



'''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 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 @property
 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    torch.manual_seed(0  )
    A_  :		Dict						=					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       UpperCAmelCase_       (      self  )    ->    Any:
    A_  :		Optional[int]						=					"""cpu"""
    A_  :		List[str]						=					self.dummy_uncond_unet
    A_  :		int						=					DDPMScheduler()

    A_  :		List[str]						=					DDPMPipeline(unet=_lowerCamelCase					,    scheduler=_lowerCamelCase  )
    ddpm.to(_lowerCamelCase  )
    ddpm.set_progress_bar_config(disable=_lowerCamelCase  )

    A_  :		List[Any]						=					torch.Generator(device=_lowerCamelCase  ).manual_seed(0  )
    A_  :		Dict						=					ddpm(generator=_lowerCamelCase					,    num_inference_steps=2					,    output_type="""numpy"""  ).images

    A_  :		List[str]						=					torch.Generator(device=_lowerCamelCase  ).manual_seed(0  )
    A_  :		int						=					ddpm(generator=_lowerCamelCase					,    num_inference_steps=2					,    output_type="""numpy"""					,    return_dict=_lowerCamelCase  )[0]

    A_  :		List[Any]						=					image[0, -3:, -3:, -1]
    A_  :		int						=					image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    A_  :		List[str]						=					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       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		int						=					self.dummy_uncond_unet
    A_  :		List[str]						=					DDPMScheduler(prediction_type="""sample"""  )

    A_  :		int						=					DDPMPipeline(unet=_lowerCamelCase					,    scheduler=_lowerCamelCase  )
    ddpm.to(_lowerCamelCase  )
    ddpm.set_progress_bar_config(disable=_lowerCamelCase  )

    A_  :		Optional[int]						=					torch.manual_seed(0  )
    A_  :		Union[str, Any]						=					ddpm(generator=_lowerCamelCase					,    num_inference_steps=2					,    output_type="""numpy"""  ).images

    A_  :		int						=					torch.manual_seed(0  )
    A_  :		Union[str, Any]						=					ddpm(generator=_lowerCamelCase					,    num_inference_steps=2					,    output_type="""numpy"""  )[0]

    A_  :		int						=					image[0, -3:, -3:, -1]
    A_  :		Optional[Any]						=					image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    A_  :		List[str]						=					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 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 def       UpperCAmelCase_       (      self  )    ->    Any:
    A_  :		Any						=					"""google/ddpm-cifar10-32"""

    A_  :		int						=					UNetaDModel.from_pretrained(_lowerCamelCase  )
    A_  :		str						=					DDPMScheduler.from_pretrained(_lowerCamelCase  )

    A_  :		Union[str, Any]						=					DDPMPipeline(unet=_lowerCamelCase					,    scheduler=_lowerCamelCase  )
    ddpm.to(_lowerCamelCase  )
    ddpm.set_progress_bar_config(disable=_lowerCamelCase  )

    A_  :		int						=					torch.manual_seed(0  )
    A_  :		Optional[Any]						=					ddpm(generator=_lowerCamelCase					,    output_type="""numpy"""  ).images

    A_  :		Optional[Any]						=					image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    A_  :		int						=					np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]  )
    assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2





'''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 ViltImageProcessor


class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=7					,    _lowerCamelCase=3					,    _lowerCamelCase=18					,    _lowerCamelCase=30					,    _lowerCamelCase=400					,    _lowerCamelCase=True					,    _lowerCamelCase=None					,    _lowerCamelCase=2					,    _lowerCamelCase=True					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    )    ->    Tuple:
    A_  :		Dict						=					size if size is not None else {"""shortest_edge""": 30}
    A_  :		List[Any]						=					parent
    A_  :		Optional[Any]						=					batch_size
    A_  :		Tuple						=					num_channels
    A_  :		Any						=					image_size
    A_  :		str						=					min_resolution
    A_  :		str						=					max_resolution
    A_  :		Any						=					do_resize
    A_  :		List[Any]						=					size
    A_  :		Union[str, Any]						=					size_divisor
    A_  :		Dict						=					do_normalize
    A_  :		Dict						=					image_mean
    A_  :		Optional[Any]						=					image_std







 def       UpperCAmelCase_       (      self  )    ->    Optional[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,
        "size_divisor": self.size_divisor,
    }







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=False  )    ->    Union[str, Any]:
    if not batched:
       A_  :		List[str]						=					self.size["""shortest_edge"""]
       A_  :		str						=					image_inputs[0]
       if isinstance(_lowerCamelCase					,    Image.Image  ):
          A_						,  A_  :		Dict						=					image.size
       else:
          A_						,  A_  :		Dict						=					image.shape[1], image.shape[2]
       A_  :		Dict						=					size / min(_lowerCamelCase					,    _lowerCamelCase  )
       if h < w:
          A_						,  A_  :		Any						=					size, scale * w
       else:
          A_						,  A_  :		List[str]						=					scale * h, size

       A_  :		Dict						=					int((1333 / 800) * size  )
       if max(_lowerCamelCase					,    _lowerCamelCase  ) > max_size:
          A_  :		Dict						=					max_size / max(_lowerCamelCase					,    _lowerCamelCase  )
          A_  :		str						=					newh * scale
          A_  :		Tuple						=					neww * scale

       A_						,  A_  :		str						=					int(newh + 0.5  ), int(neww + 0.5  )
       A_						,  A_  :		Union[str, Any]						=					(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

    else:
       A_  :		Union[str, Any]						=					[]
       for image in image_inputs:
          A_						,  A_  :		Any						=					self.get_expected_values([image]  )
          expected_values.append((expected_height, expected_width)  )
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[0]  )[0]
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[1]  )[1]

    return expected_height, expected_width


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


 """simple docstring"""

 lowerCamelCase									=     ViltImageProcessor if is_vision_available() else None







 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Optional[int]						=					ViltImageProcessingTester(self  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    return self.image_processor_tester.prepare_image_processor_dict()







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_mean"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_std"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_normalize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_resize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size_divisor"""  )  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 30}  )

    A_  :		Union[str, Any]						=					self.image_processing_class.from_dict(self.image_processor_dict					,    size=42  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 42}  )







 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    # Initialize image_processing
    A_  :		Any						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    A_  :		Any						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    Image.Image  )

    # Test not batched input
    A_  :		Tuple						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[Any]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    str:
    # Initialize image_processing
    A_  :		Tuple						=					self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    A_  :		List[str]						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    numpify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    np.ndarray  )

    # Test not batched input
    A_  :		Any						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		int						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[str]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    # Initialize image_processing
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    A_  :		str						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    torchify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    torch.Tensor  )

    # Test not batched input
    A_  :		Optional[int]						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		List[str]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		Any						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )
