"






    def __init__(       self    ,							lowercase__=768    ,							lowercase__=12    ,							lowercase__=12    ,							lowercase__=3_072    ,							lowercase__="gelu"    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.0_2    ,							lowercase__=1E-12    ,							lowercase__=224    ,							lowercase__=16    ,							lowercase__=3    ,							lowercase__=False    ,							lowercase__=False    ,							lowercase__=False    ,							lowercase__=False    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=True    ,							lowercase__=[3, 5, 7, 11]    ,							lowercase__=[1, 2, 3, 6]    ,							lowercase__=True    ,							lowercase__=0.4    ,							lowercase__=256    ,							lowercase__=1    ,							lowercase__=False    ,							lowercase__=255    ,							**lowercase__    ,							)							-> List[str]:
         super().__init__(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Dict    	=       layer_norm_eps

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       image_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       patch_size
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_channels
         SCREAMING_SNAKE_CASE					:			int    	=       use_mask_token
         SCREAMING_SNAKE_CASE					:			Tuple    	=       use_absolute_position_embeddings
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_relative_position_bias
         SCREAMING_SNAKE_CASE					:			List[str]    	=       use_shared_relative_position_bias
         SCREAMING_SNAKE_CASE					:			int    	=       layer_scale_init_value
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			List[str]    	=       use_mean_pooling
         # decode head attributes (semantic segmentation)
         SCREAMING_SNAKE_CASE					:			int    	=       out_indices
         SCREAMING_SNAKE_CASE					:			Dict    	=       pool_scales
         # auxiliary head attributes (semantic segmentation)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_auxiliary_head
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       auxiliary_loss_weight
         SCREAMING_SNAKE_CASE					:			List[str]    	=       auxiliary_channels
         SCREAMING_SNAKE_CASE					:			int    	=       auxiliary_num_convs
         SCREAMING_SNAKE_CASE					:			Any    	=       auxiliary_concat_input
         SCREAMING_SNAKE_CASE					:			int    	=       semantic_loss_ignore_index






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						version.parse("1.11" )






    @property
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ] )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4




'''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


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





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

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

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


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     if isinstance(a_       ,      torch.Tensor		):
          return mask
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [mask]

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






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   UNetaDModel
    snake_case__							:   RePaintScheduler






    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         super().__init__()
         self.register_modules(unet=lowercase__    ,							scheduler=lowercase__ )






    @torch.no_grad()
    def __call__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 250    ,							lowercase__ = 0.0    ,							lowercase__ = 10    ,							lowercase__ = 10    ,							lowercase__ = None    ,							lowercase__ = "pil"    ,							lowercase__ = True    ,							)							-> Union[ImagePipelineOutput, Tuple]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image

         SCREAMING_SNAKE_CASE					:			List[str]    	=       _preprocess_image(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       original_image.to(device=self.device    ,							dtype=self.unet.dtype )
         SCREAMING_SNAKE_CASE					:			Any    	=       _preprocess_mask(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       mask_image.to(device=self.device    ,							dtype=self.unet.dtype )

         SCREAMING_SNAKE_CASE					:			int    	=       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.""" )

         SCREAMING_SNAKE_CASE					:			Dict    	=       original_image.shape
         SCREAMING_SNAKE_CASE					:			Any    	=       randn_tensor(lowercase__    ,							generator=lowercase__    ,							device=self.device    ,							dtype=self.unet.dtype )

         # set step values
         self.scheduler.set_timesteps(lowercase__    ,							lowercase__    ,							lowercase__    ,							self.device )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       eta

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler.timesteps[0] + 1
         SCREAMING_SNAKE_CASE					:			Tuple    	=       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
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.unet(lowercase__    ,							lowercase__ ).sample
                   # compute previous image: x_t -> x_t-1
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ ).prev_sample

              else:
                   # compute the reverse: x_t-1 -> x_t
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler.undo_step(lowercase__    ,							lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       t

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       (image / 2 + 0.5).clamp(0    ,							1 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image.cpu().permute(0    ,							2    ,							3    ,							1 ).numpy()
         if output_type == "pil":
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.numpy_to_pil(lowercase__ )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=lowercase__ )



