





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 DeiTImageProcessor

class 			__A(    unittest.TestCase			):

     def __init__(   self,							A,							A=7,							A=3,							A=18,							A=30,							A=400,							A=True,							A=None,							A=True,							A=None,							A=True,							A=[0.5, 0.5, 0.5],							A=[0.5, 0.5, 0.5],							):





            """simple docstring"""

            _UpperCamelCase														=    size if size is not None else {'''height''': 20, '''width''': 20}
            _UpperCamelCase														=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

            _UpperCamelCase														=    parent
            _UpperCamelCase														=    batch_size
            _UpperCamelCase														=    num_channels
            _UpperCamelCase														=    image_size
            _UpperCamelCase														=    min_resolution
            _UpperCamelCase														=    max_resolution
            _UpperCamelCase														=    do_resize
            _UpperCamelCase														=    size
            _UpperCamelCase														=    do_center_crop
            _UpperCamelCase														=    crop_size
            _UpperCamelCase														=    do_normalize
            _UpperCamelCase														=    image_mean
            _UpperCamelCase														=    image_std



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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






@require_torch
@require_vision
class 			__A(    __UpperCAmelCase    ,      unittest.TestCase			):
     __A							=							DeiTImageProcessor if is_vision_available() else None
     __A							=							True

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    DeiTImageProcessingTester(self   )

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self.image_processor_tester.prepare_image_processor_dict()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    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,							'''center_crop'''   )   )
            self.assertTrue(hasattr(A,							'''do_normalize'''   )   )
            self.assertTrue(hasattr(A,							'''image_mean'''   )   )
            self.assertTrue(hasattr(A,							'''image_std'''   )   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

            # Test not batched input
            _UpperCamelCase														=    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
            _UpperCamelCase														=    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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )
            # create random numpy tensors
            _UpperCamelCase														=    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
            _UpperCamelCase														=    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
            _UpperCamelCase														=    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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )
            # create random PyTorch tensors
            _UpperCamelCase														=    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
            _UpperCamelCase														=    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
            _UpperCamelCase														=    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 ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase     :			str     						=	logging.get_logger(__name__)

class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							*A,							**A   ):





            """simple docstring"""

            warnings.warn(
                '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MobileNetV2ImageProcessor instead.''',							A,							)
            super().__init__(*A,							**A   )






