






'''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


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


def 				lowercase_					(     __A			:							Union[List, PIL.Image.Image, torch.Tensor]      )							->   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      ):
  lowercase       : Dict						=[image]

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

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


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



 """simple docstring"""




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

 if isinstance(mask[0]       ,   PIL.Image.Image      ):
  lowercase	,		lowercase       : List[Any]						=mask[0].size
  lowercase	,		lowercase       : Tuple						=(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
  lowercase       : int						=[np.array(m.convert('''L'''      ).resize((w, h)       ,   resample=PIL_INTERPOLATION['''nearest''']      )      )[None, :] for m in mask]
  lowercase       : Tuple						=np.concatenate(__A       ,   axis=0      )
  lowercase       : Optional[int]						=mask.astype(np.floataa      ) / 255.0
  lowercase       : Dict						=0
  lowercase       : Optional[Any]						=1
  lowercase       : Dict						=torch.from_numpy(__A      )
 elif isinstance(mask[0]       ,   torch.Tensor      ):
  lowercase       : Dict						=torch.cat(__A       ,   dim=0      )
 return mask





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      42
      UpperCamelCase_             =      42






      def __init__(			self      :		Any  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		List[Any]		)	->	Union[str, Any]:


       '''simple docstring'''

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






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


       '''simple docstring'''

       lowercase       : Optional[Any]						=image

       lowercase       : str						=_preprocess_image(UpperCAmelCase		)
       lowercase       : int						=original_image.to(device=self.device  ,   dtype=self.unet.dtype		)
       lowercase       : Tuple						=_preprocess_mask(UpperCAmelCase		)
       lowercase       : Optional[int]						=mask_image.to(device=self.device  ,   dtype=self.unet.dtype		)

       lowercase       : Optional[Any]						=original_image.shape[0]

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

       lowercase       : Dict						=original_image.shape
       lowercase       : Tuple						=randn_tensor(UpperCAmelCase  ,   generator=UpperCAmelCase  ,   device=self.device  ,   dtype=self.unet.dtype		)

       # set step values
       self.scheduler.set_timesteps(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   self.device		)
       lowercase       : List[Any]						=eta

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

        else:
         # compute the reverse: x_t-1 -> x_t
         lowercase       : Optional[int]						=self.scheduler.undo_step(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
        lowercase       : Any						=t

       lowercase       : Dict						=(image / 2 + 0.5).clamp(0  ,   1		)
       lowercase       : Union[str, Any]						=image.cpu().permute(0  ,   2  ,   3  ,   1		).numpy()
       if output_type == "pil":
        lowercase       : str						=self.numpy_to_pil(UpperCAmelCase		)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=UpperCAmelCase		)











'''simple docstring'''



import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate


def 				lowercase_					(     __A			:							str      )							->   Union[str, Any]:



 """simple docstring"""




 return x + 2





class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      def 					A__   (			self      :		Dict		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Optional[int]						='''x = 3'''
       lowercase       : Any						={}
       lowercase       : Union[str, Any]						=evaluate(UpperCAmelCase  ,   {}  ,   state=UpperCAmelCase		)
       assert result == 3
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3}		)

       lowercase       : str						='''x = y'''
       lowercase       : Optional[int]						={'''y''': 5}
       lowercase       : List[str]						=evaluate(UpperCAmelCase  ,   {}  ,   state=UpperCAmelCase		)
       # evaluate returns the value of the last assignment.
       assert result == 5
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 5, '''y''': 5}		)






      def 					A__   (			self      :		str		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Optional[int]						='''y = add_two(x)'''
       lowercase       : str						={'''x''': 3}
       lowercase       : List[str]						=evaluate(UpperCAmelCase  ,   {'''add_two''': add_two}  ,   state=UpperCAmelCase		)
       assert result == 5
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''y''': 5}		)

       # Won't work without the tool
       with CaptureStdout() as out:
        lowercase       : Optional[Any]						=evaluate(UpperCAmelCase  ,   {}  ,   state=UpperCAmelCase		)
       assert result is None
       assert "tried to execute add_two" in out.out






      def 					A__   (			self      :		List[Any]		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : int						='''x = 3'''
       lowercase       : Dict						={}
       lowercase       : List[Any]						=evaluate(UpperCAmelCase  ,   {}  ,   state=UpperCAmelCase		)
       assert result == 3
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3}		)






      def 					A__   (			self      :		str		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Optional[Any]						='''test_dict = {\'x\': x, \'y\': add_two(x)}'''
       lowercase       : str						={'''x''': 3}
       lowercase       : Tuple						=evaluate(UpperCAmelCase  ,   {'''add_two''': add_two}  ,   state=UpperCAmelCase		)
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''y''': 5}		)
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}		)






      def 					A__   (			self      :		Union[str, Any]		)	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : Optional[Any]						='''x = 3\ny = 5'''
       lowercase       : int						={}
       lowercase       : List[str]						=evaluate(UpperCAmelCase  ,   {}  ,   state=UpperCAmelCase		)
       # evaluate returns the value of the last assignment.
       assert result == 5
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''y''': 5}		)






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

       lowercase       : List[str]						='''text = f\'This is x: {x}.\''''
       lowercase       : Union[str, Any]						={'''x''': 3}
       lowercase       : Tuple						=evaluate(UpperCAmelCase  ,   {}  ,   state=UpperCAmelCase		)
       # evaluate returns the value of the last assignment.
       assert result == "This is x: 3."
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''text''': '''This is x: 3.'''}		)






      def 					A__   (			self      :		Union[str, Any]		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Tuple						='''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
       lowercase       : Union[str, Any]						={'''x''': 3}
       lowercase       : Any						=evaluate(UpperCAmelCase  ,   {}  ,   state=UpperCAmelCase		)
       # evaluate returns the value of the last assignment.
       assert result == 2
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''y''': 2}		)

       lowercase       : Optional[Any]						={'''x''': 8}
       lowercase       : str						=evaluate(UpperCAmelCase  ,   {}  ,   state=UpperCAmelCase		)
       # evaluate returns the value of the last assignment.
       assert result == 5
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 8, '''y''': 5}		)






      def 					A__   (			self      :		str		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : List[str]						='''test_list = [x, add_two(x)]'''
       lowercase       : Any						={'''x''': 3}
       lowercase       : Tuple						=evaluate(UpperCAmelCase  ,   {'''add_two''': add_two}  ,   state=UpperCAmelCase		)
       self.assertListEqual(UpperCAmelCase  ,   [3, 5]		)
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''test_list''': [3, 5]}		)






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

       lowercase       : str						='''y = x'''
       lowercase       : Dict						={'''x''': 3}
       lowercase       : Tuple						=evaluate(UpperCAmelCase  ,   {}  ,   state=UpperCAmelCase		)
       assert result == 3
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''y''': 3}		)






      def 					A__   (			self      :		str		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Any						='''test_list = [x, add_two(x)]\ntest_list[1]'''
       lowercase       : Any						={'''x''': 3}
       lowercase       : Union[str, Any]						=evaluate(UpperCAmelCase  ,   {'''add_two''': add_two}  ,   state=UpperCAmelCase		)
       assert result == 5
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''test_list''': [3, 5]}		)

       lowercase       : int						='''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
       lowercase       : Union[str, Any]						={'''x''': 3}
       lowercase       : Tuple						=evaluate(UpperCAmelCase  ,   {'''add_two''': add_two}  ,   state=UpperCAmelCase		)
       assert result == 5
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}		)






      def 					A__   (			self      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Optional[int]						='''x = 0\nfor i in range(3):\n    x = i'''
       lowercase       : List[str]						={}
       lowercase       : Union[str, Any]						=evaluate(UpperCAmelCase  ,   {'''range''': range}  ,   state=UpperCAmelCase		)
       assert result == 2
       self.assertDictEqual(UpperCAmelCase  ,   {'''x''': 2, '''i''': 2}		)



