





import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ImageGPTImageProcessor



class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def __init__(    self			:		Tuple ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		List[Any]=7 ,  lowerCAmelCase_			:		int=3 ,  lowerCAmelCase_			:		int=1_8 ,  lowerCAmelCase_			:		Optional[int]=3_0 ,  lowerCAmelCase_			:		str=4_0_0 ,  lowerCAmelCase_			:		Tuple=True ,  lowerCAmelCase_			:		int=None ,  lowerCAmelCase_			:		Dict=True ,  )							->						Union[str, Any]:
     __lowerCAmelCase							     =     size if size is not None else {'height': 1_8, 'width': 1_8}
     __lowerCAmelCase							     =     parent
     __lowerCAmelCase							     =     batch_size
     __lowerCAmelCase							     =     num_channels
     __lowerCAmelCase							     =     image_size
     __lowerCAmelCase							     =     min_resolution
     __lowerCAmelCase							     =     max_resolution
     __lowerCAmelCase							     =     do_resize
     __lowerCAmelCase							     =     size
     __lowerCAmelCase							     =     do_normalize






  def     lowercase		(    self			:		List[str]					)							->						Dict:
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                 [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
             ]					),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }





@require_torch
@require_vision
class     _UpperCAmelCase   (   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			ImageGPTImageProcessor if is_vision_available() else None






  def     lowercase		(    self			:		Union[str, Any]					)							->						str:
     __lowerCAmelCase							     =     ImageGPTImageProcessingTester(self					)






  @property
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     return self.image_processor_tester.prepare_image_processor_dict()






  def     lowercase		(    self			:		Optional[int]					)							->						Tuple:
     __lowerCAmelCase							     =     self.image_processing_class(**self.image_processor_dict					)
     self.assertTrue(hasattr(lowerCAmelCase_ ,  'clusters'					)					)
     self.assertTrue(hasattr(lowerCAmelCase_ ,  'do_resize'					)					)
     self.assertTrue(hasattr(lowerCAmelCase_ ,  'size'					)					)
     self.assertTrue(hasattr(lowerCAmelCase_ ,  'do_normalize'					)					)






  def     lowercase		(    self			:		Union[str, Any]					)							->						int:
     __lowerCAmelCase							     =     self.image_processing_class.from_dict(self.image_processor_dict					)
     self.assertEqual(image_processor.size ,  {'height': 1_8, 'width': 1_8}					)

     __lowerCAmelCase							     =     self.image_processing_class.from_dict(self.image_processor_dict ,  size=4_2					)
     self.assertEqual(image_processor.size ,  {'height': 4_2, 'width': 4_2}					)






  def     lowercase		(    self			:		Any					)							->						Any:
     __lowerCAmelCase							     =     self.image_processing_class(**self.image_processor_dict					)
     __lowerCAmelCase							     =     json.loads(image_processor.to_json_string()					)
     for key, value in self.image_processor_dict.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(lowerCAmelCase_ ,  obj[key]					)					)
        else:
           self.assertEqual(obj[key] ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		List[Any]					)							->						str:
     __lowerCAmelCase							     =     self.image_processing_class(**self.image_processor_dict					)

     with tempfile.TemporaryDirectory() as tmpdirname:
        __lowerCAmelCase							     =     os.path.join(lowerCAmelCase_ ,  'image_processor.json'					)
        image_processor_first.to_json_file(lowerCAmelCase_					)
        __lowerCAmelCase							     =     self.image_processing_class.from_json_file(lowerCAmelCase_					).to_dict()

     __lowerCAmelCase							     =     image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(lowerCAmelCase_ ,  image_processor_second[key]					)					)
        else:
           self.assertEqual(image_processor_first[key] ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		int					)							->						str:
     __lowerCAmelCase							     =     self.image_processing_class(**self.image_processor_dict					)

     with tempfile.TemporaryDirectory() as tmpdirname:
        image_processor_first.save_pretrained(lowerCAmelCase_					)
        __lowerCAmelCase							     =     self.image_processing_class.from_pretrained(lowerCAmelCase_					).to_dict()

     __lowerCAmelCase							     =     image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(lowerCAmelCase_ ,  image_processor_second[key]					)					)
        else:
           self.assertEqual(image_processor_first[key] ,  lowerCAmelCase_					)






  @unittest.skip('ImageGPT requires clusters at initialization'					)
  def     lowercase		(    self			:		Dict					)							->						Union[str, Any]:
     pass





def   a_   (			):
   __lowerCAmelCase							     =     load_dataset('hf-internal-testing/fixtures_image_utils',     split='test'     )

   __lowerCAmelCase							     =     Image.open(dataset[4]['file']     )
   __lowerCAmelCase							     =     Image.open(dataset[5]['file']     )

   __lowerCAmelCase							     =     [imagea, imagea]

   return images




@require_vision
@require_torch
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @slow
  def     lowercase		(    self			:		Optional[Any]					)							->						List[Any]:
     __lowerCAmelCase							     =     ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'					)

     __lowerCAmelCase							     =     prepare_images()

     # test non-batched
     __lowerCAmelCase							     =     image_processing(images[0] ,  return_tensors='pt'					)

     self.assertIsInstance(encoding.input_ids ,  torch.LongTensor					)
     self.assertEqual(encoding.input_ids.shape ,  (1, 1_0_2_4)					)

     __lowerCAmelCase							     =     [3_0_6, 1_9_1, 1_9_1]
     self.assertEqual(encoding.input_ids[0, :3].tolist() ,  lowerCAmelCase_					)

     # test batched
     __lowerCAmelCase							     =     image_processing(lowerCAmelCase_ ,  return_tensors='pt'					)

     self.assertIsInstance(encoding.input_ids ,  torch.LongTensor					)
     self.assertEqual(encoding.input_ids.shape ,  (2, 1_0_2_4)					)

     __lowerCAmelCase							     =     [3_0_3, 1_3, 1_3]
     self.assertEqual(encoding.input_ids[1, -3:].tolist() ,  lowerCAmelCase_					)








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


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

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   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     ):
      __lowerCAmelCase							     =     [image]

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

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

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return mask
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [mask]

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




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			42






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[Any]					)							->						Optional[int]:
     super().__init__()
     self.register_modules(unet=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)






  @torch.no_grad()
  def __call__(    self			:		Dict ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		int = 2_5_0 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		Optional[Union[torch.Generator, List[torch.Generator]]] = None ,  lowerCAmelCase_			:		Optional[str] = "pil" ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[ImagePipelineOutput, Tuple]:
     __lowerCAmelCase							     =     image

     __lowerCAmelCase							     =     _preprocess_image(lowerCAmelCase_					)
     __lowerCAmelCase							     =     original_image.to(device=self.device ,  dtype=self.unet.dtype					)
     __lowerCAmelCase							     =     _preprocess_mask(lowerCAmelCase_					)
     __lowerCAmelCase							     =     mask_image.to(device=self.device ,  dtype=self.unet.dtype					)

     __lowerCAmelCase							     =     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."""					)

     __lowerCAmelCase							     =     original_image.shape
     __lowerCAmelCase							     =     randn_tensor(lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  device=self.device ,  dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.device					)
     __lowerCAmelCase							     =     eta

     __lowerCAmelCase							     =     self.scheduler.timesteps[0] + 1
     __lowerCAmelCase							     =     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
           __lowerCAmelCase							     =     self.unet(lowerCAmelCase_ ,  lowerCAmelCase_					).sample
           # compute previous image: x_t -> x_t-1
           __lowerCAmelCase							     =     self.scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					).prev_sample

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

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_					)

