




"""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 lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase=13						,   lowercase=3						,   lowercase=224						,   lowercase=30						,   lowercase=400						,   lowercase=True						,   lowercase=None						,   lowercase=True						,   lowercase=[0.5, 0.5, 0.5]						,   lowercase=[0.5, 0.5, 0.5]						,   ):
							_lowerCamelCase  :    Dict					=					size if size is not None else {'''height''': 18, '''width''': 18}
							_lowerCamelCase  :    Any					=					parent
							_lowerCamelCase  :    int					=					batch_size
							_lowerCamelCase  :    Union[str, Any]					=					num_channels
							_lowerCamelCase  :    Any					=					image_size
							_lowerCamelCase  :    int					=					min_resolution
							_lowerCamelCase  :    int					=					max_resolution
							_lowerCamelCase  :    Optional[Any]					=					do_resize
							_lowerCamelCase  :    List[str]					=					size
							_lowerCamelCase  :    int					=					do_normalize
							_lowerCamelCase  :    Dict					=					image_mean
							_lowerCamelCase  :    int					=					image_std






			def       A_					(						self						):
							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 lowerCAmelCase__			(  lowerCamelCase__,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    ViTImageProcessor if is_vision_available() else None
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					EfficientFormerImageProcessorTester(self						)
			@property
			def       A_					(						self						):
							return self.image_proc_tester.prepare_image_processor_dict()
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowerCamelCase						,   'image_mean'						)						)
							self.assertTrue(hasattr(__lowerCamelCase						,   'image_std'						)						)
							self.assertTrue(hasattr(__lowerCamelCase						,   'do_normalize'						)						)
							self.assertTrue(hasattr(__lowerCamelCase						,   'do_resize'						)						)
							self.assertTrue(hasattr(__lowerCamelCase						,   'size'						)						)
			def       A_					(						self						):
							pass
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							_lowerCamelCase  :    List[str]					=					prepare_image_inputs(self.image_proc_tester						,   equal_resolution=__lowerCamelCase						)
							for image in image_inputs:
											self.assertIsInstance(__lowerCamelCase						,   Image.Image						)

							# Test not batched input
							_lowerCamelCase  :    Tuple					=					image_processor(image_inputs[0]						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )						,   )

							# Test batched
							_lowerCamelCase  :    List[Any]					=					image_processor(__lowerCamelCase						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )						,   )
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							_lowerCamelCase  :    Union[str, Any]					=					prepare_image_inputs(self.image_proc_tester						,   equal_resolution=__lowerCamelCase						,   numpify=__lowerCamelCase						)
							for image in image_inputs:
											self.assertIsInstance(__lowerCamelCase						,   np.ndarray						)

							# Test not batched input
							_lowerCamelCase  :    int					=					image_processor(image_inputs[0]						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )						,   )

							# Test batched
							_lowerCamelCase  :    int					=					image_processor(__lowerCamelCase						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )						,   )






			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							_lowerCamelCase  :    List[str]					=					prepare_image_inputs(self.image_proc_tester						,   equal_resolution=__lowerCamelCase						,   torchify=__lowerCamelCase						)
							for image in image_inputs:
											self.assertIsInstance(__lowerCamelCase						,   torch.Tensor						)

							# Test not batched input
							_lowerCamelCase  :    Tuple					=					image_processor(image_inputs[0]						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )						,   )

							# Test batched
							_lowerCamelCase  :    Union[str, Any]					=					image_processor(__lowerCamelCase						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )						,   )





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				return "".join(chr(ord(lowercase__	) - 32	) if 'a' <= char <= 'z' else char for char in word	)


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

			testmod()