





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 LevitImageProcessor







class   __lowerCAmelCase			(					unittest.TestCase  ):





			def __init__( self					,			snake_case					,			snake_case=7					,			snake_case=3					,			snake_case=18					,			snake_case=30					,			snake_case=400					,			snake_case=True					,			snake_case=None					,			snake_case=True					,			snake_case=None					,			snake_case=True					,			snake_case=[0.5, 0.5, 0.5]					,			snake_case=[0.5, 0.5, 0.5]					,			)				->  Dict:
						"""simple docstring"""





						a__	:						str      		=  size if size is not None else {"shortest_edge": 18}
						a__	:						List[str]      		=  crop_size if crop_size is not None else {"height": 18, "width": 18}
						a__	:						int      		=  parent
						a__	:						str      		=  batch_size
						a__	:						List[Any]      		=  num_channels
						a__	:						Tuple      		=  image_size
						a__	:						int      		=  min_resolution
						a__	:						Any      		=  max_resolution
						a__	:						Dict      		=  do_resize
						a__	:						Optional[Any]      		=  size
						a__	:						Tuple      		=  do_center_crop
						a__	:						Dict      		=  crop_size
						a__	:						int      		=  do_normalize
						a__	:						int      		=  image_mean
						a__	:						List[str]      		=  image_std





			def 	_snake_case ( self      )				->  Optional[int]:
						"""simple docstring"""





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







@require_torch
@require_vision
class   __lowerCAmelCase			(					_UpperCamelCase		,unittest.TestCase  ):
			_UpperCamelCase :						str     		=				LevitImageProcessor if is_vision_available() else None





			def 	_snake_case ( self      )				->  List[str]:
						"""simple docstring"""





						a__	:						Union[str, Any]      		=  LevitImageProcessingTester(self      )





			@property
			def 	_snake_case ( self      )				->  Tuple:
						"""simple docstring"""





						return self.image_processor_tester.prepare_image_processor_dict()





			def 	_snake_case ( self      )				->  Optional[int]:
						"""simple docstring"""





						a__	:						Optional[Any]      		=  self.image_processing_class(**self.image_processor_dict      )
						self.assertTrue(hasattr(UpperCamelCase__					,			"image_mean"      )      )
						self.assertTrue(hasattr(UpperCamelCase__					,			"image_std"      )      )
						self.assertTrue(hasattr(UpperCamelCase__					,			"do_normalize"      )      )
						self.assertTrue(hasattr(UpperCamelCase__					,			"do_resize"      )      )
						self.assertTrue(hasattr(UpperCamelCase__					,			"do_center_crop"      )      )
						self.assertTrue(hasattr(UpperCamelCase__					,			"size"      )      )





			def 	_snake_case ( self      )				->  List[str]:
						"""simple docstring"""





						a__	:						Tuple      		=  self.image_processing_class.from_dict(self.image_processor_dict      )
						self.assertEqual(image_processor.size					,			{"shortest_edge": 18}      )
						self.assertEqual(image_processor.crop_size					,			{"height": 18, "width": 18}      )

						a__	:						List[Any]      		=  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 	_snake_case ( self      )				->  List[Any]:
						"""simple docstring"""





						pass





			def 	_snake_case ( self      )				->  Dict:
						"""simple docstring"""





						a__	:						List[str]      		=  self.image_processing_class(**self.image_processor_dict      )
						# create random PIL images
						a__	:						str      		=  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__	:						Optional[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
						a__	:						str      		=  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 	_snake_case ( self      )				->  List[str]:
						"""simple docstring"""





						a__	:						Optional[int]      		=  self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						a__	:						Union[str, Any]      		=  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__	:						Tuple      		=  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__	:						Optional[Any]      		=  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 	_snake_case ( self      )				->  List[str]:
						"""simple docstring"""





						a__	:						Union[str, Any]      		=  self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						a__	:						Union[str, Any]      		=  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__	:						List[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
						a__	:						Any      		=  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"],
						    )					,			)








import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  SCREAMING_SNAKE_CASE__       :			int			      =						{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

