






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor


class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     def __init__(					self							:						str       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						List[Any]=7       ,			UpperCAmelCase_							:						str=3       ,			UpperCAmelCase_							:						Any=18       ,			UpperCAmelCase_							:						int=30       ,			UpperCAmelCase_							:						Tuple=400       ,			UpperCAmelCase_							:						int=True       ,			UpperCAmelCase_							:						int=None       ,			UpperCAmelCase_							:						int=True       ,			UpperCAmelCase_							:						Optional[int]=None       ,			UpperCAmelCase_							:						Tuple=True       ,			):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     size if size is not None else {"shortest_edge": 20}
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     crop_size if crop_size is not None else {"height": 18, "width": 18}
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     parent
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     batch_size
       SCREAMING_SNAKE_CASE							:   Any						    =     num_channels
       SCREAMING_SNAKE_CASE							:   str						    =     image_size
       SCREAMING_SNAKE_CASE							:   Tuple						    =     min_resolution
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     max_resolution
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     do_resize
       SCREAMING_SNAKE_CASE							:   str						    =     size
       SCREAMING_SNAKE_CASE							:   Dict						    =     do_center_crop
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     crop_size
       SCREAMING_SNAKE_CASE							:   List[str]						    =     do_flip_channel_order






     def 					_A							(					self							:						List[str]   ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_flip_channel_order": self.do_flip_channel_order,
       }





@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE      (      __lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Union[str, Any]								=						MobileViTImageProcessor if is_vision_available() else None


     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   str						    =     MobileViTImageProcessingTester(self   )


     @property
     def 					_A							(					self							:						Union[str, Any]   ):
       return self.image_processor_tester.prepare_image_processor_dict()


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,			"do_resize"   )   )
       self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,			"size"   )   )
       self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,			"do_center_crop"   )   )
       self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,			"center_crop"   )   )
       self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,			"do_flip_channel_order"   )   )


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size       ,			{"shortest_edge": 20}   )
       self.assertEqual(image_processor.crop_size       ,			{"height": 18, "width": 18}   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.image_processing_class.from_dict(self.image_processor_dict       ,			size=42       ,			crop_size=84   )
       self.assertEqual(image_processor.size       ,			{"shortest_edge": 42}   )
       self.assertEqual(image_processor.crop_size       ,			{"height": 84, "width": 84}   )


     def 					_A							(					self							:						Optional[Any]   ):
       pass


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       SCREAMING_SNAKE_CASE							:   str						    =     prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=__SCREAMING_SNAKE_CASE   )
       for image in image_inputs:
         self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			Image.Image   )

       # Test not batched input
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     image_processing(image_inputs[0]       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )       ,			)

       # Test batched
       SCREAMING_SNAKE_CASE							:   str						    =     image_processing(__SCREAMING_SNAKE_CASE       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )       ,			)


     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE							:   int						    =     prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=__SCREAMING_SNAKE_CASE       ,			numpify=__SCREAMING_SNAKE_CASE   )
       for image in image_inputs:
         self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			np.ndarray   )

       # Test not batched input
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     image_processing(image_inputs[0]       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )       ,			)

       # Test batched
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     image_processing(__SCREAMING_SNAKE_CASE       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )       ,			)






     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   int						    =     self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE							:   List[str]						    =     prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=__SCREAMING_SNAKE_CASE       ,			torchify=__SCREAMING_SNAKE_CASE   )
       for image in image_inputs:
         self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			torch.Tensor   )

       # Test not batched input
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     image_processing(image_inputs[0]       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )       ,			)

       # Test batched
       SCREAMING_SNAKE_CASE							:   Any						    =     image_processing(__SCREAMING_SNAKE_CASE       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )       ,			)












'''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_							:	Tuple	     =		logging.get_logger(__name__)  # pylint: disable=invalid-name







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





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

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

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







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





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

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







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




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




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



            '''simple docstring'''




            __snake_case            =	image

            __snake_case            =	_preprocess_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	original_image.to(device=self.device     ,	dtype=self.unet.dtype	)
            __snake_case            =	_preprocess_mask(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	mask_image.to(device=self.device     ,	dtype=self.unet.dtype	)

            __snake_case            =	original_image.shape[0]

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

            __snake_case            =	original_image.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=self.unet.dtype	)

            # set step values
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	eta

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

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

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






