

import unittest

from transformers.image_processing_utils import get_size_dict



class 					__snake_case     (			unittest.TestCase				):

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  {'wrong_key': 224}
      with self.assertRaises(_lowercase):
          get_size_dict(_lowercase)

      SCREAMING_SNAKE_CASE_													=  {'height': 224}
      with self.assertRaises(_lowercase):
          get_size_dict(_lowercase)

      SCREAMING_SNAKE_CASE_													=  {'width': 224, 'shortest_edge': 224}
      with self.assertRaises(_lowercase):
          get_size_dict(_lowercase)

      # Test a dict with the correct keys is returned as is
      SCREAMING_SNAKE_CASE_													=  {'height': 224, 'width': 224}
      SCREAMING_SNAKE_CASE_													=  get_size_dict(_lowercase)
      self.assertEqual(_lowercase  ,						_lowercase)

      SCREAMING_SNAKE_CASE_													=  {'shortest_edge': 224}
      SCREAMING_SNAKE_CASE_													=  get_size_dict(_lowercase)
      self.assertEqual(_lowercase  ,						{'shortest_edge': 224})

      SCREAMING_SNAKE_CASE_													=  {'longest_edge': 224, 'shortest_edge': 224}
      SCREAMING_SNAKE_CASE_													=  get_size_dict(_lowercase)
      self.assertEqual(_lowercase  ,						{'longest_edge': 224, 'shortest_edge': 224})

      # Test a single int value which  represents (size, size)
      SCREAMING_SNAKE_CASE_													=  get_size_dict(224)
      self.assertEqual(_lowercase  ,						{'height': 224, 'width': 224})

      # Test a single int value which represents the shortest edge
      SCREAMING_SNAKE_CASE_													=  get_size_dict(224  ,						default_to_square=_lowercase)
      self.assertEqual(_lowercase  ,						{'shortest_edge': 224})

      # Test a tuple of ints which represents (height, width)
      SCREAMING_SNAKE_CASE_													=  get_size_dict((150, 200))
      self.assertEqual(_lowercase  ,						{'height': 150, 'width': 200})

      # Test a tuple of ints which represents (width, height)
      SCREAMING_SNAKE_CASE_													=  get_size_dict((150, 200)  ,						height_width_order=_lowercase)
      self.assertEqual(_lowercase  ,						{'height': 200, 'width': 150})

      # Test an int representing the shortest edge and max_size which represents the longest edge
      SCREAMING_SNAKE_CASE_													=  get_size_dict(224  ,						max_size=256  ,						default_to_square=_lowercase)
      self.assertEqual(_lowercase  ,						{'shortest_edge': 224, 'longest_edge': 256})

      # Test int with default_to_square=True and max_size fails
      with self.assertRaises(_lowercase):
          get_size_dict(224  ,						max_size=256  ,						default_to_square=_lowercase)









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







  @torch.no_grad()
  def __call__(			self  :Union[str, Any]    ,							_lowercase  :int = 1    ,							_lowercase  :int = 50    ,							_lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							_lowercase  :Optional[str] = "pil"    ,							_lowercase  :bool = True    ,							**_lowercase  :List[Any]    ,							):






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






