





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






      def __init__(      self      :      Optional[Any]		,   _A      :      List[str]		,   _A      :      int=7		,   _A      :      Tuple=3		,   _A      :      Any=18		,   _A      :      str=30		,   _A      :      Union[str, Any]=400		,   _A      :      List[str]=True		,   _A      :      int=None		,   _A      :      List[str]=True		,   _A      :      Union[str, Any]=False		,   _A      :      Dict=True		,   _A      :      Tuple=True		,   _A      :      Optional[int]=[0.5, 0.5, 0.5]		,   _A      :      Dict=[0.5, 0.5, 0.5]		,   )							->    int:







       """simple docstring"""


       snake_case_		:			Optional[Any]											=	parent
       snake_case_		:			Union[str, Any]											=	batch_size
       snake_case_		:			str											=	num_channels
       snake_case_		:			Any											=	image_size
       snake_case_		:			int											=	min_resolution
       snake_case_		:			Tuple											=	max_resolution
       snake_case_		:			Tuple											=	do_resize
       snake_case_		:			Tuple											=	size if size is not None else {'height': 18, 'width': 20}
       snake_case_		:			Any											=	do_thumbnail
       snake_case_		:			int											=	do_align_axis
       snake_case_		:			List[str]											=	do_pad
       snake_case_		:			List[Any]											=	do_normalize
       snake_case_		:			str											=	image_mean
       snake_case_		:			Optional[int]											=	image_std






      def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Dict:







       """simple docstring"""


       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_						(					UpperCAmelCase_       ,				unittest.TestCase					):
      __magic_name__:			Union[str, Any]       =       DonutImageProcessor if is_vision_available() else None






      def UpperCAmelCase_				(      self      :      str	)							->    List[Any]:







       """simple docstring"""


       snake_case_		:			Optional[int]											=	DonutImageProcessingTester(self	)






      @property
      def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    str:







       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()






      def UpperCAmelCase_				(      self      :      int	)							->    int:







       """simple docstring"""


       snake_case_		:			int											=	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_thumbnail'	)	)
       self.assertTrue(hasattr(_snake_case		,   'do_align_long_axis'	)	)
       self.assertTrue(hasattr(_snake_case		,   'do_pad'	)	)
       self.assertTrue(hasattr(_snake_case		,   'do_normalize'	)	)
       self.assertTrue(hasattr(_snake_case		,   'image_mean'	)	)
       self.assertTrue(hasattr(_snake_case		,   'image_std'	)	)






      def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







       """simple docstring"""


       snake_case_		:			List[Any]											=	self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size		,   {'height': 18, 'width': 20}	)

       snake_case_		:			Any											=	self.image_processing_class.from_dict(self.image_processor_dict		,   size=42	)
       self.assertEqual(image_processor.size		,   {'height': 42, 'width': 42}	)

       # Previous config had dimensions in (width, height) order
       snake_case_		:			int											=	self.image_processing_class.from_dict(self.image_processor_dict		,   size=(42, 84)	)
       self.assertEqual(image_processor.size		,   {'height': 84, 'width': 42}	)






      def UpperCAmelCase_				(      self      :      Tuple	)							->    str:







       """simple docstring"""


       pass






      @is_flaky()
      def UpperCAmelCase_				(      self      :      int	)							->    List[Any]:







       """simple docstring"""


       snake_case_		:			Optional[Any]											=	self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       snake_case_		:			Union[str, Any]											=	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_		:			Union[str, Any]											=	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
       snake_case_		:			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.size['height'],
               self.image_processor_tester.size['width'],
           )		,   )






      @is_flaky()
      def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    List[Any]:







       """simple docstring"""


       snake_case_		:			Optional[Any]											=	self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       snake_case_		:			Tuple											=	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_		:			Optional[Any]											=	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
       snake_case_		:			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.size['height'],
               self.image_processor_tester.size['width'],
           )		,   )






      @is_flaky()
      def UpperCAmelCase_				(      self      :      Dict	)							->    Optional[int]:







       """simple docstring"""


       snake_case_		:			Dict											=	self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       snake_case_		:			Tuple											=	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_		:			Optional[int]											=	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
       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.size['height'],
               self.image_processor_tester.size['width'],
           )		,   )








import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						@property
						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			List[Any]											=	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      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.dummy_uncond_unet
							snake_case_		:			Optional[Any]											=	ScoreSdeVeScheduler()

							snake_case_		:			Tuple											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
							snake_case_		:			Tuple											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			int											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A		,   return_dict=_A	)[
							    0
							]

							snake_case_		:			List[str]											=	image[0, -3:, -3:, -1]
							snake_case_		:			Dict											=	image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:			List[Any]											=	np.array([0.0, 1.0, 0.0, 0.0, 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 SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Dict	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	'google/ncsnpp-church-256'
							snake_case_		:			List[Any]											=	UNetaDModel.from_pretrained(_A	)

							snake_case_		:			str											=	ScoreSdeVeScheduler.from_pretrained(_A	)

							snake_case_		:			Optional[Any]											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Any											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=10		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							snake_case_		:			Dict											=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

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

