






'''simple docstring'''

import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = 32      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = 1 / 2_55      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]      ,  __lowerCAmelCase = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=30      ,  __lowerCAmelCase=4_00      ,  __lowerCAmelCase=3      ,  ):
       UpperCamelCase_					: Any            =				parent
       UpperCamelCase_					: Dict            =				do_resize
       UpperCamelCase_					: Optional[int]            =				size if size is not None else {"""shortest_edge""": 2_88}
       UpperCamelCase_					: List[Any]            =				size_divisor
       UpperCamelCase_					: Any            =				do_rescale
       UpperCamelCase_					: Union[str, Any]            =				rescale_factor
       UpperCamelCase_					: int            =				do_normalize
       UpperCamelCase_					: List[Any]            =				do_center_crop
       UpperCamelCase_					: str            =				image_mean
       UpperCamelCase_					: Union[str, Any]            =				image_std
       UpperCamelCase_					: Optional[int]            =				do_pad
       UpperCamelCase_					: Tuple            =				batch_size
       UpperCamelCase_					: int            =				num_channels
       UpperCamelCase_					: Any            =				min_resolution
       UpperCamelCase_					: List[str]            =				max_resolution
 def 	_UpperCAmelCase							(				self			):
       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			):
       if not batched:
             UpperCamelCase_					: int            =				self.size["""shortest_edge"""]
             UpperCamelCase_					: Any            =				image_inputs[0]
             if isinstance(__lowerCAmelCase      ,  Image.Image			):
                   UpperCamelCase_     ,      UpperCamelCase_					: Optional[int]            =				image.size
             else:
                   UpperCamelCase_     ,      UpperCamelCase_					: int            =				image.shape[1], image.shape[2]
             UpperCamelCase_					: Tuple            =				size / min(__lowerCAmelCase      ,  __lowerCAmelCase			)
             if h < w:
                   UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				size, scale * w
             else:
                   UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				scale * h, size

             UpperCamelCase_					: Any            =				int((13_33 / 8_00) * size			)
             if max(__lowerCAmelCase      ,  __lowerCAmelCase			) > max_size:
                   UpperCamelCase_					: Any            =				max_size / max(__lowerCAmelCase      ,  __lowerCAmelCase			)
                   UpperCamelCase_					: Tuple            =				newh * scale
                   UpperCamelCase_					: List[str]            =				neww * scale

             UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				int(newh + 0.5			), int(neww + 0.5			)
             UpperCamelCase_     ,      UpperCamelCase_					: int            =				(
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

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

       return expected_height, expected_width






@require_torch
@require_vision
class      A     (   SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    Tuple			  =    BridgeTowerImageProcessor if is_vision_available() else None
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				BridgeTowerImageProcessingTester(self			)
 @property
 def 	_UpperCAmelCase							(				self			):
       return self.image_processor_tester.prepare_image_processor_dict()
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				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			):
       pass
 def 	_UpperCAmelCase							(				self			):
       # Initialize image processor
       UpperCamelCase_					: Optional[Any]            =				self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       UpperCamelCase_					: Tuple            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowerCAmelCase			)
       for image in image_inputs:
             self.assertIsInstance(__lowerCAmelCase      ,  Image.Image			)

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

       UpperCamelCase_     ,      UpperCamelCase_					: Optional[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
       UpperCamelCase_					: int            =				image_processing(__lowerCAmelCase      ,  return_tensors="""pt"""			).pixel_values

       UpperCamelCase_     ,      UpperCamelCase_					: 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			):
       # Initialize image processor
       UpperCamelCase_					: int            =				self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       UpperCamelCase_					: Union[str, Any]            =				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
       UpperCamelCase_					: List[str]            =				image_processing(image_inputs[0]      ,  return_tensors="""pt"""			).pixel_values

       UpperCamelCase_     ,      UpperCamelCase_					: Optional[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
       UpperCamelCase_					: Optional[Any]            =				image_processing(__lowerCAmelCase      ,  return_tensors="""pt"""			).pixel_values

       UpperCamelCase_     ,      UpperCamelCase_					: Tuple            =				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			):
       # Initialize image processor
       UpperCamelCase_					: List[Any]            =				self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       UpperCamelCase_					: Dict            =				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
       UpperCamelCase_					: Union[str, Any]            =				image_processing(image_inputs[0]      ,  return_tensors="""pt"""			).pixel_values

       UpperCamelCase_     ,      UpperCamelCase_					: 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
       UpperCamelCase_					: Optional[int]            =				image_processing(__lowerCAmelCase      ,  return_tensors="""pt"""			).pixel_values

       UpperCamelCase_     ,      UpperCamelCase_					: str            =				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,
           )      ,  )














'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: int            =				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			):
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_uncond_unet
       UpperCamelCase_					: List[str]            =				PNDMScheduler()

       UpperCamelCase_					: Dict            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: int            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Optional[int]            =				np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

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






@slow
@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				"""google/ddpm-cifar10-32"""

       UpperCamelCase_					: List[str]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: int            =				PNDMScheduler()

       UpperCamelCase_					: Any            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Tuple            =				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Union[str, Any]            =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

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






