


'''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 ViTImageProcessor




class 			a  (  unittest.TestCase							):
 def __init__(			self   ,					__magic_name__   ,					__magic_name__=7   ,					__magic_name__=3   ,					__magic_name__=18   ,					__magic_name__=30   ,					__magic_name__=4_00   ,					__magic_name__=True   ,					__magic_name__=None   ,					__magic_name__=True   ,					__magic_name__=[0.5, 0.5, 0.5]   ,					__magic_name__=[0.5, 0.5, 0.5]   ,					)       ->				Union[str, Any]:
      _a         =				size if 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_normalize
      _a         =				image_mean
      _a         =				image_std




 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }





@require_torch
@require_vision
class 			a  (  _SCREAMING_SNAKE_CASE	,					unittest.TestCase							):
 _lowerCAmelCase			       =						ViTImageProcessor if is_vision_available() else None
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      _a         =				ViTImageProcessingTester(self    )
 @property
 def 				__UpperCAmelCase		(			self    )       ->				Dict:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 				__UpperCAmelCase		(			self    )       ->				Optional[int]:
      _a         =				self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(__magic_name__   ,					'image_mean'    )    )
      self.assertTrue(hasattr(__magic_name__   ,					'image_std'    )    )
      self.assertTrue(hasattr(__magic_name__   ,					'do_normalize'    )    )
      self.assertTrue(hasattr(__magic_name__   ,					'do_resize'    )    )
      self.assertTrue(hasattr(__magic_name__   ,					'size'    )    )
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      _a         =				self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size   ,					{'height': 18, 'width': 18}    )

      _a         =				self.image_processing_class.from_dict(self.image_processor_dict   ,					size=42    )
      self.assertEqual(image_processor.size   ,					{'height': 42, 'width': 42}    )
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      pass
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      # 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=__magic_name__    )
      for image in image_inputs:
           self.assertIsInstance(__magic_name__   ,					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.size['height'],
              self.image_processor_tester.size['width'],
          )   ,					)

      # Test batched
      _a         =				image_processing(__magic_name__   ,					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.size['height'],
              self.image_processor_tester.size['width'],
          )   ,					)
 def 				__UpperCAmelCase		(			self    )       ->				List[str]:
      # 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=__magic_name__   ,					numpify=__magic_name__    )
      for image in image_inputs:
           self.assertIsInstance(__magic_name__   ,					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.size['height'],
              self.image_processor_tester.size['width'],
          )   ,					)

      # Test batched
      _a         =				image_processing(__magic_name__   ,					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.size['height'],
              self.image_processor_tester.size['width'],
          )   ,					)




 def 				__UpperCAmelCase		(			self    )       ->				Optional[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=__magic_name__   ,					torchify=__magic_name__    )
      for image in image_inputs:
           self.assertIsInstance(__magic_name__   ,					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.size['height'],
              self.image_processor_tester.size['width'],
          )   ,					)

      # Test batched
      _a         =				image_processing(__magic_name__   ,					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.size['height'],
              self.image_processor_tester.size['width'],
          )   ,					)





'''simple docstring'''






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline

