

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 ViTImageProcessor



class 						a_			(				unittest.TestCase	):



      """simple docstring"""







      def __init__( self	:				Dict     ,snake_case	:				Dict     ,snake_case	:				Optional[Any]=7     ,snake_case	:				int=3     ,snake_case	:				str=18     ,snake_case	:				Optional[int]=30     ,snake_case	:				Union[str, Any]=400     ,snake_case	:				Union[str, Any]=True     ,snake_case	:				Optional[Any]=None     ,snake_case	:				Optional[int]=True     ,snake_case	:				List[str]=[0.5, 0.5, 0.5]     ,snake_case	:				Any=[0.5, 0.5, 0.5]     ,):
           SCREAMING_SNAKE_CASE 		=size if size is not None else {'height': 18, 'width': 18}
           SCREAMING_SNAKE_CASE 		=parent
           SCREAMING_SNAKE_CASE 		=batch_size
           SCREAMING_SNAKE_CASE 		=num_channels
           SCREAMING_SNAKE_CASE 		=image_size
           SCREAMING_SNAKE_CASE 		=min_resolution
           SCREAMING_SNAKE_CASE 		=max_resolution
           SCREAMING_SNAKE_CASE 		=do_resize
           SCREAMING_SNAKE_CASE 		=size
           SCREAMING_SNAKE_CASE 		=do_normalize
           SCREAMING_SNAKE_CASE 		=image_mean
           SCREAMING_SNAKE_CASE 		=image_std


      def 			_lowerCAmelCase	( self	:				List[Any]							):
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
           }







@require_torch
@require_vision
class 						a_			(				__A					,     unittest.TestCase	):



      """simple docstring"""







      __UpperCAmelCase     						= ViTImageProcessor if is_vision_available() else None
      def 			_lowerCAmelCase	( self	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=ViTImageProcessingTester(self							)
      @property
      def 			_lowerCAmelCase	( self	:				List[Any]							):
           return self.image_processor_tester.prepare_image_processor_dict()
      def 			_lowerCAmelCase	( self	:				Any							):
           SCREAMING_SNAKE_CASE 		=self.image_processing_class(**self.image_processor_dict							)
           self.assertTrue(hasattr(A_     ,'image_mean'							)							)
           self.assertTrue(hasattr(A_     ,'image_std'							)							)
           self.assertTrue(hasattr(A_     ,'do_normalize'							)							)
           self.assertTrue(hasattr(A_     ,'do_resize'							)							)
           self.assertTrue(hasattr(A_     ,'size'							)							)
      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE 		=self.image_processing_class.from_dict(self.image_processor_dict							)
           self.assertEqual(image_processor.size     ,{'height': 18, 'width': 18}							)

           SCREAMING_SNAKE_CASE 		=self.image_processing_class.from_dict(self.image_processor_dict     ,size=42							)
           self.assertEqual(image_processor.size     ,{'height': 42, 'width': 42}							)
      def 			_lowerCAmelCase	( self	:				Tuple							):
           pass
      def 			_lowerCAmelCase	( self	:				Tuple							):
           SCREAMING_SNAKE_CASE 		=self.image_processing_class(**self.image_processor_dict							)
           # create random PIL images
           SCREAMING_SNAKE_CASE 		=prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A_							)
           for image in image_inputs:
                self.assertIsInstance(A_     ,Image.Image							)

           # Test not batched input
           SCREAMING_SNAKE_CASE 		=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.size['height'],
                   self.image_processor_tester.size['width'],
               )     ,)

           # Test batched
           SCREAMING_SNAKE_CASE 		=image_processing(A_     ,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.size['height'],
                   self.image_processor_tester.size['width'],
               )     ,)
      def 			_lowerCAmelCase	( self	:				Optional[int]							):
           SCREAMING_SNAKE_CASE 		=self.image_processing_class(**self.image_processor_dict							)
           # create random numpy tensors
           SCREAMING_SNAKE_CASE 		=prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A_     ,numpify=A_							)
           for image in image_inputs:
                self.assertIsInstance(A_     ,np.ndarray							)

           # Test not batched input
           SCREAMING_SNAKE_CASE 		=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.size['height'],
                   self.image_processor_tester.size['width'],
               )     ,)

           # Test batched
           SCREAMING_SNAKE_CASE 		=image_processing(A_     ,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.size['height'],
                   self.image_processor_tester.size['width'],
               )     ,)


      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE 		=self.image_processing_class(**self.image_processor_dict							)
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE 		=prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A_     ,torchify=A_							)
           for image in image_inputs:
                self.assertIsInstance(A_     ,torch.Tensor							)

           # Test not batched input
           SCREAMING_SNAKE_CASE 		=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.size['height'],
                   self.image_processor_tester.size['width'],
               )     ,)

           # Test batched
           SCREAMING_SNAKE_CASE 		=image_processing(A_     ,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.size['height'],
                   self.image_processor_tester.size['width'],
               )     ,)






"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			UNetaDModel(
          block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,							)
      return model




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
