
'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase            = {
    "configuration_swin2sr": ["SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swin2SRConfig"],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase            = [
						    "SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "Swin2SRForImageSuperResolution",
						    "Swin2SRModel",
						    "Swin2SRPreTrainedModel",
						]


try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase            = ["Swin2SRImageProcessor"]


if TYPE_CHECKING:
						from .configuration_swinasr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinaSRConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swinasr import (
												    SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwinaSRForImageSuperResolution,
												    SwinaSRModel,
												    SwinaSRPreTrainedModel,
												)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_swinasr import SwinaSRImageProcessor


else:
						import sys

						UpperCAmelCase            = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






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 EfficientNetImageProcessor





class    __lowerCAmelCase					(	unittest.TestCase			):




				def __init__(				self		,							_snake_case		,							_snake_case=13		,							_snake_case=3		,							_snake_case=18		,							_snake_case=30		,							_snake_case=400		,							_snake_case=True		,							_snake_case=None		,							_snake_case=True		,							_snake_case=[0.5, 0.5, 0.5]		,							_snake_case=[0.5, 0.5, 0.5]		,							):



									"""simple docstring"""





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




				def 				snake_case  (				self						):



									"""simple docstring"""





									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}





@require_torch
@require_vision
class    __lowerCAmelCase					(	lowerCamelCase__			,		unittest.TestCase			):
				__lowerCamelCase					=							EfficientNetImageProcessor if is_vision_available() else None




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       EfficientNetImageProcessorTester(self						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_snake_case		,							"""image_mean"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""image_std"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_normalize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_resize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""size"""						)						)




				def 				snake_case  (				self						):



									"""simple docstring"""





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

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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"""],
									    )		,							)



