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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class 					UpperCamelCase	(     unittest.TestCase      ):




					def __init__( self :					List[Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					List[Any]=7      ,_lowerCAmelCase :					Optional[Any]=3      ,_lowerCAmelCase :					Optional[Any]=18      ,_lowerCAmelCase :					str=30      ,_lowerCAmelCase :					str=400      ,_lowerCAmelCase :					Any=True      ,_lowerCAmelCase :					int=1 / 255      ,_lowerCAmelCase :					Dict=True      ,_lowerCAmelCase :					Tuple=8      ,):


							"""simple docstring"""




							__snake_case		       =     parent
							__snake_case		       =     batch_size
							__snake_case		       =     num_channels
							__snake_case		       =     image_size
							__snake_case		       =     min_resolution
							__snake_case		       =     max_resolution
							__snake_case		       =     do_rescale
							__snake_case		       =     rescale_factor
							__snake_case		       =     do_pad
							__snake_case		       =     pad_size




					def 							UpperCamelCase_						( self :					Tuple						):


							"""simple docstring"""




							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}







					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					List[Any]=False      ,_lowerCAmelCase :					Any=False      ,_lowerCAmelCase :					Tuple=False						):


							"""simple docstring"""




							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
									__snake_case		       =     []
									for i in range(self.batch_size						):
											image_inputs.append(
											    np.random.randint(
											        255      ,size=(self.num_channels, self.max_resolution, self.max_resolution)      ,dtype=np.uinta						)						)
							else:
									__snake_case		       =     []
									for i in range(self.batch_size						):
											__snake_case       ,       __snake_case		       =     np.random.choice(np.arange(self.min_resolution      ,self.max_resolution						)      ,2						)
											image_inputs.append(np.random.randint(255      ,size=(self.num_channels, width, height)      ,dtype=np.uinta						)						)

							if not numpify and not torchify:
									# PIL expects the channel dimension as last dimension
									__snake_case		       =     [Image.fromarray(np.moveaxis(_lowerCAmelCase      ,0      ,-1						)						) for x in image_inputs]

							if torchify:
									__snake_case		       =     [torch.from_numpy(_lowerCAmelCase						) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class 					UpperCamelCase	(     snake_case__			,      unittest.TestCase      ):
					__UpperCamelCase						=   SwinaSRImageProcessor if is_vision_available() else None




					def 							UpperCamelCase_						( self :					List[Any]						):


							"""simple docstring"""




							__snake_case		       =     SwinaSRImageProcessingTester(self						)




					@property
					def 							UpperCamelCase_						( self :					str						):


							"""simple docstring"""




							return self.image_processor_tester.prepare_image_processor_dict()




					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




							__snake_case		       =     self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(_lowerCAmelCase      ,"do_rescale"						)						)
							self.assertTrue(hasattr(_lowerCAmelCase      ,"rescale_factor"						)						)
							self.assertTrue(hasattr(_lowerCAmelCase      ,"do_pad"						)						)
							self.assertTrue(hasattr(_lowerCAmelCase      ,"pad_size"						)						)




					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




							pass




					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Optional[int]						):


							"""simple docstring"""




							__snake_case       ,       __snake_case		       =     get_image_size(_lowerCAmelCase						)
							__snake_case		       =     self.image_processor_tester.pad_size

							__snake_case		       =     (old_height // size + 1) * size - old_height
							__snake_case		       =     (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width




					def 							UpperCamelCase_						( self :					Optional[Any]						):


							"""simple docstring"""




							__snake_case		       =     self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							__snake_case		       =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase						)
							for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase      ,Image.Image						)

							# Test not batched input
							__snake_case		       =     image_processor(image_inputs[0]      ,return_tensors="pt"						).pixel_values
							__snake_case       ,       __snake_case		       =     self.calculate_expected_size(np.array(image_inputs[0]						)						)
							self.assertEqual(
							    encoded_images.shape      ,(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,)




					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




							__snake_case		       =     self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							__snake_case		       =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase      ,numpify=_lowerCAmelCase						)
							for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase      ,np.ndarray						)

							# Test not batched input
							__snake_case		       =     image_processor(image_inputs[0]      ,return_tensors="pt"						).pixel_values
							__snake_case       ,       __snake_case		       =     self.calculate_expected_size(image_inputs[0]						)
							self.assertEqual(
							    encoded_images.shape      ,(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,)







					def 							UpperCamelCase_						( self :					Any						):


							"""simple docstring"""




