


"""simple docstring"""






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      __lowerCamelCase	(       unittest.TestCase					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=7     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=18     ,					__UpperCAmelCase=30     ,					__UpperCAmelCase=400     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					)							->				List[Any]:
          _a     						=					size if size is not None else {'''shortest_edge''': 20}
          _a     						=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          _a     						=					parent
          _a     						=					batch_size
          _a     						=					num_channels
          _a     						=					image_size
          _a     						=					min_resolution
          _a     						=					max_resolution
          _a     						=					do_resize
          _a     						=					size
          _a     						=					do_center_crop
          _a     						=					crop_size





     def 				_UpperCAmelCase   (   self				)							->				Dict:
          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      __lowerCamelCase	(       a__	,       unittest.TestCase					):

     '''simple docstring'''
     A_							:						Tuple     =		MobileNetVaImageProcessor if is_vision_available() else None


     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a     						=					MobileNetVaImageProcessingTester(self				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				Any:
          return self.image_processor_tester.prepare_image_processor_dict()


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					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 				_UpperCAmelCase   (   self				)							->				Tuple:
          _a     						=					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     						=					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 				_UpperCAmelCase   (   self				)							->				int:
          pass


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          # Initialize image_processing
          _a     						=					self.image_processing_class(**self.image_processor_dict				)
          # create random PIL images
          _a     						=					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     						=					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     						=					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 				_UpperCAmelCase   (   self				)							->				int:
          # Initialize image_processing
          _a     						=					self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          _a     						=					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     						=					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     						=					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 				_UpperCAmelCase   (   self				)							->				int:
          # Initialize image_processing
          _a     						=					self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          _a     						=					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     						=					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     						=					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'''],
              )     ,					)



"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str							):






     """simple docstring"""

     return "".join(chr(ord(_lowerCAmelCase							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


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

   testmod()