
"""simple docstring"""
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 OwlViTImageProcessor






class   _UpperCAmelCase					(       unittest.TestCase):
    def __init__(     self    ,     snake_case_    ,     snake_case_=7    ,     snake_case_=3    ,     snake_case_=18    ,     snake_case_=30    ,     snake_case_=4_00    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=[0.48145466, 0.4578275, 0.40821073]    ,     snake_case_=[0.26862954, 0.26130258, 0.27577711]    ,     snake_case_=True    ,     ):
      _snake_case  :    str            = parent
      _snake_case  :    Dict            = batch_size
      _snake_case  :    List[Any]            = num_channels
      _snake_case  :    int            = image_size
      _snake_case  :    Optional[int]            = min_resolution
      _snake_case  :    Optional[Any]            = max_resolution
      _snake_case  :    Tuple            = do_resize
      _snake_case  :    int            = size if size is not None else {"height": 18, "width": 18}
      _snake_case  :    Dict            = do_center_crop
      _snake_case  :    Optional[Any]            = crop_size if crop_size is not None else {"height": 18, "width": 18}
      _snake_case  :    Union[str, Any]            = do_normalize
      _snake_case  :    Dict            = image_mean
      _snake_case  :    Tuple            = image_std
      _snake_case  :    Any            = do_convert_rgb


    def        lowerCamelCase__						(     self  ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }






@require_torch
@require_vision
class   _UpperCAmelCase					(       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       OwlViTImageProcessor if is_vision_available() else None
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = OwlViTImageProcessingTester(self  )
    @property
    def        lowerCamelCase__						(     self  ):
      return self.image_processor_tester.prepare_image_processor_dict()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(snake_case_    ,     "do_resize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "size"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_center_crop"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "center_crop"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_normalize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_mean"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_std"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_convert_rgb"  )  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.image_processing_class.from_dict(self.image_processor_dict  )
      self.assertEqual(image_processor.size    ,     {"height": 18, "width": 18}  )
      self.assertEqual(image_processor.crop_size    ,     {"height": 18, "width": 18}  )

      _snake_case  :    str            = self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42    ,     crop_size=84  )
      self.assertEqual(image_processor.size    ,     {"height": 42, "width": 42}  )
      self.assertEqual(image_processor.crop_size    ,     {"height": 84, "width": 84}  )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    str            = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _snake_case  :    Tuple            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_  )

      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     Image.Image  )

      # Test not batched input
      _snake_case  :    Dict            = 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
      _snake_case  :    Dict            = image_processing(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        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    Union[str, Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _snake_case  :    Union[str, Any]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     numpify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     np.ndarray  )

      # Test not batched input
      _snake_case  :    Tuple            = 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
      _snake_case  :    Optional[int]            = image_processing(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        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    str            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    Optional[int]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     torchify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     torch.Tensor  )

      # Test not batched input
      _snake_case  :    Tuple            = 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
      _snake_case  :    Optional[Any]            = image_processing(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"],
          )    ,     )








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 SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@property
					def        __magic_name__			(			self						):
										torch.manual_seed(0						)
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler()

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						,	return_dict=_lowerCAmelCase						)[0]

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array(
										    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-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        __magic_name__			(			self						):
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler(prediction_type="sample"						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						)[0]

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							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 SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"google/ddpm-cifar10-32"

										lowerCamelCase__      =							UNetaDModel.from_pretrained(_lowerCAmelCase						)
										lowerCamelCase__      =							DDPMScheduler.from_pretrained(_lowerCAmelCase						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	output_type="numpy"						).images

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							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