							__snake_case		       =     self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							__snake_case		       =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase      ,torchify=_lowerCAmelCase						)
							for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase      ,torch.Tensor						)

							# Test not batched input
							__snake_case		       =     image_processor(image_inputs[0]      ,return_tensors="pt"						).pixel_values
							__snake_case       ,       __snake_case		       =     self.calculate_expected_size(image_inputs[0]						)
							self.assertEqual(
							    encoded_images.shape      ,(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,)






from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any




class 					UpperCamelCase	:
					__UpperCamelCase						=   ("""_front""", """_back""", """_len""")




					@dataclass
					class 					UpperCamelCase	:
										__UpperCamelCase						=   None
										__UpperCamelCase						=   None
										__UpperCamelCase						=   None




					class 					UpperCamelCase	:
										__UpperCamelCase						=   ("""_cur""",)




										def __init__( self :					Dict      ,_lowerCAmelCase :					Deque._Node | None						):


												"""simple docstring"""




												__snake_case		       =     cur




										def __iter__( self :					Dict						):


												"""simple docstring"""




												return self







										def 							UpperCamelCase_						( self :					Optional[Any]						):


												"""simple docstring"""




												if self._cur is None:
														# finished iterating
														raise StopIteration
												__snake_case		       =     self._cur.val
												__snake_case		       =     self._cur.next_node

												return val




					def __init__( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any] | None = None						):


							"""simple docstring"""




							__snake_case		       =     None
							__snake_case		       =     None
							__snake_case		       =     0

							if iterable is not None:
									# append every value to the deque
									for val in iterable:
											self.append(_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any						):


							"""simple docstring"""




							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
							if self.is_empty():
									# front = back
									__snake_case		       =     __snake_case		       =     node
									__snake_case		       =     1
							else:
									# connect nodes
									__snake_case		       =     node
									__snake_case		       =     self._back
									__snake_case		       =     node  # assign new back to the new node

									self._len += 1

									# make sure there were no errors
									assert not self.is_empty(), "Error on appending value."




					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Any						):


							"""simple docstring"""




							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
							if self.is_empty():
									# front = back
									__snake_case		       =     __snake_case		       =     node
									__snake_case		       =     1
							else:
									# connect nodes
									__snake_case		       =     self._front
									__snake_case		       =     node
									__snake_case		       =     node  # assign new front to the new node

									self._len += 1

									# make sure there were no errors
									assert not self.is_empty(), "Error on appending value."




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Iterable[Any]						):


							"""simple docstring"""




							for val in iterable:
									self.append(_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any]						):


							"""simple docstring"""




							for val in iterable:
									self.appendleft(_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					List[Any]						):


							"""simple docstring"""




							assert not self.is_empty(), "Deque is empty."

							__snake_case		       =     self._back
							__snake_case		       =     self._back.prev_node  # set new back
							# drop the last node - python will deallocate memory automatically
							__snake_case		       =     None

							self._len -= 1

							return topop.val




					def 							UpperCamelCase_						( self :					List[str]						):


							"""simple docstring"""




							assert not self.is_empty(), "Deque is empty."

							__snake_case		       =     self._front
							__snake_case		       =     self._front.next_node  # set new front and drop the first node
							__snake_case		       =     None

							self._len -= 1

							return topop.val




					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							return self._front is None




					def __len__( self :					Tuple						):


							"""simple docstring"""




							return self._len




					def __eq__( self :					Union[str, Any]      ,_lowerCAmelCase :					object						):


							"""simple docstring"""




							if not isinstance(_lowerCAmelCase      ,_lowerCAmelCase						):
									return NotImplemented

							__snake_case		       =     self._front
							__snake_case		       =     other._front

							# if the length of the dequeues are not the same, they are not equal
							if len(self						) != len(_lowerCAmelCase						):
									return False

							while me is not None and oth is not None:
									# compare every value
									if me.val != oth.val:
											return False
									__snake_case		       =     me.next_node
									__snake_case		       =     oth.next_node

							return True




					def __iter__( self :					Tuple						):


							"""simple docstring"""




							return Deque._Iterator(self._front						)







					def __repr__( self :					List[Any]						):


							"""simple docstring"""




							__snake_case		       =     []
							__snake_case		       =     self._front
							while aux is not None:
									# append the values in a list to display
									values_list.append(aux.val						)
									__snake_case		       =     aux.next_node

							return F"""[{", ".join(repr(_lowerCAmelCase						) for val in values_list						)}]"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()





