



"""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 MobileViTImageProcessor







class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      def __init__(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Tuple=7   ,      SCREAMING_SNAKE_CASE_    :				Dict=3   ,      SCREAMING_SNAKE_CASE_    :				List[str]=1_8   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3_0   ,      SCREAMING_SNAKE_CASE_    :				Any=4_0_0   ,      SCREAMING_SNAKE_CASE_    :				List[str]=True   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=None   ,      SCREAMING_SNAKE_CASE_    :				str=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=None   ,      SCREAMING_SNAKE_CASE_    :				Tuple=True   ,      ):
        lowerCAmelCase_       :    List[str]								=			size if size is not None else {'shortest_edge': 2_0}
        lowerCAmelCase_       :    Union[str, Any]								=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
        lowerCAmelCase_       :    Tuple								=			parent
        lowerCAmelCase_       :    Tuple								=			batch_size
        lowerCAmelCase_       :    Union[str, Any]								=			num_channels
        lowerCAmelCase_       :    List[str]								=			image_size
        lowerCAmelCase_       :    Optional[Any]								=			min_resolution
        lowerCAmelCase_       :    List[Any]								=			max_resolution
        lowerCAmelCase_       :    List[str]								=			do_resize
        lowerCAmelCase_       :    List[str]								=			size
        lowerCAmelCase_       :    Optional[int]								=			do_center_crop
        lowerCAmelCase_       :    Union[str, Any]								=			crop_size
        lowerCAmelCase_       :    Any								=			do_flip_channel_order





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        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 				UpperCamelCase__       (     lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					MobileViTImageProcessor if is_vision_available() else None



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			MobileViTImageProcessingTester(self	)



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        return self.image_processor_tester.prepare_image_processor_dict()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Union[str, Any]								=			self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'do_resize'	)	)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'size'	)	)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'do_center_crop'	)	)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'center_crop'	)	)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'do_flip_channel_order'	)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Dict								=			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}	)

        lowerCAmelCase_       :    str								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        pass



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        # Initialize image_processing
        lowerCAmelCase_       :    Optional[int]								=			self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowerCAmelCase_       :    Any								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=SCREAMING_SNAKE_CASE_	)
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      Image.Image	)

        # Test not batched input
        lowerCAmelCase_       :    Any								=			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
        lowerCAmelCase_       :    int								=			image_processing(SCREAMING_SNAKE_CASE_   ,      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 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        # Initialize image_processing
        lowerCAmelCase_       :    Any								=			self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        lowerCAmelCase_       :    Optional[Any]								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=SCREAMING_SNAKE_CASE_   ,      numpify=SCREAMING_SNAKE_CASE_	)
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      np.ndarray	)

        # Test not batched input
        lowerCAmelCase_       :    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
        lowerCAmelCase_       :    List[str]								=			image_processing(SCREAMING_SNAKE_CASE_   ,      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 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        # Initialize image_processing
        lowerCAmelCase_       :    int								=			self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        lowerCAmelCase_       :    str								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=SCREAMING_SNAKE_CASE_   ,      torchify=SCREAMING_SNAKE_CASE_	)
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      torch.Tensor	)

        # Test not batched input
        lowerCAmelCase_       :    Any								=			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
        lowerCAmelCase_       :    Dict								=			image_processing(SCREAMING_SNAKE_CASE_   ,      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"""







import baseaa

def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       bytes:

  """simple docstring"""




  return baseaa.aaaencode(string.encode('utf-8'						)						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    bytes						)						->       str:

  """simple docstring"""




  return baseaa.aaadecode(lowerCAmelCase__						).decode('utf-8'						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()





