



"""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 LevitImageProcessor



class     A__     (						unittest.TestCase							):




	'''simple docstring'''





	def __init__(  self:	int ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=7 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=3 ,    _SCREAMING_SNAKE_CASE:	Tuple=18 ,    _SCREAMING_SNAKE_CASE:	Any=30 ,    _SCREAMING_SNAKE_CASE:	str=400 ,    _SCREAMING_SNAKE_CASE:	List[str]=True ,    _SCREAMING_SNAKE_CASE:	List[Any]=None ,    _SCREAMING_SNAKE_CASE:	List[Any]=True ,    _SCREAMING_SNAKE_CASE:	List[str]=None ,    _SCREAMING_SNAKE_CASE:	str=True ,    _SCREAMING_SNAKE_CASE:	Dict=[0.5, 0.5, 0.5] ,    _SCREAMING_SNAKE_CASE:	Dict=[0.5, 0.5, 0.5] ,    )       -> List[str]:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Tuple:

				"""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     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			LevitImageProcessor if is_vision_available() else None

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= LevitImageProcessingTester(self)

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Dict:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= 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 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= 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})

				__lowerCAmelCase   :						Tuple				= 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 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> List[Any]:

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= self.image_processing_class(**self.image_processor_dict)
				# create random PIL images
				__lowerCAmelCase   :						Optional[Any]				= prepare_image_inputs(self.image_processor_tester ,    equal_resolution=__UpperCamelCase)
				for image in image_inputs:
							self.assertIsInstance(__UpperCamelCase ,    Image.Image)

				# Test not batched input
				__lowerCAmelCase   :						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
				__lowerCAmelCase   :						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 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= self.image_processing_class(**self.image_processor_dict)
				# create random numpy tensors
				__lowerCAmelCase   :						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
				__lowerCAmelCase   :						Optional[int]				= 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   :						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 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= self.image_processing_class(**self.image_processor_dict)
				# create random PyTorch tensors
				__lowerCAmelCase   :						Tuple				= 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
				__lowerCAmelCase   :						int				= 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[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"],
				    ) ,    )




"""simple docstring"""






from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	ArgumentParser)       -> str:

				"""simple docstring"""

				raise NotImplementedError()







	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> str:

				"""simple docstring"""

				raise NotImplementedError()