





"""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


a_ =			logging.get_logger(__name__)  # pylint: disable=invalid-name







def 		__lowercase     (   snake_case_  :  Union[List, PIL.Image.Image, torch.Tensor]						)   ->Any:



    '''simple docstring'''







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

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

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







def 		__lowercase     (   snake_case_  :  Union[List, PIL.Image.Image, torch.Tensor]						)   ->int:



    '''simple docstring'''







    if isinstance(snake_case_				,torch.Tensor						):
        return mask
    elif isinstance(snake_case_				,PIL.Image.Image						):
        __A    :						List[Any]						 =     [mask]

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






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     42

 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     super().__init__()
     self.register_modules(unet=__lowerCamelCase					,						scheduler=__lowerCamelCase  )







 @torch.no_grad()
 def __call__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = 250					,						__lowerCamelCase = 0.0					,						__lowerCamelCase = 10					,						__lowerCamelCase = 10					,						__lowerCamelCase = None					,						__lowerCamelCase = "pil"					,						__lowerCamelCase = True					,						):



     '''simple docstring'''




     __A    :						Any						 =     image

     __A    :						Tuple						 =     _preprocess_image(__lowerCamelCase  )
     __A    :						List[Any]						 =     original_image.to(device=self.device					,						dtype=self.unet.dtype  )
     __A    :						str						 =     _preprocess_mask(__lowerCamelCase  )
     __A    :						Optional[int]						 =     mask_image.to(device=self.device					,						dtype=self.unet.dtype  )

     __A    :						int						 =     original_image.shape[0]

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

     __A    :						List[Any]						 =     original_image.shape
     __A    :						Union[str, Any]						 =     randn_tensor(__lowerCamelCase					,						generator=__lowerCamelCase					,						device=self.device					,						dtype=self.unet.dtype  )

     # set step values
     self.scheduler.set_timesteps(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						self.device  )
     __A    :						Any						 =     eta

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

         else:
             # compute the reverse: x_t-1 -> x_t
             __A    :						int						 =     self.scheduler.undo_step(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )
         __A    :						str						 =     t

     __A    :						int						 =     (image / 2 + 0.5).clamp(0					,						1  )
     __A    :						Dict						 =     image.cpu().permute(0					,						2					,						3					,						1  ).numpy()
     if output_type == "pil":
         __A    :						str						 =     self.numpy_to_pil(__lowerCamelCase  )

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=__lowerCamelCase  )











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_ =			{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			["""PerceiverFeatureExtractor"""]
       a_ =			["""PerceiverImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			[
           """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """PerceiverForImageClassificationConvProcessing""",
           """PerceiverForImageClassificationFourier""",
           """PerceiverForImageClassificationLearned""",
           """PerceiverForMaskedLM""",
           """PerceiverForMultimodalAutoencoding""",
           """PerceiverForOpticalFlow""",
           """PerceiverForSequenceClassification""",
           """PerceiverLayer""",
           """PerceiverModel""",
           """PerceiverPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
       from .tokenization_perceiver import PerceiverTokenizer

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_perceiver import PerceiverFeatureExtractor
              from .image_processing_perceiver import PerceiverImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_perceiver import (
                  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PerceiverForImageClassificationConvProcessing,
                  PerceiverForImageClassificationFourier,
                  PerceiverForImageClassificationLearned,
                  PerceiverForMaskedLM,
                  PerceiverForMultimodalAutoencoding,
                  PerceiverForOpticalFlow,
                  PerceiverForSequenceClassification,
                  PerceiverLayer,
                  PerceiverModel,
                  PerceiverPreTrainedModel,
              )

else:
       import sys

       a_ =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




