


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        lowerCamelCase__	(							unittest.TestCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=7      ,						snake_case=3      ,						snake_case=18      ,						snake_case=30      ,						snake_case=400      ,						snake_case=True      ,						snake_case=None      ,						snake_case=True      ,						snake_case=False      ,						snake_case=True      ,						snake_case=True      ,						snake_case=[0.5, 0.5, 0.5]      ,						snake_case=[0.5, 0.5, 0.5]      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       num_channels
						UpperCamelCase__					=       image_size
						UpperCamelCase__					=       min_resolution
						UpperCamelCase__					=       max_resolution
						UpperCamelCase__					=       do_resize
						UpperCamelCase__					=       size if size is not None else {"height": 18, "width": 20}
						UpperCamelCase__					=       do_thumbnail
						UpperCamelCase__					=       do_align_axis
						UpperCamelCase__					=       do_pad
						UpperCamelCase__					=       do_normalize
						UpperCamelCase__					=       image_mean
						UpperCamelCase__					=       image_std







			def 				snake_case__				(							self     ):

						'''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        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Tuple    =		DonutImageProcessor if is_vision_available() else None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       DonutImageProcessingTester(self     )




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       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 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.image_processing_class.from_dict(self.image_processor_dict     )
						self.assertEqual(image_processor.size      ,						{"height": 18, "width": 20}     )

						UpperCamelCase__					=       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
						UpperCamelCase__					=       self.image_processing_class.from_dict(self.image_processor_dict      ,						size=(42, 84)     )
						self.assertEqual(image_processor.size      ,						{"height": 84, "width": 42}     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			@is_flaky()
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.image_processing_class(**self.image_processor_dict     )
						# create random PIL images
						UpperCamelCase__					=       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
						UpperCamelCase__					=       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
						UpperCamelCase__					=       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 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.image_processing_class(**self.image_processor_dict     )
						# create random numpy tensors
						UpperCamelCase__					=       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
						UpperCamelCase__					=       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
						UpperCamelCase__					=       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 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.image_processing_class(**self.image_processor_dict     )
						# create random PyTorch tensors
						UpperCamelCase__					=       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
						UpperCamelCase__					=       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
						UpperCamelCase__					=       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

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


