


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 MobileNetVaImageProcessor





class 					snake_case__  ( unittest.TestCase       ):





	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_=7				,    UpperCamelCase_=3				,    UpperCamelCase_=18				,    UpperCamelCase_=30				,    UpperCamelCase_=400				,    UpperCamelCase_=True				,    UpperCamelCase_=None				,    UpperCamelCase_=True				,    UpperCamelCase_=None				,    )		->			Dict:
								"""simple docstring"""






								a_       :   List[str]			=	size if size is not None else {"""shortest_edge""": 20}
								a_       :   List[Any]			=	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								a_       :   List[Any]			=	parent
								a_       :   Union[str, Any]			=	batch_size
								a_       :   Optional[int]			=	num_channels
								a_       :   List[str]			=	image_size
								a_       :   Any			=	min_resolution
								a_       :   Optional[Any]			=	max_resolution
								a_       :   str			=	do_resize
								a_       :   Union[str, Any]			=	size
								a_       :   List[str]			=	do_center_crop
								a_       :   Optional[Any]			=	crop_size





	def     A				(  self							)		->			List[Any]:
								"""simple docstring"""






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





@require_torch
@require_vision
class 					snake_case__  ( __A      ,    unittest.TestCase       ):
	UpperCAmelCase   :							Any         = MobileNetVaImageProcessor if is_vision_available() else None





	def     A				(  self							)		->			int:
								"""simple docstring"""






								a_       :   List[Any]			=	MobileNetVaImageProcessingTester(self							)





	@property
	def     A				(  self							)		->			int:
								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()





	def     A				(  self							)		->			Optional[int]:
								"""simple docstring"""






								a_       :   int			=	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"""							)							)





	def     A				(  self							)		->			Tuple:
								"""simple docstring"""






								a_       :   str			=	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}							)

								a_       :   Optional[Any]			=	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     A				(  self							)		->			str:
								"""simple docstring"""






								pass





	def     A				(  self							)		->			List[str]:
								"""simple docstring"""






								a_       :   int			=	self.image_processing_class(**self.image_processor_dict							)
								# create random PIL images
								a_       :   List[Any]			=	prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCamelCase_							)
								for image in image_inputs:
															self.assertIsInstance(UpperCamelCase_				,    Image.Image							)

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

								# Test batched
								a_       :   int			=	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     A				(  self							)		->			Tuple:
								"""simple docstring"""






								a_       :   Optional[int]			=	self.image_processing_class(**self.image_processor_dict							)
								# create random numpy tensors
								a_       :   Dict			=	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
								a_       :   Dict			=	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
								a_       :   Union[str, Any]			=	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     A				(  self							)		->			Union[str, Any]:
								"""simple docstring"""






								a_       :   Dict			=	self.image_processing_class(**self.image_processor_dict							)
								# create random PyTorch tensors
								a_       :   List[Any]			=	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
								a_       :   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.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,    )

								# Test batched
								a_       :   Any			=	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





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list      ):






							"""simple docstring"""



							a_       :   Tuple			=	sorted(SCREAMING_SNAKE_CASE_      )
							a_       :   Tuple			=	len(SCREAMING_SNAKE_CASE_      )
							a_       :   Tuple			=	length >> 1
							return (
							    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
							    if length % 2 == 0
							    else sorted_list[mid_index]
							)





def 						_lowerCamelCase		(							):






							"""simple docstring"""



							import doctest

							doctest.testmod()


if __name__ == "__main__":
	main()


