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 MobileViTImageProcessor


class 						lowercase    (     unittest.TestCase     ):




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"shortest_edge": 20}
												SCREAMING_SNAKE_CASE    			=       crop_size if crop_size is not None else {"height": 18, "width": 18}
												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       min_resolution
												SCREAMING_SNAKE_CASE    			=       max_resolution
												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       do_center_crop
												SCREAMING_SNAKE_CASE    			=       crop_size
												SCREAMING_SNAKE_CASE    			=       do_flip_channel_order





							def 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												    "do_flip_channel_order": self.do_flip_channel_order,
												}



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Any								=							MobileViTImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					Optional[int]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MobileViTImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					str      )					->				Tuple:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					str      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_center_crop"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"center_crop"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_flip_channel_order"      )      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Tuple:



												'''simple docstring'''

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

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




							def 	__snake_case(						self   :					int      )					->				Tuple:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					Tuple      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							Image.Image      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,							)




							def 	__snake_case(						self   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

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

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,							)





							def 	__snake_case(						self   :					Optional[int]      )					->				Dict:



												'''simple docstring'''

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

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,							)





from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCamelCase					:   Optional[Any]							=		TypeVar('''T''')


class 						lowercase    (     Generic[T]     ):




							def __init__(						self   :					Any	,							_UpperCamelCase   :					T      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       data
												SCREAMING_SNAKE_CASE    			=       None





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



												'''simple docstring'''

												return F"{self.data}"



class 						lowercase    (     Generic[T]     ):




							def __init__(						self   :					Optional[int]      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None




							def __iter__(						self   :					str      )					->				Iterator[T]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.top
												while node:
																	yield node.data
																	SCREAMING_SNAKE_CASE    			=       node.next




							def __str__(						self   :					int      )					->				str:



												'''simple docstring'''

												return "->".join([str(_UpperCamelCase      ) for item in self]      )




							def __len__(						self   :					Tuple      )					->				int:



												'''simple docstring'''

												return len(tuple(iter(self      )      )      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				bool:



												'''simple docstring'''

												return self.top is None




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					T      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       Node(_UpperCamelCase      )
												if not self.is_empty():
																	SCREAMING_SNAKE_CASE    			=       self.top
												SCREAMING_SNAKE_CASE    			=       node




							def 	__snake_case(						self   :					Union[str, Any]      )					->				T:



												'''simple docstring'''

												if self.is_empty():
																	raise IndexError("pop from empty stack"      )
												assert isinstance(self.top	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.top
												SCREAMING_SNAKE_CASE    			=       self.top.next
												return pop_node.data




							def 	__snake_case(						self   :					Union[str, Any]      )					->				T:



												'''simple docstring'''

												if self.is_empty():
																	raise IndexError("peek from empty stack"      )

												assert self.top is not None
												return self.top.data





							def 	__snake_case(						self   :					Dict      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None


if __name__ == "__main__":
							from doctest import testmod

							testmod()




