
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 				lowercase		(					unittest.TestCase		):




							def __init__(				self   ,A__   ,A__=7   ,A__=3   ,A__=1_8   ,A__=3_0   ,A__=4_0_0   ,A__=True   ,A__=None   ,A__=True   ,A__=None   ,):
												lowercase							=						size if size is not None else {'''shortest_edge''': 2_0}
												lowercase							=						crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
												lowercase							=						parent
												lowercase							=						batch_size
												lowercase							=						num_channels
												lowercase							=						image_size
												lowercase							=						min_resolution
												lowercase							=						max_resolution
												lowercase							=						do_resize
												lowercase							=						size
												lowercase							=						do_center_crop
												lowercase							=						crop_size





							def  A__      (				self):
												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 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Tuple       					=MobileNetVaImageProcessor if is_vision_available() else None




							def  A__      (				self):
												lowercase							=						MobileNetVaImageProcessingTester(self)




							@property
							def  A__      (				self):
												return self.image_processor_tester.prepare_image_processor_dict()




							def  A__      (				self):
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(A__   ,'''do_resize'''))
												self.assertTrue(hasattr(A__   ,'''size'''))
												self.assertTrue(hasattr(A__   ,'''do_center_crop'''))
												self.assertTrue(hasattr(A__   ,'''crop_size'''))




							def  A__      (				self):
												lowercase							=						self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size   ,{'''shortest_edge''': 2_0})
												self.assertEqual(image_processor.crop_size   ,{'''height''': 1_8, '''width''': 1_8})

												lowercase							=						self.image_processing_class.from_dict(self.image_processor_dict   ,size=4_2   ,crop_size=8_4)
												self.assertEqual(image_processor.size   ,{'''shortest_edge''': 4_2})
												self.assertEqual(image_processor.crop_size   ,{'''height''': 8_4, '''width''': 8_4})




							def  A__      (				self):
												pass




							def  A__      (				self):
												# Initialize image_processing
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												lowercase							=						prepare_image_inputs(self.image_processor_tester   ,equal_resolution=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__   ,Image.Image)

												# Test not batched input
												lowercase							=						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
												lowercase							=						image_processing(A__   ,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):
												# Initialize image_processing
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												lowercase							=						prepare_image_inputs(self.image_processor_tester   ,equal_resolution=A__   ,numpify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__   ,np.ndarray)

												# Test not batched input
												lowercase							=						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
												lowercase							=						image_processing(A__   ,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):
												# Initialize image_processing
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												lowercase							=						prepare_image_inputs(self.image_processor_tester   ,equal_resolution=A__   ,torchify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__   ,torch.Tensor)

												# Test not batched input
												lowercase							=						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
												lowercase							=						image_processing(A__   ,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'''],
												    )   ,)





import warnings
from functools import wraps
from typing import Callable





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				@wraps(lowercase							)
				def _inner_fn(*lowercase							,			**lowercase							):
								warnings.warn(
								    (F"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")							,			lowercase							,			)
								return fn(*lowercase							,			**lowercase							)

				return _inner_fn